Detecting Anomalous Programmable Logic Controller Events Using Machine Learning

Industrial control system failures can be hazardous to human lives and the environment. Programmable logic controllers are major components of industrial control systems that are used across the critical infrastructure. Attack and accident investigations involving programmable logic controllers rely on forensic techniques to establish the root causes and to develop mitigation strategies. However, programmable logic controller forensics is a challenging task, primarily because of the lack of system logging. This chapter proposes a novel methodology that logs the values of relevant memory addresses used by a programmable logic controller program along with their timestamps. Machine learning techniques are applied to the logged data to identify anomalous or abnormal programmable logic controller operations. An application of the methodology to a simulated traffic light control system demonstrates its effectiveness in performing forensic investigations of programmable logic controllers.

Introduction
Industrial control systems, which are widely used in the critical infrastructure, contribute to safety and convenience in every aspect of modern society. These systems have served reliably for decades, but a changing technological environment is exposing them to risks that they were not designed to handle [4]. In particular, their reliance on networking technologies, including remote access and control over the Internet, significantly increase the likelihood of attacks.
A common approach when investigating attacks and anomalies involving an industrial control system is to concentrate on the central server of the digital control system or supervisory control and data acquisition (SCADA) system [4]. These servers typically use commodity operating systems, enabling the use of standard digital forensic tools. However, field devices in an industrial control system such as programmable logic controllers (PLCs) and remote terminal units (RTUs) typically rely on proprietary hardware and embedded operating systems, and, therefore, require specialized digital forensic tools and techniques. Unfortunately, these tools and techniques are very limited in their functionality or simply do not exist.
Programmable logic controllers, which interact with and manage sensors and actuators, are important components of industrial control systems. As a result, they are attractive targets for attackers. A notable example is the Stuxnet malware that targeted Siemens programming logic controllers that operated Iran's uranium hexafluoride centrifuges [3]. The malware reprogrammed programmable logic controller code to cause malfunctions and damage while providing fabricated data to the operators in order to mask the attacks.
Unlike traditional digital forensics, no standard guidelines, procedures and tools are available for performing programmable logic controller forensics. A key challenge is the lack of system logging for forensic investigations. This chapter proposes a forensic methodology that captures the values of relevant memory addresses used by a programmable logic controller program in a log file. Machine learning techniques are applied to the logged data to identify anomalous or abnormal programmable logic controller operations. The methodology is applied to the popular Siemens Simatic S7-1212C programmable logic controller. Experiments with a simulated traffic light control system demonstrate the effectiveness and utility of the methodology in forensic investigations of incidents involving programmable logic controllers.

Programmable Logic Controllers
A programmable logic controller is a special microprocessor-based device that uses programmable memory to store instructions and implement functions such as logic, sequencing, timing, counting and arithmetic in order to monitor and control equipment and processes [2]. Figure 1 shows a schematic diagram of a programmable logic controller.
The programming of controllers is an important task when designing and implementing control applications. Each programmable logic controller has to be loaded with a program that controls the status of outputs based on the status of inputs. A programmable logic controller identifies each input or output according to its memory address. In the case of Siemens programmable logic controllers, the addresses of inputs and outputs are expressed in terms of their byte and bit numbers. For example, I0.1 is an input at bit 1 in byte 0 and Q0.7 is an output at bit 7 in byte 0.
A programmable logic controller exhibits anomalous operations in the following situations: (i) hardware failure; (ii) incompatible firmware version; (iii) control program bugs created by an authorized programmer or attacker; (iv) stop and start attacks [1]; and (v) memory read and write attacks [1].
The first step in detecting these anomalous operations is to capture the values of the inputs and outputs used by the control program in a log file. Machine learning techniques are subsequently applied to the logged data in order to detect anomalous operations.

Forensic Challenges
Digital forensic guidelines, procedures and tools have been developed for traditional information technology infrastructures and environments. A digital forensic process includes identification, collection, analysis and reporting. However, performing digital forensic techniques on programmable logic controllers is subject to several challenges [11]: Lack of Documentation: Low-level documentation of proprietary hardware, firmware and applications is usually not available for programmable logic controllers.

Lack of Domain-Specific Knowledge and Experience:
Digital forensics of programmable logic controllers is hindered by the lack of expertise and experience on the part of investigators. Lack of Logging Mechanisms: Programmable logic controllers typically do not log data for forensic purposes.

Lack of Forensic Tools:
Limited, if any, forensic tools are available for conducting investigations of incidents involving programmable logic controllers.

Availability/Always On:
The availability of programmable logic controllers in an industrial control environment is a priority. It is extremely difficult to shut down a control system and physical process in order to conduct a forensic investigation.

Machine Learning
Machine learning is a data analysis method that automates model building. By leveraging algorithms that iteratively learn from data, machine learning enables computer systems to find hidden insights without being explicitly programmed [12]. Machine learning techniques have been applied in a number of areas, including pattern and image recognition, email spam filtering and network intrusion detection.
Supervised learning is the most common machine learning approach and several algorithms such as decision trees, support vector machines and artificial neural networks have been developed to implement supervised learning. In general, a supervised learning algorithm takes a known set of input data and known responses to the data, and creates a model that effectively predicts the responses to new input data [7]. Figure 2 shows a schematic diagram of the supervised learning approach. The experiments conducted in this research leveraged decision tree (DT) and support vector machine (SVM) learning algorithms to analyze log file data in order to detect anomalous programmable logic controller operations.

Related Work
The Stuxnet attack [3] significantly increased research efforts related to industrial control system security, including intrusion detection and anomaly detection. However, very little research has specifically focused on applying machine learning techniques to detect intrusions and anomalous operations. An example is the work of Morris et al. [9], which trained a classifier on log data captured from a laboratory-scale gas pipeline and used it to detect 35 cyber attacks.
Another example is the research of Mantere et al. [6], which leveraged network traffic features to detect anomalies in specific industrial control systems. Mantere and colleagues used machine learning to decrease the amount of manual customization required to deploy network security monitors and intrusion detection systems in industrial control systems.
The research presented in this chapter differs from related work in that it concentrates on monitoring and capturing data directly from programmable logic controllers to support forensic investigations of intrusions and anomalous operations.

Experimental Setup and Methodology
This section describes the experimental setup and the methodology for identifying anomalous programmable logic controller operations.

Experimental Setup
The experiments used a Siemens S7-1212C programmable logic controller loaded with the TLIGHT traffic light control program. TLIGHT is a sample program provided with the Siemens SIMATIC S7-300 Programmable Controller Quick Start User Guide [16]. As shown in Figure 3, the TLIGHT program controls vehicles and pedestrian traffic at an intersection.
In order to simulate the hardware configuration of a traffic light control system, the programmable logic controller inputs I0.0 and I0.1 were connected to switches and the outputs Q0.0, Q0.1, Q0.5, Q0.6 and Q0.7 were connected to traffic lights. Figure 4 shows the input/output connections of the Siemens S7 1212C programmable logic controller. The Ethernet port of the programmable logic controller was used to establish a network connection for communicating with a peripheral device such as a laptop for programming the system.  A program using the libnodave open source library [5] was used to log the values of relevant memory addresses used by the TLIGHT program. In particular, the program monitored the programmable logic controller memory addresses over the network and recorded the values along with their timestamps. To simplify the supervised learning process, all the non-binary values of memory addresses (e.g., timers) were converted to binary values.

Classifying Anomalous Operations
A machine learning technique typically splits the available data into two parts: (i) training set for learning the properties of the data; and (ii) testing set for evaluating the learned properties of the data. The accuracy of response prediction was evaluated using the testing set [14]. Green for pedestrians T 4 Red for vehicles T 5 Yellow for vehicles T 6 Green for vehicles In order to implement supervised leaning, it was first necessary to understand the TLIGHT program logic. The TLIGHT program comprises instructions that involve inputs, outputs, memory bits and timers. Table 1 provides details about the TLIGHT instructions. Figure 5 shows the input and output signal states during the TLIGHT sample program sequence [16].
The supervised learning approach involved the following steps: Step 1: Training Set Creation: A training example corresponds to a pair of input objects (values of relevant addresses) and known responses (normal/anomalous operations of the traffic lights). Normal and anomalous operations of TLIGHT were determined according to the values at the relevant addresses (timers and outputs). The seven normal operations of TLIGHT presented in Figure 5 are based on the values of the timers and outputs at various time intervals. Table 2 shows the input objects and known responses for the seven normal operations of TLIGHT. These were transformed to the input data matrix format for use in supervised learning.
The training set was generated by running the traffic light control system and logging system to capture the values of relevant addresses (inputs, outputs and timers) used by TLIGHT. Anoma- Ethernet communications suite for interfacing with Siemens S7 programmable logic controllers [10]. Thus, the generated log file contained normal and anomalous traffic light operations.
Step 2: Supervised Learning Algorithm Selection: Decision tree (DT) and support vector machine (SVM) supervised learning algorithms were employed in the experiments. A decision tree algorithm was selected to classify anomalous operations of TLIGHT for several reasons [8]. First, the target function has discrete output  values (TLIGHT operations). Additionally, a decision tree algorithm is fairly robust at handling training data errors, including mislabeled attribute values. Indeed, somewhat noisy data (e.g., due to errors in assigning response values) do not pose much of a problem for a decision tree algorithm. A decision tree algorithm can also handle data with missing attribute values (e.g., missing values at memory addresses during data capture).
In addition to a decision tree algorithm, a support vector machine supervised learning algorithm was used. This was done to determine if any obvious differences in accuracy and performance occur when a different machine learning algorithm is used. Furthermore, the support vector machine algorithm performs well even with small training datasets Step 3: Supervised Learning Algorithm Application: The captured data was assigned response values corresponding to normal or anomalous operations and was subsequently transformed to a matrix format for input to the supervised learning algorithms ( Table 2). Decision tree and support vector machine classifiers provided by scikit-learn [15] were used for model training. Table 3 lists the settings of the classifiers used in the experiments. The classifiers were implemented using default values of the input parameters. No k-fold cross validation was applied to the training samples. Finally, the accuracy of response prediction was evaluated based on the testing data.

Experimental Results and Discussion
In order to evaluate the accuracy of the learned models, two datasets were prepared for the decision tree and support vector machine learning algorithms. The first set (Dataset 1) contained 2,800 records, 560 for training and 2,240 for testing. The second set (Dataset 2) contained 8,000 records, 1,600 for training and 6,400 for testing. Table 4 shows the classification accuracy. The accuracy rates with Dataset 1 for the decision tree and support vector machine learning algorithms were 99.91% while the accuracy rates with Dataset 2 for the decision tree and support vector machine learning algorithms were 99.94% and 99.60%, respectively.
In the experiments, transaction records in the log file corresponding to anomalous operations were identified by machine learning. Because the values at the relevant addresses used by TLIGHT were recorded along with timestamps in the log file, it was possible to trace which values had been altered and when they were altered, and subsequently identify the anomalous TLIGHT operations. However, a log file alone may be insufficient in a forensic investigation because it does not contain information about what (e.g., IP address) induced an anomalous operation and how it was induced. For this reason, a forensic investigator should use a network packet analyzer such as Wireshark to capture Corresponds to Table 2 Training Data Abnormal Operations packets while employing the logging system to record the relevant addresses used by TLIGHT. Wireshark supports the PROFINET industrial data communications standard in order to record and analyze Ethernet message frames. It can be used to dissect the ISO-on-TCP packets in Siemens S7 programmable logic controller communications after adding the Wireshark S7 dissector plugin. Note that S7 is a function-oriented or command-oriented protocol in that each transmission contains a command or a reply.
After collecting and analyzing the log file and network packet data, a forensic investigator can discover where the compromise originated, how it was carried out and, possibly, who was responsible for the incident.  In the experiment, the anomalous operations that started on 09 June 2016 at 09:55.49.16 AM were the result of a programmable logic controller self-test and the anomalous operations that started on 29 August 2016 at 09:27:38.96 PM were due to a simulated attack (Snap7) [10]. These examples demonstrate that machine learning can help a forensic investigator filter unnecessary log data and narrow the scope of the forensic investigation.
The methodology presented in this chapter can be extended to other brands of programmable logic controllers and other control programs. However, it is not possible to create a single logging system for all programmable logic controller applications because different applications require different control programs. Therefore, each programmable logic controller application should have its own logging system. In order to create a logging system, it is necessary to understand the design of the control program and identify the programmable logic controller memory addresses that must be monitored and analyzed.
In order to simplify machine learning, the experiments did not consider the time sequences of normal programmable logic controller operations. Therefore, the accuracy of the results may vary. Note also that supervised learning is by no means the only way to identify anomalous programmable logic controller operations. In fact, the work described in this chapter serves as an initial approach to determine whether or not supervised learning is feasible for programmable logic controller forensics. Indeed, the experimental results demonstrate that supervised learning can help predict anomalous operations with uncertain inputs and responses, even in the case of complicated user control programs.

Conclusions
A log containing the values at the relevant memory addresses used by a programmable logic controller program along with their timestamps can be very valuable in a forensic investigation of an industrial control system incident. In particular, machine learning techniques can applied to the logged data to identify anomalous programmable logic controller operations. The application of the methodology to a simulated traffic light control system demonstrates its effectiveness in a forensic investigation involving a programmable logic controller. Since different programmable logic controller applications require different control programs, each application should have its own logging system. In order to create the logging system, it is necessary to understand the design of the control program and identify the programmable logic controller memory addresses that must be monitored and analyzed. However, a log file alone may be insufficient in a forensic investigation because it may not contain information about what induced an anomalous operation and how it was induced. Therefore, it is recommended to augment the log file with data from a network packet analyzer such as Wireshark.
This research is an initial step in developing forensic capabilities for programmable logic controllers. Future research will attempt to apply and refine machine learning techniques to various industrial control system applications to support forensic investigations of intrusions and anomalous behavior in these vital systems that permeate the critical infrastructure.


Delivering Active Fashion Advertisement to Potential Viewers

Nowadays, more and more fashion stores have started to introduce digital signage displays for dynamically switching advertisement contents of their products. To attract people’s attention and interest towards a display, some signage systems distribute suitable contents for each individual based on personal attributes, acquired through a camera. However, if there are people who are not interested in the digital signage around the display, suitable contents are not necessarily distributed to those who really intend to view it. Therefore, in this paper we propose a digital signage that can take into account people who have higher potential of viewing the display. First, we acquire human behavior and personal attributes of each person. Then, the potential degree of viewing the display is calculated for each person. Next, our system searches content candidates for each person based on their personal attributes. Finally, the distributed contents are determined by selecting more candidates for people with a higher viewing potential.

Introduction
Generally, in fashion stores, advertisements come in the form of actual products or their posters. Recently, more and more stores have begun introducing digital signage displays as the advertisement contents can be easily updated. At the end of 2016, over 37 million digital screens were in use all over the world [1].
To change the advertisement contents of a digital signage, one of the simplest ways is to loop them on repeat. To make people more attracted, some signages can change contents by the time of day and using weather information [8], or based on social media [3], or a specific human action as the trigger such as approaching the display [2]. Moreover, to provide suitable contents for a certain person, that is, "for content personalization", [5] can change contents based on gender and age as personal attributes, which are acquired using a camera mounted on the display. However, if there are people who are not interested in the digital signage around the display, suitable contents are not necessarily distributed to those who really intend to view it.
To solve the aforementioned issue, in this paper we propose a digital signage system that can take people who have higher potential of viewing the display  Fig. 1. In our system we first use an RBG-D sensor to acquire human behavior, including walking speed and face orientation, together with personal attributes such as gender, and the representative colors of the upper and the lower body. Then, based on their behavior, the potential degree of viewing the display is calculated for each person. Next, our system searches content candidates for each person based on their personal attributes. Finally, the distributed contents are determined by selecting more candidates for people with a higher viewing potential.

Proposed system
Our proposed system assumes that people are passing in front of the system as shown in Fig. 1. To acquire human behavior and personal attributes, a sensor is mounted on the top of the system; we chose to adopt an RGB-D sensor as it can easily detect people and obtain point cloud data of each person in the metric scale. The angle of the sensor is set downwards so that it can observe people from a bird's-eye view. The following subsections describe the details of our proposed system, which observes time-series point cloud data from the RGB-D sensor as source data.

Acquiring human behavior and personal attributes
As preparation for the acquisition, we extract time-series point cloud data of each person from the source data. First, the coordinate system is converted from the camera coordinates to world coordinates, where the xy and the xz plane are aligned to the display and the floor plane respectively and the origin O w is located at the bottom center of our system as shown in Fig. 1. Then, the 3D region of each person is detected at each frame by Munaro et al.'s method [4] focusing on the shape of a human head. Finally, person tracking is performed by matching detected persons between the current and the previous frame. In our system, a greedy algorithm is used to find matching correspondences that minimize the Euclidean distance between the center position of a person at the current frame and the estimated position from a person's walking trajectory.
From the time-series point cloud data of each person, we obtain their behavioral and personal attributes. In terms of behavioral attributes, we acquire the position x i , the vector of walking speed v i and horizontal face orientation θ i of the i-th person. In this paper, θ i is set as 1 if Viola and Jones' detector [7] detects a frontal face from the color image of his or her head region; otherwise 0. As personal attributes, we acquire the representative colors of the upper and lower body, and the gender of each person. Additionally, a compatible color with the two representative body colors is chosen based on the accent color and separation color of fashion color theory [6].

Calculating Potential Degree of Viewing the Display
The potential degree of viewing the display of the i-th person p i is calculated from his or her personal behavior. In the calculation, we consider "from where, how frequently, and how they are walking whilst viewing" as the state of viewing behavior.
where θ i is the vector of face orientation in the past N frames; f is a negative piecewise linear activation function of the distance between the person and the display plane, which returns higher values for people closer to the display; g is a monotone increasing function of the frequency of detected frontal faces in the N frames, which returns higher values for people viewing the display more frequently; and h is a step function of the walking speed that returns 0 for a person walking more quickly than a threshold speed, otherwise 1.

Distributing Suitable Advertisement Contents
In preparation for the content distribution, a database is constructed beforehand so that suitable advertisement contents can be searched by personal attributes. Our database consists of the image, gender information (i.e. men's, ladies' and unisex), the representative color and the type of clothes (e.g. tops and bottoms) of each fashion item.
To distribute suitable advertisement contents, first our system searches content candidates for each person from the database by their gender and compatible color. Then, the distributed contents are selected from the content candidates of each person so that the ratio of the number selected contents for each person is the same as the normalized potential degree p i = p i / i p i . The above procedure is repeated to update advertisement contents at a certain interval.

Conclusion
In this paper, we proposed a digital signage system that can take people who have higher potential of viewing the display into account. First, we acquired the human behavior and the personal attributes from time-series point cloud data observed from a RGB-D sensor. Next, the potential degree was calculated from the human behavior with consideration of their state of viewing behavior. Then, content candidates that are suitable for each person are searched from the database by using the personal attributes. Finally, distributed contents are selected from the candidates so that the ratio of selected content candidates for each person matches their normalized potential viewing degree. As shown in Fig. 3, we have finished developing the proposed system, which will be demonstrated in the interactive session of ICEC2017. As future work, for better personalization of advertisement contents, we will try to acquire new personal attributes including age or the fashion style of pedestrians from the RGB-D sensor.


extfieldsofstudy:
Engineering
Computer Science

text:
SensX: About Sensing and Assessment of Complex Human Motion

The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without professional supervision which may lead to suboptimal training success or even serious injuries. We believe, that automated supervision and realtime feedback during a workout may help to solve these issues. Therefore we introduce four fundamental steps for complex human motion assessment and present SensX, a sensor-based architecture for monitoring, recording, and analyzing complex and multi-dimensional motion chains. We provide the results of our preliminary study encompassing 8 different body weight exercises, 20 participants, and more than 9,220 recorded exercise repetitions. Furthermore, insights into SensXs classification capabilities and the impact of specific sensor configurations onto the analysis process are given.

I. INTRODUCTION
Today it is widely admitted that exercising regularly betters physical health and psychological well-being. Moreover, the risk for obesity and sufferings such as chronic diseases, hypertension, diabetes, or the Alzheimers disease is reduced and physically active people experience an improved quality of live as well as an increased emotional and cognitive feeling of well-being [1]. Body weight exercises are performed by only using an athletes own body weight without artificial support [2]. Originally, they were carried out within school sports or special forces training. Now they are often seen in combination with endurance and free weight training, e.g., within Crossfit or obstacle races.
The popularity of exercising among athletes and the ubiquitous presence of mobile devices are reasons for the huge number of existing workout apps (Freeletics, etc.). These apps feature millions of downloads and offer customized workout planning as well as detailed instructions for challenging exercises. But the distribution of such information without professional supervision emerges new problems like suboptimal training success or even serious injuries (e.g., at the lower back or the human spine). Reasons are constantly wrong conduction of specific exercises, the absence of a warming up, or false positioning of individual extremities [3]. Moreover, the aforementioned applications provide only a sparse possibility of exercise monitoring and no possibilities for qualitative evaluation concerning the execution of specific exercises. E.g., when detecting defective positions which are potentially dangerous, the user needs to be warned and instructed about which detail of the exercise was performed wrong. Thereby, injuries due to unsupervised training sessions could be reduced drastically and inefficient or suboptimal workouts could be avoided. Available sensor systems on the consumer market are already enabling simple activity recognition and tracking, such as step counting with wearables or tracking of running via GPS. But none these systems is capable of providing support during the conduction of complex activities and multidimensional human motion chains.
We address this subject by presenting SensX, a sensorbased architecture for monitoring, recording, and analyzing complex motion chains. Therefore, we give an insight into related work within Section II. In Section III, we present a generic paradigm for analyzing and assessing recurrent human motion consisting of four fundamental steps. These are used as a basis for the development of the SensX architecture. Afterwards, we explain the systems actual state of implementation in Section IV, followed by the presentation of our preliminary study featuring 20 athletes and the systems capabilities of recognizing specific exercises with different sensor configurations in Section V. Section VI deals with ongoing work and still unsolved issues.

II. ANALYSIS OF HUMAN AND ARTIFICIAL MOTION
Dernbach et al. present a smartphone driven approach for recognizing human activities, arranged in two groups [4]. So called simple activities (e.g., walking) are classified correctly with a rate of over 90%. So called complex activities (e.g., watering flowers) were classified correctly with a rate of 35%-50%. Strohrmann et al. examine running data of 21 participants tracked with ETHOS units [5]. In order to assess performance levels and to assist training as well as to detect fatigue, they extracted foot contact duration, foot strike type and other kinematic parameters.
Chang et al. tracked 9 different weight lifting exercises by using two accelerometers, one in a glove and one worn as a belt clip [6]. All output data was analyzed by using the Naive Bayes Classification (NBC) and Hidden Markov Models (HMM), both with a success of more than 90%. Still, complex movement chains which encompass individual movements of specific extremities were not examined. Ding et al. present another platform for free-weight exercise monitoring which is called FEMO. It examines strength, intensity, smoothness and continuity of RFID signals [7]. They analyzed 10 different weight-lifting exercises with 15 participants and achieved a successful detection rate of about 85% with a subsequent correct classification rate of 90.4%. Another system for finding, recognizing, and counting repetitive exercises is Recofit, provided by Morris et al. [8]. It gathers acceleration as well as rotation data of activities with different complexity. Subsequently, these are recognized and counted by conducting a multiclass Support Vector Machine (SVM). For evaluation 13 exercises of varying complexity and organized in 4 different classes were examined. The authors were able to recognize 95% of the workout periods and out of these they recognized exercises with a success rate of 96% (within a group of 7 exercises). Still, Recofit is not able to track individual extremities or to provide information concerning their position.
Ladha et al. introduce ClimbAX, a sensor platform for skill assessment of rock climbing which is evaluating parameters such as power, control, stability, and speed [9]. Therefore, they use a tri-axial accelerometer worn on the wrist. After recording, the data is down-sampled to 30Hz and split into segments capturing one move between two rest periods, respectively. The systems performance results for 53 climbers were compared with official results of a climbing competition correlations could be noticed. ClimBSN is a system for translating accelerometer data into climbing specific measures like fluidity, strength, and endurance [10]. The authors use a miniaturized ear-worn 3D accelerometer as a sensor and visualize their measured and translated parameters within climbing style graphs. In order to isolate the most discriminative features, they use Principal Component Analysis (PCA).
One of the few works which tries to achieve a qualitative analysis of weight-lifting exercises is presented by Velloso et al. [11]. Their system consists of three sensor devices (glove, belt, and wrist-band) which encompass an accelerometer, a gyroscope and a magnetometer. Subsequently, they recorded 5 wrongly executed exercises and tried to classify them afterwards. They succeeded with a rate of 78%. But due to the need of recording mistakes individually, the system is not very scalable and the achieved sampling rate is comparably low. GymSkill is a system for analysis and qualitative evaluation of exercises conducted on a balance board [12]. Therefore, the authors tracked accelerometer and magnetometer data and tried to rate quality on basis of features like movement smoothness and continuity. For evaluation they conducted a study with 6 participants and 1200 exercises; the results ranged from average to good based on the specific exercise setup.
Other works deal with motion analysis on basis of templating approaches. Ebert et al. introduced a system for segmented collision detection for vehicles by using templates and Dy- namic Time Warping (DTW) [13]. Incoming inertial signals are processed in realtime and the direction as well as the impact segment of hits onto a vehicles surface are recognized with a success rate of more than 94%.
In summary, there are different existing approaches for analyzing human or artificial motion on basis of inertial sensor data. Some also aim to provide a feature based, qualitative assessment, e.g., in terms of smoothness or continuity of motions. Still, a real assessment of complex motions in terms of exercise safety, the detection of defective positions of extremities, prevention of injuries, as well as the optimization of training results due to automated feedback to the athletes, is missing. Reasons for that may be the absence of additional information concerning the position of ankles or extremities due to a lack of adequate sensors for specific extremity tracking and capable of live sensing. Moreover, none of the approaches mentioned above encompasses an active computing unit within the system, which makes the provision of realtime feedback unreachable.

III. ARCHITECTURAL CONCEPT
Within this section we present the architecture of SensX, which can be split into a logical layer consisting of four fundamental steps for human motion assessment and the requirements to the systems physical layer. Figure 1 depicts the overall architecture and arranges the theoretical components in context with the systems physical properties. The physical layer encompasses all hardware related issues of SenseX (see Section III-B). It tracks and distributes acceleration and rotation data which is later on used for analysis by the logical layer. After a successful data processing, the logical unit's results may be thrown back to the physical layer for providing feedsback to the user.

A. Four fundamental steps for assessment of recurrent human motion
Derived from our requirements for individual human motion analysis as well as from information out of the related work mentioned above, we identified four fundamental steps (see  Step 1 covers the detection of a purposeful activity out of a continuous stream of signals as well as its segmentation and preprocessing. Skipping of noise and unsubstantial sequences is simplifying later analysis.

2)
Step 2 deals with the automated recognition of a detected activity by examining its similarity or diversity in comparison to already known classes of activities.

3)
Step 3 encompasses the assessment process which is used to make a statement concerning qualitative predictions towards a recognized motion figure,e.g., by checking for deviations from idealized or known motion patterns or feature sets.

4)
Step 4 targets the characterization and identification of reasons, e.g., specific anomalies, malpositions, or movements, which are leading to a potentially good or bad qualitative assessment. Steps 1 and step 2 as well as a possible implementation of both seems to be much more generalizable concerning their applicability for detecting and recognizing a variety of different activities. We conjecture, that the higher a step's layer is arranged within the proposed architecture, the more customization is needed to adapt it to a specific task (e.g., Step 4 characterizing a specific anomaly within an individual set of body weight exercises). Figure 2 shows the acceleration of an athletes chest along the x-axis within 2 sets of 20 pushups each. It illustrates the inevitable need for customization on higher levels: a corresponding acceleration for another exercise will not contain the same qualitative information and therefore needs to be treated individually. Moreover, it demonstrates the athlete's exhaustion: the repetitions in set 1 are comparably equal a decreasing acceleration, a longer periodic time, and striking up and down movements indicating a shivering of the athlete, are symptomatic for set 2. The following sections focus on SensX's implementation concerning step 1 and step 2, which are highlighted in Figure 1.
Step 3 and step 4 are subject of ongoing research and will be discussed in detail within future work.

B. Requirements to the physical layer
In order to provide sufficient information for the conversion of the single steps named above we identified specific requirements which have to be met by SensX: 1) In order to identify the individual positioning of extremities and the rest of the body as well as to detect possibly defective positions, individual tracking of extremities is needed. This indicates the need for a minimum of 4 sensors for the extremities and a minimum of one for the torso to enable a selective and differentiated movement analysis. 2) As stated in II, a minimum data rate as well as accelerometer and gyroscope data is needed for analysis. 3) Only conventional hardware is about to be used to design a system which may be affordable to ordinary athletes, disregarding of social background and financial options. 4) An integrated central processing unit (CPU) including computation capabilities is needed to enable realtime analysis during workouts. 5) SensX must be physically robust against hits and weather conditions as well as comfortable to wear for the athletes, in order to avoid disturbances during a workout.

IV. IMPLEMENTATION
Within these section we describe the system's current state of implementation as well as the used technologies and underlying hardware.

A. Distributed sensor system
As stated before, SensX needs to be able to track an athlete's extremities individually; moreover, a CPU with processing capabilities needs to be included within the system to enable immediate feedback during a workout.
1) Central processing unit: Central processing unit: As a CPU we experimented with two different Android smartphones: A HTC One (M7) with Android 5.0 Lollipop and a Google Nexus 5x by LG running Android 6.0.1 Marshmallow. Our motion-tracking app enables the labeling of 8 different exercises and controls the compliance of mandatory breaks in between individual workout sets automatically. The tracked workout sets are currently stored row-based within individual text files, organized by the specific sensor used for tracking and by providing a timestamp for each data fix. The smartphones are also used as a motion sensor (accelerometer and gyroscope) and provide data-rates of roughly 150Hz (HTC) / 100Hz (LG). Due to different Bluetooth Low Energy (BLE) implementations (deviating from official specifications) the achieved sampling rates when connecting 4 external sensors were significantly different. In context of the LG we achieved a stable sampling rate for each sensor board of 50Hz (only 3axial accelerometer, no gyroscope), the HTC performed with a 40Hz sampling rate for both, four 3-axial accelerometers and four 3-axial gyroscopes.
2) External devices: Small, multi-functional, highly-usable, and robust sensors at affordable prices and suitable for analyzing human motion under harsh conditions (e.g., humidity, rough motions, etc.) are only available since a short time. SensX uses 4 MBIENTLAB Meta Wear CPRO sensors, containing an accelerometer, a gyroscope, a magnetometer, a barometer and an ambient light sensor. In context of this work, we recorded 30 different signal vectors: x-, y-, and z-axis of acceleration and rotation for four external sensors plus the smartphone's sensor set. The theoretical sampling rate for the Meta Wear boards is more than 100Hz per sensor, but due to BLE related restrictions, the achieved data rate is lower (see Section IV-A1).
3) Data transfer: Data may be stored on a small 256kB storage or transferred directly to the processing unit via BLE. Due to the required realtime feedback and the necessity for processing bigger amounts of data while tracking an exercise we use the BLE connection for connecting all four sensor boards. Hardware related differences for the achieved sampling rates are described in Section IV-A1.

4) Sensor attachment:
The processing unit is mounted with a GoPro harness on the athletes' chest, the display pointing forward. The external sensor devices are packed within a plastic case fastened with rubber bands on each individual extremity (see Figure 3). Each external sensor has a mark concerning its orientation in order to ensure a correct attachment at all times.

B. Logical Layer
In order to detect and extract single exercises into frames, an advanced version of the peak-detection algorithm presented in [13] is used. In contrast to the homogeneous profile of directional impacts on a vehicle's surface, signals of body weight exercises may vary significantly in their length, continuity, wave shape, and complexity. To respect that fact, we extended the proposed algorithm and use the signal with the greatest steady dynamics and identify the first occurrence of a periodic exercise as well as its initial frame-length from its autocorrelation. Subsequently, every occurrence is checked for its zero-crossings and the frame size is enlarged if necessary. For smoothing, a Butterworth lowpass is used again. A new problem occurring by using this approach is the techincally conditioned capability for realtime processing: in order to process the auto-correlated signal, a sequence of exercises is needed. This issue could be solved in future by using initial frame lengths which are determined statistically and become adjusted over time. To realize the recognition of different exercises we experimented with the Naive Bayes Classification (NBC) as well as with the J48 algorithm, an Open-Source implementation of the C4.5 decision tree algorithm which is capable of multidecisioning (in contrast to e.g., binary CART capabilities), both in combination with the WEKA machine learning framework [14]. Again, the system still lacks of realtime capabilities and the analysis is currently conducted offline by using a separate machine. For the following evaluation in Section V we only used NBC due to a significantly faster runtime as well as constantly better results for our use-case.

V. EVALUATION
Subsequently, we present the results of a preliminary study concerning the system's capabilities of recognizing different exercises as well as the performance of different sensor configurations. As a CPU we only used the Nexus 5x in combination with four external sensors.

A. Study design
Within the study's context we tracked the execution of 8 different body weight exercises (Crunch (CR), Lunge (LU), Jumping Jack (JJ), Bicycle Crunch (BC), Squat (SQ), Mountain Climber (MC), Russian Twist (RT), and Pushup (PU), see Figure 4) carried out by 20 amateur athletes with an average age of 24 years, whereby 20% were of female and 80% of male sex. The average Body Mass Index was 22, 32kg/m 2 and the average height was 178, 2m. To ensure the completion of the whole workout by as much athletes as possible and to avoid muscular congestion, the exercises' order was designed to stress different groups of muscles, successively. Each exercise was instructed with a video, then the athlete had to conduct 3 sets with 20 repetitions; between the individual sets there was an obligatory break of 30s. All in all we recorded more than 9,220 repetitions (approximately 1,152 repetitions per exercise), not all athletes managed to complete the whole workout. Besides tracking the athlete's acceleration data all workouts were also captured on video to enable additional analysis later on. Furthermore, we surveyed the personal information about each athlete and the subjectively felt individual performance. Anomalies occurring during the workout were also noted.

B. Analysis and data splitting
The configuration tests described within Section V-C were conducted with a training vs. test data ratio of 80:20 as proposed by Ng [15]. For the evaluation in Section V-D we used the same ratio for the training/test datasets, for k-fold cross validation (k = 4) we used the training dataset.
wrist left wrist right foot left foot right To examine different sensor configurations, we created 9 different sensor groups which are depicted in Table V-C. These are encompassing different regions of the human body, such as top (both arms), bottom (both legs), top right (right arm), or right (right arm + right leg). Subsequently we determined the successful recognition rate for each individual sensor configuration. Figure 5 presents the classification results for using sensor data of the specific sensor groups and including (1) as well as excluding (2) additional acceleration and rotation information provided by the CPU. The performance for tracking only a single leg (BR, BL) is the worst with success rates lower than 70%. In contrast to that, the tracking of only one arm (TL, TR), both arms (T), or both legs (B) provides better results, but still retrieves a successful recognition of activities in less than 80%. The tracking of the whole left (L) or the whole right side (R) of an athlete performs significantly better with success rates in between 80% and more than 90%, respectively. When only the CPU without any external sensor data is used, a rate of 75.5% can be achieved. (2) shows the results for all configurations in combination with the CPU. All of them reach a correct classification rate of more than 80%, the groups TR, TL, T, L, and R perform with a success rate of even more than 90%. The results show, that information provided by the bottom extremities contains less valuable information for exercise classification than from the top. Moreover, the acceleration data of only two external sensors in combination with a chest sensor is enough to reach recognition rates of more than 90%, although there is no additional information about the position of all individual extremities.
The overall correct classification rate when using all sensors together was about 95.2%. The proposed full sensor configuration setup with four external sensors performs perceptible better compared to reduced configurations.

D. General exercise recognition
After presenting performance differences in between different sensor groups within Section V-C, we now examine the differences within results for specific exercises. Therefore, we conducted a new NBC featuring two test cycles: one cycle conducts a k-fold cross validation a second cycle uses training and test data (see Section V-B). The results for both cycles show a high amount of similarity, which is also indicated by the confusion matrices depicted in Figure 6. It is significant that for all exercises except pushups within both cycles the successful recognition rate is far above 90% pushups are only recognized with a rate of 75.9% and 82.0%. All in all, pushups were mostly mistaken with the mountain climber exercise. We believe, that there are 3 reasons for that: 1) the starting position as well as the general position for both exercises is very similar (see Figure 4), 2) both exercises were extremely exhausting compared to others so that motions were not always conducted as distinct as for other exercises, and 3) because of the pushups scheduling at the end of the workout all athletes were exhausted when coming to that challenging exercise. As a consequence of that, only a few managed to conduct 20 clean pushups for all 3 sets. This resulted in fewer and more noisy pushup datasets. Furthermore, the presence of rotation information could have given essential information for recognizing pushups more distinct from the mountain climber. The average results are more than 94% when using training data as well as when using cross validation. If we only validate data of 7 exercises of even data quality and quantity (without pushups) the successful classification rate is 96.2% for using data and 97.3% cross validation.

E. Miscellaneous characteristics and findings
SensX meets all requirements stated within Section III-B. Due to its small and flexible external sensors it is capable of tracking the movements of all individual extremities with a joint sampling rate of more than 400Hz, split in 30 different motion signals. Concerning the system's attachment, all athletes stated that they felt comfortable and not hindered by the sensors during working out.

VI. CONCLUSION AND FUTURE WORK
In context of this paper we presented a paradigm for analyzing and assessing recurrent, complex human motion consisting of four fundamental steps. Subsequently, we introduced SensX, an architecture for tracking, analyzing and assessing human motion as well as its current state of implementation and the results of our preliminary study for activity recognition. The system consists of one central processing unit and four external sensors that track acceleration as well as rotation data. Moreover, it is able to track all four human extremities individually. Furthermore, we presented a study with 20 athletes conducting 8 different body weight exercises and more than 9,220 individual repetitions. The successful exercise classification within our evaluation proofs that SensX already provides convincing results when only using acceleration information. Besides that, we also examined the performance of different sensor configurations and learned that tracking only one side of the human body plus the CPU data is enough information for getting results which are comparable to using the complete sensor set (but only for recognition). While step 1 and 2 of the proposed logical layer are implemented by SensX, step 3 and 4 which deal with qualitative exercise assessment are still unsolved and part of our ongoing work. In that context, we conducted a new study with nearly 30 participants using the HTC configuration (see IV-A2) and tracked acceleration as well as rotation data. Again, all workouts were video taped. As shown in [13], rotation data contains crucial information about complex movements and we believe that this additional information together with the one of individual limb movement will enable us to create valid quality assessment processes for human motion.
Another unsolved problem is the porting of our analysis approach towards a live capable system, which may provide feedback to the user in realtime. By using small, flexible and comfortable-to-wear external sensors, the system seems to be fit for realtime in hardware terms, but the analysis runtime, the identification of adequate and economical algorithms for quality assessment as well as the fact that exercises must be processed sequentially from a continuous data stream rises up new challenges. Here, the usage of distance measurements or more simplified alternatives to machine learning approaches may be the solution. Based on the learnings of this work, we believe that efficient recognition and assessment of body weight exercises as well as the provision of specific feedback to the user is reachable.


MULTI-TARGET TRACKING USING PREDEFINED REFERENCE SET IN NON-OVERLAPPING CAMERAS BY ADABOOST ALGORITHM

Multi-target tracking is still recent approach which is used to find the same object across different camera views and also used to find the location and sizes of different object at different places [7]. Tracking and detection of moving objects are challenging research topic of many computer vision applications. Nowadays, the demand of surveillance camera is increasing rapidly which is useful for developing surveillance as well as monitoring purpose. Some previous methods are used for multi-target tracking that are color histogram, brightness transfer function (BTF) [11]. Many times it is not possible to cover complete area of interest by using single camera, such a cases there is need to use multi-target tracking system with non-overlapping field of views (FOV) [2]. In this paper we use method of feature extractions that is AdaBoost. The paper proposes the reference set based tracking in non-overlapping FOV‟s due to overlapping FOV‟s are having high cost. In this work widely used features HSV color histograms, Local Binary Pattern (LBP), Histogram of Gradient (HOG) are used to extract color, texture, shape of target. We use LBP, HOG, HSV color histogram features to determine person‟s characteristics. KeywordsMulti-target tracking, AdaBoost, feature extraction, LBP, HOG, HSV color histogram.

I. INTRODUCTION
The paper presents an object extraction system for multi-target tracking application that needs pixel wise extraction accuracy. Multi-target tracking is the research approach which is used to find the locations and sizes of multiple targets. Nowadays CCTV cameras are used for many purposes such as in public places, schools, airports, colleges etc. For security purpose the rate of use of CCTV cameras are increases [2]. The paper proposes to track the object robustly using multi-target tracking concept in non-overlapping field of views. There are two types of field of views that are overlapping FOV"s and non-overlapping FOV"s. The paper focused on non-overlapping FOV"s. The main goal of this project to find the appearance of same object across the multiple cameras output tracks. In computer vision automatic tracking of multiple targets is the recent topic which is used for industrial applications such as security purpose or monitoring purpose. The idea of multi-target tracking which is done by comparing both outputs that are output obtained from different cameras is compared with multi-cam dataset output. Using techniques for human detection, multi-target tracking system can play important role to capture location of people at public areas such as stores and travel sites and then produce congestion analysis to assist in the management of the people [3]. In such a way tracking system can monitor express ways and junctions of the road network. In some cases it is necessary to analyse the behaviour of people and vehicles also and check whether these behaviours are normal or abnormal. For example multi-target tracking system set in parking lots and supermarkets could track abnormal behaviour of theft which is useful to identify any criminals quickly and then contact the police immediately [1].

II. RELATED WORK
In this paper we propose the novel approach of multi-target tracking in non-overlapping cameras. With the help of tracking system we get the exact location and size of the object which are captured by the surveillance camera. Due to variation in illumination conditions, and camera imaging characteristics, there might be significant appearance change of target across camera views. In multitarget tracking system there is need to track same object across multiple camera output tracks provided cameras are consist of non-overlapping regions [2]. Consequently the same target may appear in very different in two cameras which causes to error. Hence in most previous methods the appearance similarity is captured either using color histogram or based on brightness transfer function (BTF) [11]. BTF is used to find color difference between different cameras output. Our main aim is to implement multi-object tracking system in non-overlapping cameras. This project proposes reference set based appearance of the target to determine the similarity of multiple targets in different camera. The goal of multi-tracking across camera is to associate tracks in different cameras which contain the same object [2].Our main goal is to implement the algorithm for multi-target tracking in non-overlapping cameras using predefined reference set.

III. CONTRIBUTION OF THE PAPER
The contributions of this paper are as follows  The paper proposes the work for providing solution to track same target across multi-cam dataset [2].  A novel reference set based appearance model to improve multi-target tracking in a network of non-overlapping camera.  To track one target in one camera output video stream and register target features.  On another camera i.e. multi-cam dataset output extracting features of target and comparing with pre-registered target features and to identify whether same target.  This paper gives the detail information of art in personal identification at a distance and fusion of data from multiple cameras [8].  We also produce detail discussion on future research direction in multi-target tracking system.

IV. PROPOSED WORK
The basic aim of multi-target tracking across non-overlapping cameras is to automatically recover the trajectories of all targets and keep their identities consistent while they travel from one camera to another camera [3]. Here we propose two methods for tracked target detection

AdaBoost Method:
Basically, AdaBoost is stands for "Adaptive Boosting". It is good for things with soft edges like facial recognition. It is used for producing a strong classifier. The strong classifier is defined as threshold linear combination of weak classifier outputs. Adaboost is very simple to implement and it does feature selection resulting in relatively simple classifier. It is sensitive to noisy data and outliers. Adaboost improves classification accuracy and it can be used with many different classifiers [17]. It is called adaptive boosting because it uses multiple iterations to generate a single composite strong learner. Adaboost is an ensemble learning algorithm that can be used for classification. It contains many weak classifiers and minimizes overall error. In this block here first track moving object and take it as a camera 1 output. After this take its features and store it as a reference set.

Divide image with equal height:
After tracking object the whole body is dividing into two parts that are torso and legs. Torso is upper part of detection and legs are lower part of detection. Here whole body is divided into upper and lower part with equal height [2].

Register for further use:
All above obtained information is stored as a reference set which is used for comparison purpose. This is our register set which is use to comparison with different cameras outputs dataset.

Compare with register features:
In this block compare pre-recorded multiple cameras outputs dataset with the registered features to identify appearance of similar object. For comparison purpose here uses two methods that are as follows.

Feature Extraction:
The features obtained from first block the features are extracted with using AdaBoost feature extraction method. AdaBoost is stands for "Adaptive Boosting". AdaBoost is the method of feature extraction. The role of AdaBoost algorithm is used for selection purpose. It can be used in conjunction with many other types of learning algorithms to improve performance of the system [17]. It is referred as the best classifier. Here we take three kinds of appearance features that are HSV color histograms, Local binary pattern (LPB) and histogram of gradient (HOG). These appearance features are used to capture color, texture, and shape information of object. Here we are partitioning body image into torso and legs for each detection with equal height. For this total nine features are extracted that are Torso HSV, Torso LBP, Torso HOG, Body HSV, Body LBP, Body HOG, Legs HSV, Legs LBP, Legs HOG. For distance measurement the Bhattacharya coefficient is used [2]. To measure the similarity of two discrete or continuous probability distributions the Bhattacharya distance is proposed.

HISTOGRAM OF GRADIENT:
HOG is a feature descriptor in computer vision and image processing which is used for object detection. It is used to count occurrence of gradient orientation localize portions of an image. The image is divided into small connected regions called cells. A HOG direction is compiled. HOG has advantage over other descriptor, it operates on local cells and it is invariant to geometric and photometric.

HSV COLOR HISTOGRAM:
Histogram is a graphical representation of number of pixel in an image. X-axis represents tonal scale i.e. black at the left and white at the right. Y-axis represents number of pixel in an image in a certain area of tonal scale. A color histogram of an image represents the distribution of the composition of colors in the image. It shows different types of colors appeared and number of pixel in each type of the color appeared. HSV stands for the angle around the central axis corresponds to "hue". The distance from the axis corresponds to "saturation". The distance along the axis corresponds to "value". Hue deals with the purity of the color. Saturation determines the amount of white light mixed with the original value. Value gives the intensity of color.

Principle of the formation of a color histogram:
We can simply count the number of pixel for each 256 scales in each of the 3 RGB color and plot them on 3 individual bar graphs. When we compute the pixel of different colors in an image if the color space is large then we can first divide the color space into certain number of small intervals.

@IJMTER-2017, All rights Reserved 128
Each of the intervals is called a bin. This process is called color quantization. Then by counting the number of pixel in each of the bins we get the color histogram of the image.

LOCAL BINARY PATTERN (LBP):
LBP is a type of visual descriptor used for classification in computer vision. LBP is a particular case of texture spectrum model. It has been found a powerful feature for texture classification. When LBP is combined with HOG it improves the detection performance.

VI. RESULTS
The following images show our result of experiment. Figure 1 is the original image which has been taken from dataset. Here we want to extract three features that are HOG which shown in figure  1a,HSV Color histogram which is shown as figure 1b and figure1c indicate that RGB to HSV color transformed image, LBP features which is shown in figure 1d. Figure 2 shows the different image and its three individual features as per the figure 1. Figure 2 indicate another example of feature extraction.


Wearable sweat detector device design for health monitoring and clinical diagnosis

Miniaturized sensor is necessary part for wearable detector for biomedical applications. Wearable detector device is indispensable for online health care. This paper presents a concept of an wearable digital health monitoring device design for sweat analysis. The flexible sensor is developed to quantify the amount of hydrogen ions in sweat and skin temperature in real time. The detection system includes pH sensor, temperature sensor, signal processing module, power source, microprocessor, display module and so on. The sweat monitoring device is designed for sport monitoring or clinical diagnosis.

Introduction
With the government proposed the concept of "internet plus", online health care services become the development tendency of the future of health care industry. Sweat is a body fluid with present scant use as clinical sample. It's a kind of body fluid detection, is noninvasive, easy to get and able to realize realtime monitor compared to others. In the situation, we focus on sweat detection, design a wearable device to detect both pH concentration of sweat and skin temperature . The main components of sweat is water (99%). Beyond that, the slightly acidic body fluid (pH4.0-6.8) contains some electrolytes (such as sodium, chlorine) and small amount of organics (such as urea, pyruvic and lactate ) Very small concentration of proteins, amino acids, metal ions, inhibitors, antigens, antibody and a variety of xenobiotic are also found in sweat [1]. Physical disorders will change the ingredients of sweat, including producing new components or changing the concentration of components. Therefore, sweat detection is a method of both clinical diagnosis and health monitoring. Monitoring pH of sweat during exercise could realize the state of hydration. Besides, the concentration of hydrogen is associated with skin disease. Skin temperature is formed by heat exchange between body and surrounding environment, and is related to body temperature. It has great application in medical and human engineering research, especially in the field of thermal comfort.
Currently there are some wearable devices have been designed and became commercially available [2][3][4][5][6][7][8], but most of them only detect physiologically parameters of body, lack of observe physical state from molecular level. Thus, in this paper, sweat pH and skin temperature were selected from two aspects, both physical and biochemistry, to monitor physiological state. The system of the wearable monitoring device is illustrated in introduction. We design a system to detect the sweat pH and skin temperature when tester doing exercise or perspire after drug stimulation. The device is intergrated by sensors, signal processing circuit, microprocessor, power source and data presentation, realized in situ physiological monitoring in real time. The signal transmission of the system is shown in Figure.1. In sensor part, we use Antimony electrode as ion selective electrode, for it quick response, easy to operate and accuracy could reach 0.01pH in acidic solution, which achieve the requirement of sweat pH detection, also it is economical and reusable, Antimony electrode is shown in figure 2. And Ag-AgCl electrode is reference electrode. Antimony electrode is metal-metal oxide electrode, the electrode potential is generated on the interface between the metal and the oxide that covered the metal. When the sensor is in contact with the solution, surface is oxidized to generate Sb 2 O 3 , the potential difference between metal antimony and oxide depends on the concentration of Sb 2 O 3 , and the concentration of Sb 2 O 3 is related to the concentration of hydrogen ion in the solution, its chemical reaction is as Equation (1,2):

pH and temperature sensors
Measuring standard pH solution by pH sensor, result show in Figure 3, V o is the output voltage of pH sensor. As figure 3 shows, as sulotions pH less than 7, there is a linear relation between voltage output of sensor and solution pH.Fitting the curve, the sensitivity of pH sensor is 58mV/pH.   As for temperature sensor, we choose LM35, its sensitivy is 10mV/℃, output voltage is proportional to temperature, extra calibration is not required。

Singal processing circuit
In order to ensure the output value of the sensor in the microprocessor input range, singal processing circuit is requied. Singal processing circuit include voltage follower, amplifier and high frequency filter. Circuit diagram design and debug circuit are shown in figure 4.

Microprocessor display screen and source
Wearable devices require a small size of the microprocessor, so we choose Arduino uno single chip. Arduino uno realizes the calculation and conversion of the two analog channel data. Display screen adopt 1602 liquid crystal display, for it low power,small volume and light weigh.
We use Arduino development platform to program, the output anolog voltage signals of pH and temperature sensor respectively input from two analog ports, anolog voltage signals convert to pH and temperature by calculation, then tranmiss to 1602, 1602 shows user these two parameters.About power supply, we choose 3V button battery in series connection.Device and prototype circuit is shown in figure  5.

Conclusion
In this paper, we designed a wearable sweat monitoring device to detect pH in sweat and skin temperature,and finished preliminary debug. A brief introduction about principle and construction is given above. The simulation prove the feasibility of design and sensitivity of the sensor. In further study,  we would add more sensor to detector more parameters, get more comprehensive physiological information; circuit will be painted on PCB board to make device smaller, and easy to encapsulation; signal will be transfer to Bluetooth serial port, upload data to mobile phone to realize online health.


EVENTUAL SMOOTHNESS AND ASYMPTOTIC BEHAVIOUR OF SOLUTIONS TO A CHEMOTAXIS SYSTEM PERTURBED BY A LOGISTIC GROWTH

. In this paper we study the chemotaxis-system (cid:40) deﬁned in a convex smooth and bounded domain Ω of R n , n ≥ 1, with χ > 0 and endowed with homogeneous Neumann boundary conditions. The source g behaves similarly to the logistic function and satisﬁes g ( s ) ≤ a − bs α , for s ≥ 0, with a ≥ 0, b > 0 and α > 1. Continuing the research initiated in [33], where for appropriate 1 < p < α < 2 and ( u 0 ,v 0 ) ∈ C 0 (¯Ω) × C 2 (¯Ω) the global existence of very weak solutions ( u,v ) to the system (for any n ≥ 1) is shown, we principally study boundedness and regularity of these solutions after some time. More precisely, when n = 3, we establish that - for all τ > 0 an upper bound for ab , || u 0 || L 1 (Ω) , || v 0 || W 2 ,α (Ω) can be pre-scribed in a such a way that ( u,v ) is bounded and H¨older continuous beyond τ ; - for all ( u 0 ,v 0 ), and suﬃciently small ratio ab , there exists a T > 0 such that ( u,v ) is bounded and H¨older continuous beyond T . Finally, we illustrate the range of dynamics present within the chemotaxis system in one, two and three dimensions by means of numerical simulations.

Introduction and motivations.
According to the logistic model in population dynamics (Pierre-François Verhulst, 1838), the self-limiting growth of a biological u t = ∇ · ( ∇u − χu∇v), v t = ∆v − κv + u. (1) In system (1), the parameters , χ and κ are positive constants. Although we will consider such cross diffusive terms in terms of chemotactic movement, they are able to appear for a variety of reasons, including growth of the underlying solution space [14,43,44,5] and inhomogeneities in the underlying environment [4].
In the present case we prescribe the following description to the equations. Chemoattractant, v, spreads diffusively, decays with rate κ and is also produced by the bacteria with rate 1. The bacteria diffuse with mobility and drift in the direction of the gradient of concentration of the chemoattractant with velocity χ|∇v|; χ is called chemosensitivity. Hence, once the initial cells distribution and chemical concentration (that is u 0 (x) = u(x, 0) and v 0 (x) = v(x, 0)) are given, under zero-flux boundary conditions on both u and v, the previous problem describes the chemotactic dynamics of a cells population in a totally insulated domain.
Real observations show that this movement may eventually lead to aggregation processes, in which the density of the cells spatially coalesces and grows without bound (chemotactic collapse). Mathematically, this collapse implies that (possibly) u becomes unbounded at one or more points of its domain at a certain instant (blowup time). It is known that, in a one-dimensional domain, all the solutions of (1) are global and uniformly bounded in time (see [22]), while that in the n-dimensional setting, with n ≥ 2, unbounded solutions to the same problem have been detected (see, for instance, [8] and [39]).
In line with the chemotactic scenario, in [10] for radial solutions and in [20] for non-radial, the authors prove that under suitable assumptions the bacteria concentration blows up in finite time, for certain domains of R 2 and in the cases in which the second differential equation of (1) is replaced by 0 = ∆v − v + u (parabolicelliptic case). Moreover, for the classical parabolic-parabolic (or fully parabolic) case, estimates from below and numerical computations for the blow-up time of unbounded solutions to (1) are derived in [24] and [7], respectively (see also [16] for a more general analysis).
Furthermore, a number of interesting results concerning properties of solutions to chemotaxis-systems have been also attained for a broader class of problems, in which the first equation of (1) reads u t = ∇ · (S(u)∇u) − ∇ · (T (u)∇v). Precisely, bounded or unbounded solutions of the corresponding problem is determined by the asymptotic behaviour of the ratio T (u)/S(u), especially in terms of the space dimension; we refer, for instance, to [6] and [40] for the parabolic-elliptic case and to [9,17,27,28,38] for the parabolic-parabolic case.
As an approach towards the model of self-organizing behaviour of cells populations, it seems coherent to adapt the original Keller-Segel formulation to the case in which the temporal evolution of a cells distribution may be perturbed by the proliferation and the death of the cells themselves. Mathematically it is possible by adding a linear combination of power functions depending on u and, possibly, on |∇u| to the first equation of system (1) (see details in [1,18,32] for pure chemotaxissystems, but also in [31] for weakly coupled systems).
Conforming to the previous paragraph, this investigation focuses on fully parabolic chemotaxis-systems which are complemented by logistic-type effects. To the best of our knowledge, the following are the most recent and partial results in this regard; under Neumann boundary conditions and in a convex smooth and bounded domain Ω of R n , n ≥ 1: i) For the problem the existence of global weak solutions is proven for any nonnegative and sufficient regular initial data (u 0 , v 0 ) and arbitrarily small values of b > 0. Moreover, if n = 3 and a is not too large, these solutions become classical after some time (see [13]). ii) For the problem where g generalizes the logistic source in (2), and satisfies g(0) ≥ 0 and g(s) ≤ a − bs 2 , for s ≥ 0, and with a ≥ 0, b, χ, τ positive constants, it is proved in [37] that if b is big enough, for all sufficiently smooth and nonnegative initial data, (u 0 , v 0 ), the problem possesses a unique bounded and global-in-time classical solution. Furthermore, even though [21] yields global classical solutions to (3) for any (not necessarily large) b > 0, which remain bounded in a (convex smooth and bounded) domain of R 2 , the same conclusion is not clear to occur for n ≥ 3. iii) For the same problem (3), but with source term g controlled, respectively from below and above, by −c 0 (s + s α ) and a − bs α , for s ≥ 0, and with some α > 1, a ≥ 0 and b, c 0 > 0, global existence of very weak solutions is attained in [33]. Moreover, for n = 3, sufficient conditions on the initial data and the coefficients of the source g which ensure the boundedness of such solutions are discussed in [34]. Additionally, analogous conclusions dealing with parabolic-elliptic versions of models related to (2) or (3) are also available. For instance, in [29] it is proven that weak solutions exist for arbitrary b > 0; moreover they are smooth and globally classical if b > (n − 2)/n. Finally, with source term g as in the above item iii), global existence of very weak solutions and their boundedness and eventual smoothness properties are established in [35].
Our starting point are the contributions [33] and [34], where these partials results are, respectively, presented: 1 ) existence of global very weak solutions: for any n ≥ 1 and α ∈ (1, 2) satisfying α > 2 − 1 n , the global existence of very weak solutions (u, v) to the system is shown for any nonnegative initial data, (u 0 , v 0 ) ∈ C 0 (Ω) × C 2 (Ω), and under zero-flux boundary condition on v 0 ; 2 ) boundedness of very weak solutions: in the most realistic three dimensional setting, these very weak solutions derived in 1 ) are bounded. More precisely, if the ratio a/b does not exceed a certain value and the initial data are such that ||u 0 || L p (Ω) and ∇v 0 L 4 (Ω) are small enough then, for appropriate 9/5 < p < α < 2, (u, v) is uniformly-in-time bounded in (0, ∞). A natural and complementary question connected to the above results 1 ) and 2 ) is to show that singularities of solutions to (4), possibly arising after some finite time, disappear. In other words, our main objective is to analyse whether solutions to (4), with "unclear" behaviour over a certain period, become eventually bounded and smooth beyond some time; additionally, it is also interesting to investigate and characterize their long time behaviour. Specifically, we address the following issues, which are directly related to each other.
-For any fixed time τ > 0, is it possible to claim that the very weak solutions of (4) improve their regularity and become bounded beyond such τ ? According to Theorem 2.1 below, by imposing suitable smallness conditions on u 0 and v 0 , measured in proper norms, the very weak solutions are eventually bounded and Hölder continuous provided the ratio a/b does not exceed a certain value.
-Is it possible to claim that any of the very weak solutions of (4) improve their regularity and become bounded beyond some time, regardless the initial sizes of u 0 and v 0 ? Again under smallness assumption on the ratio a/b, this question is positively shown in the forthcoming Theorem 2.2. It is proved that it is always possible to find a T > 0 such that the very weak solution are bounded and Hölder continuous beyond T , independently by some norm of the initial data u 0 and v 0 ; clearly, the time beyond which it occurs depends on such initial norm. -Is it possible to characterize the asymptotics of bounded solutions, i.e. their behaviour for t → ∞? If on the one hand all the solutions of the Pierre-François Verhulst model converge to the constant steady state K (as shown in §1), then the chemotaxis-diffusion-growth models may lead to a spatially uniform steady state, or to a spatially heterogeneous steady state, as well as irregular spatiotemporal solutions, possibly defined by time-periodic or timeirregular pattern formations (see [23,2]). The theoretical analysis dealing with the behaviour of the solutions to (4) when the time increases is currently unclear and goes beyond the scope of this paper; here, we present an important number of numerical simulations (in one, two and three dimensions) concerning the long time behaviour of bounded solutions and also the blow-up scenario of unbounded ones (see §6).
3. Preliminaries and definition of suitable solution. The following results are herein formulated according to our purposes, and they are used through the paper to prove the claimed theorems. For the sake of clarity, we also close the section with the definition of very weak solutions to (4).
Then, for any concave function ϕ : R → R this inequality holds 1 in Ω ⊂ R n , n ≥ 1.
Then the operator (−∆ + 1) is sectorial in L p (Ω) and for any ρ ≥ 0 possesses fractional powers (−∆ + 1) ρ , with dense domain D((−∆ + 1) ρ ). Moreover, there exist positive constants C S and µ 1 such that -for all t > 0 and p ≤ q < ∞ the following L p -L q estimates hold -for all t > 0 and 1 < p ≤ q < ∞ the operator e t∆ ∇· possesses a uniquely determined extension to an operator from L p (Ω) to L q (Ω) obeying this L p -L q estimate Proof. See Section 2. of [9] and Lemma 1.3 of [36].
We also make use of these elementary results.
Lemma 3.3. Let y be a positive real number satisfying y ≤ c(y l + 1) for some c > 0 and 0 < l < 1. Then y ≤ max{1, (2c) Proof. Let us suppose y ≥ 1 (if y ≤ 1 there is nothing left to show): y l ≥ 1 so that y ≤ c(y l + 1) ≤ 2cy l and hence y ≤ (2c) Lemma 3.4. For any A, B ≥ 0 and p > 1 we have Proof. Let us prove relation (12) for both A and B strictly positive; otherwise the inequality is obvious. If Finally, we give these definitions.
is said to be a very weak subsolution of (4) in Ω × (0, T ) if g(u) and u∇v belong to is said to be a weak γ-entropy supersolution of (4) in for all nonnegative ψ ∈ C ∞ 0 (Ω × [0, T )). Definition 3.7. Let T > 0. A pair (u, v) of functions is called very weak solution for problem (4) in Ω × (0, T ) if it is both a very weak subsolution and a weak γ-entropy supersolution of (4) in Ω × (0, T ), in the sense of Definitions 3.5 and 3.6.
A global very weak solution of (4) is a pair (u, v) of functions defined in Ω×(0, ∞) which is a very weak solution of (4) in Ω × (0, T ) for all T > 0. 4. Approximate problem and existence of very weak solutions. In preparation for the main estimates, by means of a parameter ε ∈ (0, 1), we define a perturbed chemotaxis-system, properly constructed to approximate and hence solve and analyze the original problem (4). Precisely, we rely on these specific results.
We also present this result concerning both the existence and boundedness properties of global very weak solutions to system (4).

Proposition 2.
Let Ω be a convex smooth and bounded domain of R n , with n ≥ 1. For some α ∈ (1, 2), with α > 2 − 1 n , let us assume g ∈ C 1 ([0, ∞)), such that g(0) ≥ 0, and verifying both assumptions (H1 α ) and (H2 α ). Then for all nonnegative functions (u 0 (x), v 0 (x)) ∈ C 0 (Ω)×C 2 (Ω), with ∂v0 ∂ν = 0 on ∂Ω, problem (4) admits at least one global very weak solution (u, v), according to Definition 3.7. More precisely, this solution is the limit of a sequence of globally bounded couples of functions (u ε , v ε ) which classically solve the approximate problem (13), in the sense that, for any t > 0 and 1 < p < α < 2, as ε → 0 we have Additionally, for n = 3, there exists a positive real δ > 0 such that if a and b are such that a/b < δ then for all 9/5 < p < α < 2 it is possible to find a λ > 0 with the following property: Proof. For the existence question, see the proof of Theorem 2.1 in [33]. For the boundedness, Theorem 2.1 in [34]. Remark 1. By taking into account the regularity properties that the limit (u, v) provided by Proposition 2 inherits from the sequence (u ε , v ε ), we have that for n = 1 the Sobolev inequalities imply that both W 1,2 (Ω) and W 1, α α−1 (Ω) are compactly embedded in C(Ω). As a consequence, in the one dimensional setting, for any t > 0 the solution (u(·, t), v(·, t)) to problem (4) belongs to L ∞ (Ω) for any choice of the initial distribution (u 0 , v 0 ) and the parameters a and b which determine the behaviour of function g.

5.
A priori estimates and proof of the theorems. In this section our principal objective is to infer some ε-independent and uniform-in-time estimates for both u ε and v ε components of the solution (u ε , v ε ) to (13). In this sense, the following lemma includes some inequalities which are strongly employed with this aim.

GIUSEPPE VIGLIALORO AND THOMAS E. WOOLLEY
In addition, since p/α < 1, the function t → Ω u α ε p α is concave; an application of the Jensen inequality (8) where we have also used relations (14c) and (12) with p = α/p > 1.
On the other hand, since 9/5 < α < 2, the Sobolev embedding W 2,α → W 1,4 infers a positive constantĈ such that The linear (Cauchy) problem extracted from the second equation of (13), that is , has a unique classical and global solution, so that we can apply relation (12) of Proposition 2 of [33] in the interval [t 1 , t 2 ]. Precisely, in line with the nomenclature used in such a proposition, setting p = q = α > 1, using that for any A, B ≥ 0 the relation (A + B) α ≤ 2 α (A α + B α ) holds and estimating the interpolation norm v ε (·, t 1 ) α,1− 1 α with v ε (·, t 1 ) W 2,α (Ω) , we can find a positive constantC, independent on ε, such that t2 t1 Ω In this way, by integrating (20) between t 1 and t 2 , and in view of (14c), relation where C Ω =ĈC. The sum of the expressions (18) and (22), once the bound (19) is also taken into account, concludes the proof.

EVENTUAL SMOOTHNESS AND ASYMPTOTICS OF SOLUTIONS 3033
Proof. For any t 1 ≥ 0, let us define the function By retracing the proof of Lemma 5.2 in [34], and using the same constants therein introduced, one can show that Φ ε satisfies this absorptive differential inequality for any t > t 1 Through a continuous dependence argument, regardless the size of the positive root of Θ 0 (ξ), it is always possible to find a value M lim > 0 with the property that the equation Θ M lim (ξ) = 0 possesses two positive roots (let us say ξ 1 and ξ 2 , with ξ 1 < ξ 2 ) such that ξ lim = ξ 1 < 1. In this sense S M lim ≡ {ξ lim , ξ 2 }. Hence for any ξ ≥ 0 we have Additionally, Φ ε (t) ≡ ξ lim satisfies this initial problem Successively, for any τ > 0 let us define From now on our aim is to justify the existence of at ∈ (t 1 , t 1 + τ /2) and show that Φ ε (t) satisfies the initial problem Indeed, in view of (26), assumption (23) would imply relations (24), which in turn would allow us to apply an ODE comparison principle to problems (25) and (27) and conclude that Φ ε (t) ≤ ξ min for all t >t, ξ min being some zero of Θ Mε (ξ), with 0 < ξ min < ξ lim < 1.
The average theorem establishes the existence of a timet belonging to (t 1 , t 1 + τ 2 ) such that In this way, an application of (15) from Lemma 5.1 with Through (23) and (26) we derive that so that, in particular, 1 2 Ω |∇v ε (·,t)| 4 α 4 < 1. Subsequently, in view of α/4 < 1, we finally get As claimed, this implies that Φ ε (t) ≤ ξ min for all t >t, for some ξ min = ξ min (τ ) < 1; thereafter, since ||u ε || L p (Ω) ≤ p Now, the next conclusion proves that the boundedness of ||u ε || L p (Ω) is sufficient to show that for some 3 < q < 3p/(p − 3) the W 1,q (Ω)-norm of the component v ε is controlled by a certain ε-independent and uniform-in-time positive constant; more exactly, we show that if u ε ∈ L p (Ω) for some t ≥ t 1 , with t 1 ≥ 0, than v ε belongs to some W 1,∞ (Ω) for t beyond t 1 . We will use also some ideas presented in [3].
In addition, for all t ≥ t 1 + τ * , we have
After these preparations, the proof of our main results consists in demonstrating higher regularity for (u ε , v ε ); precisely, we first apply to the classical solution (u ε , v ε ) of problem (13) regularity results which enable us to derive ε-independent bounds of this solution in some Hölder space. Then, by properly interpreting the two equations of the same (13) as Neumann boundary value problems with Hölder and bounded sources and coefficients, we gain higher regularity for (u ε , v ε ). Subsequently, passing to the limit and organizing the statements, our claims follow.
6. Numerical simulations. In this section we numerically test the presented results by simulating the chemotaxis systems in one, two and three dimensions. Further, we investigate whether the global solutions are bounded and stationary, or whether they have complex temporal dynamics, such as moving peaks, oscillations, or chemotactic blow-up. Specifically, we use finite element methods to simulate on domains of different dimension, employing Neumann conditions on the domain boundaries, where applicable. Further, (unless otherwise stated) the initial conditions are uniformly randomly generated with mean equal to the homogeneous steady state, (a/b) 1/α and with range (a/b) 1/α /100. Domain sizes, domain discretisations and parameter values are given in the captions of each figure. Figure 1 illustrates the one-dimensional evolution of system (45) over time and over multiple values of α. As highlighted in Remark 1 of Proposition 2, we expect that when 1 < α < 2 the solution converges to a bounded solution, which we note is heterogeneous and stationary (see Figure 1(a), and, in particular, compare the figures as times 50 and 100). However, although the theory ensures existence of global (and bounded) solutions only for α > 1, we can see that this is not a bifurcation point since in Figure 1(b) the system also converges to a bounded, heterogeneous, stationary solution when α = 0.9.  In Figure 2 we further illustrate the coherence of Remark 1 in this paper as it suggests that for all 1 < α < 2 the one-dimensional simulations should be bounded no matter the initial conditions on u and no matter the size of b. Figure 2(a) illustrates that even after increasing the average initial value of u to 100 the solution still tends to a stable, heterogeneous, bounded solution. Equally, in Figure 2(b), where the parameter b has been reduced to 0.2 we see that the solution is bounded. However, here we see a new dynamic of continuously evolving peaks. Namely, peaks appear approximately in the centre of the domain and then travel towards the boundaries x = 0, or x = 10. The direction of travel depends on which side of x = 5 the peak first appears. Further, the peaks appear to alternate in directions, with the first peak travelling to the left.  Figure 1(a). However, each simulation involves a single parameter change. Specifically, in (a) a larger initial condition for u was used (100 was added to the mean); in (b) the parameter b was reduced to 0.2; Finally, in (c) the spatial solution domain has been reduced from 10 to 1.
Up until now all of the solutions have presented bounded, spatially heterogeneous solutions. However, stationary, bounded, uniform solutions are also possible and these are illustrated in Figure 2(c). Specifically, using spectral analysis it is well known [41,19] that concentration heterogeneity arises through a symmetry breaking of the balance between diffusion and chemotaxis. Hence, patterning of the solution domain requires a minimal spatial scale before instability of the uniform steady state can occur [42,15]. Since the solution domain in Figure 2(c) has been reduced below this critical spatial scale the system simply tends to a uniform steady state. Figure 3 illustrates the two-dimensional evolution of system (45) for α = 1.6 and 1.1. The solutions are simulated on a circular domain of radius 5. The two simulations contain values of α either side of the critical value of 3/2 (recall again Proposition 2), which states that when α is above the critical value the solutions exist, are global and (possibly) bounded. In the specific case of Figure 3(a) the solutions are, once again, bounded, heterogeneous and stationary. However, for shows the full extent of the peak, which is growing without bound. α = 1.1, which is below this critical value, we are able to produce solutions that are prone to blow up. Note that this difference in convergence is purely a property of domain dimension because, apart for this difference, figures 1(a) and 3(b) have the same parameters values. Further, we note that the exploding solution appears to occur foremost on the boundary and that the blow-up occurs very quickly since the maximum value of the solution is over 10 3 (see inset of Figure 3(b)) in just over 17.4 time points. By t ≈ 17.43 the peak is over 10 12 and the solution fails to converge. Finally, Figure 4 illustrates the three-dimensional evolution of system (45) for α = 1.8 and 1.3, which are values either side of the critical value of 5/3. The solutions are simulated within a solid sphere of radius 7. Once again, we see that Proposition 2 holds in Figure 4(a) as, for suitable initial data, the system evolves to a stationary solution, which is both bounded and heterogeneous. On the other hand, we see that the simulation within Figure 4(b) blows up within 1.44 time units. In both cases, we can see that the balls become darker towards their centre. This means that the population u becomes denser there because the isosurfaces corresponding to darker colours correspond to larger values of u (see caption of Figure 4 for more details). Whilst Figure 4(a) remains finite over the entire region, Figure 4(b) contains a region that begins to undergo chemotactic collapse (see the dark spot in Figure 4(b)). At the illustrated time point the density is over 10 6 and grows to over 10 12 , before the simulation fails to converge. In this case, in contrast to Figure 3(b), the unstable growth occurs within the domain, rather than on the boundary. In summary, these simulations illustrate the veracity of the proof contained within this paper. Specifically, global boundedness of a chemotactic system depends on the spatial dimension we are considering, as well as the kinetic parameters of the system.