Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages&#x2014;it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.
In the IoT healthcare and BAN domain, all of the existing solutions do sensing and communication in the digital domain (using ADCs DACs Microprocessors), which needs more power than analog sensing and communication. For example, Yang's group @cite_18 developed a miniaturized node that incorporates wireless communication, on-board processing, nine-axis motion tracking, and other sensors @cite_0 . It also developed an e-AR sensor @cite_27 , a small device to be worn behind the ear that captures information about the balance of the wearer such as gait, posture, skelet al joint shock-wave transmission, and activity of the individual. Yuce's group developed techniques based on Ultra Wide Band (UWB) wireless technology to reduce the power consumption of body-worn sensors @cite_11 @cite_8 . Another important example is the activity recognition of the user using various body sensors viz., accelerometer and gyroscope data. The data from the sensors is digitally processed and stored into a wrist-band device that syncs the data with the mobile phone using Bluetooth technology @cite_19 . Unlike these approaches, we adopt an entirely different approach based on analog sensing and communication that does not use any power-hungry ADCs (see Table ).
@cite_18 @cite_8 @cite_0 @cite_19 @cite_27 @cite_11
2042956874 2002261403 1644895716 1970099751 2017634428 1973675857 1980481605 1980909913 2611258330 2188373600 2066603728 2103112159 2553915786 2176664831 2168538666 2534709300 2403657431 2295333746 1981094339 2195342085
The basic requirement of wireless healthcare monitoring systems is to send physiological signals acquired from implantable or on-body sensor nodes to a remote location. Low-power consumption is required for wireless healthcare monitoring systems since most medical sensor nodes are battery powered. The emergence of new technologies in measuring physiological signals has increased the demand for high data rate transmission systems. Ultra-wide band (UWB) is a suitable wireless technology to achieve high data rates while keeping power consumption and form factors small. Although UWB transmitters are designed based on simple techniques, UWB receivers require complex hardware and consume comparatively higher power. In order to achieve reliable low power two-way communication, a sensor node can be constructed using a UWB transmitter and a narrow band receiver. This paper proposes a new medium access control (MAC) protocol based on a dual-band physical layer technology. Co-simulation models based on MATLAB and OPNET have been developed to analyze the performance of the proposed MAC protocol. We analyzed the performance of the MAC protocol for a realistic scenario where both implantable and wearable sensor nodes are involved in the data transmission. Priority-based packet transmission techniques have been used in the MAC protocol to serve different sensors according to their QoS requirements. Analysis is done with regard to important network parameters, such as packet loss ratio, packet delay, percentage throughput, and power consumption. A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods. As the proliferation of pervasive and ubiquitous computing devices continues, users will carry more devices. Without the ability for these devices to unobtrusively interact with one another, the user’s attention will be spent on coordinating, rather than using, these devices. We present a method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain. We also show that these low-cost sensors perform similarly to more expensive accelerometers for the frequency range of human motion, 0 to 10Hz. We also present results from a large test group illustrating the algorithm’s robustness and its ability to withstand real world time delays, crucial for wireless technologies like Bluetooth and 802.11. We present results that show that our technique is 100 accurate using a sliding window of 8 seconds of data when the devices are carried in the same location on the body, is tolerant to inter-device communication latencies, and requires little communication bandwidth. In addition we present results for when devices are carried on different parts of the body. Background: Over the past few years, the world has witnessed an unprecedented growth in smartphone use. With sensors such as accelerometers and gyroscopes on board, smartphones have the potential to enhance our understanding of health behavior, in particular physical activity or the lack thereof. However, reliable and valid activity measurement using only a smartphone in situ has not been realized. Objective: To examine the validity of the iPod Touch (Apple, Inc.) and particularly to understand the value of using gyroscopes for classifying types of physical activity, with the goal of creating a measurement and feedback system that easily integrates into individuals’ daily living. Methods: We collected accelerometer and gyroscope data for 16 participants on 13 activities with an iPod Touch, a device that has essentially the same sensors and computing platform as an iPhone. The 13 activities were sitting, walking, jogging, and going upstairs and downstairs at different paces. We extracted time and frequency features, including mean and variance of acceleration and gyroscope on each axis, vector magnitude of acceleration, and fast Fourier transform magnitude for each axis of acceleration. Different classifiers were compared using the Waikato Environment for Knowledge Analysis (WEKA) toolkit, including C4.5 (J48) decision tree, multilayer perception, naive Bayes, logistic, k-nearest neighbor (kNN), and meta-algorithms such as boosting and bagging. The 10-fold cross-validation protocol was used. Results: Overall, the kNN classifier achieved the best accuracies: 52.3 –79.4 for up and down stair walking, 91.7 for jogging, 90.1 –94.1 for walking on a level ground, and 100 for sitting. A 2-second sliding window size with a 1-second overlap worked the best. Adding gyroscope measurements proved to be more beneficial than relying solely on accelerometer readings for all activities (with improvement ranging from 3.1 to 13.4 ). Conclusions: Common categories of physical activity and sedentary behavior (walking, jogging, and sitting) can be recognized with high accuracies using both the accelerometer and gyroscope onboard the iPod touch or iPhone. This suggests the potential of developing just-in-time classification and feedback tools on smartphones. [J Med Internet Res 2012;14(5):e130] Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise. Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications. The rapid deployment of sensing technology in smartphones and the explosion of their usage in people's daily lives provide users with the ability to collectively sense the world. This leads to a growing trend of mobile healthcare systems utilizing sensing data collected from smartphones with without additional external sensors to analyze and understand people's physical and mental states. However, such healthcare systems are vulnerable to user spoofing attacks, in which an adversary distributes his registered device to other users such that data collected from these users can be claimed as his own to obtain more healthcare benefits and undermine the successful operation of mobile healthcare systems. Existing mitigation approaches either only rely on a secret PIN number (which can not deal with colluded attacks) or require an explicit user action for verification. In this paper, we propose a user verification scheme leveraging unique gait patterns derived from acceleration readings in mobile healthcare systems to detect possible user spoofing attacks. Our framework exploits the readily available accelerometers embedded within smartphones for user verification. Specifically, our user spoofing attack mitigation scheme (which consists of three components, namely Step Cycle Identification, Step Cycle Interpolation, and Similarity Score Computation) is used to extract gait patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. Our experiments using 322 smartphone-based traces over a period of 6 months confirm that our scheme is highly effective for detecting user spoofing attacks. This strongly indicates the feasibility of using smartphone based low grade accelerometer to conduct gait recognition and facilitate effective user verification without active user cooperation. This paper presents an energy-aware method for recognizing time series acceleration data containing both activities and gestures using a wearable device coupled with a smartphone. In our method, we use a small wearable device to collect accelerometer data from a user's wrist, recognizing each data segment using a minimal feature set chosen automatically for that segment. For each collected data segment, if our model finds that recognizing the segment requires high-cost features that the wearable device cannot extract, such as dynamic time warping for gesture recognition, then the segment is transmitted to the smartphone where the high-cost features are extracted and recognition is performed. Otherwise, only the minimum required set of low-cost features are extracted from the segment on the wearable device and only the recognition result, i.e., label, is transmitted to the smartphone in place of the raw data, reducing transmission costs. Our method automatically constructs this adaptive processing pipeline solely from training data. Infrastructure monitoring applications currently lack a cost-effective and reliable solution for supporting the last communication hop for low-power devices. The use of cellular infrastructure requires contracts and complex radios that are often too power hungry and cost prohibitive for sensing applications that require just a few bits of data each day. New low-power, sub-GHz, long-range radios are an ideal technology to help fill this communication void by providing access points that are able to cover multiple kilometers of urban space with thousands of end-point devices. These new Low-Power Wide-Area Networking (LPWAN) platforms provide a cost-effective and highly deployable option that could piggyback off of existing public and private wireless networks (WiFi, Cellular, etc). In this paper, we present OpenChirp, a prototype end-to-end LPWAN architecture built using LoRa Wide-Area Network (LoRaWAN) with the goal of simplifying the design and deployment of Internet-of-Things (IoT) devices across wide areas like campuses and cities. We present a software architecture that exposes an application layer allowing users to register devices, describe transducer properties, transfer data and retrieve historical values. We define a service model on top of LoRaWAN that acts as a session layer to provide basic encoding and syntax to raw data streams. At the device-level, we introduce and benchmark an open-source hardware platform that uses Bluetooth Low-Energy (BLE) to help provision LoRa clients that can be extended with custom transducers. We evaluate the system in terms of end-node energy consumption, radio penetration into buildings as well as coverage provided by a network currently deployed at Carnegie Mellon University. Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning (Cohn, Ghahramani, and Jordan 1996) serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data. Vehicles today employ many different sensors to provide information such as tire pressure, proximity to objects and so on to Electronic Control Units for state maintenance and actuation functions. This has resulted in an increase in the number, weight and complexity of wiring as well as wiring harness for in-vehicle communication networks. Reducing the amount of cables will improve fuel efficiency; reduce cost of manufacturing and maintenance of vehicles greatly. Bluetooth Low Energy (BLE) is a great candidate for cable replacement given its supported data rates and widespread adoption in many markets. BLE device discovery is required to establish the presence of nodes within each other's communication range and begin reliable exchange of data. In our system, the nodes are the sensors and actuators. The fundamental problem in such device discovery is that energy consumption and discovery latency are inversely related. Therefore an efficient trade-off BLE connection setup that reduces energy-consumption while being able to provide worst-case discovery latency guarantees is critical especially in the automotive industry. We propose a mechanism that exploits Chinese Remainder Theorem (CRT) to provide worst-case latency guarantees during the connection setup phase of BLE devices. We provide the required modifications in BLE as well as provide evaluation results to show the improved performance. To the best of our knowledge this is the first work that considers providing bounded-latency connection setup for BLE devices using CRT and its application for automotive cable replacement. The continual advancement in semiconductor technology has enabled vendors to integrate an increasing number of sensors, e.g., accelerometer, gyroscope, digital compass, high resolution cameras and others, in modern mobile phones. Combining with the ever expanding market penetration of mobile phones, the onboard sensors are driving the sensing community towards a new paradigm called participatory sensing in which ordinary citizens voluntarily collect and share information from their local environment using personal mobile devices. The inherent openness of this platform is a double-edged sword. While encouraging user participation on the one hand, it also makes it easy to contribute corrupted data. As such, data trustworthiness becomes a key issue which needs to be addressed to ensure sustainable development of this emerging paradigm. In this paper, we propose a reputation system in which a reputation score is calculated for each device as a reflection of the trustworthiness of its sensor data. We adopt the Gompertz function as a fundamental building block of our system, since it is better suited to deal with the unique characteristics of participatory sensing. We present an application-agnostic implementation of the system, which can work with a wide variety of participatory sensing applications. We evaluate the performance of our reputation system in the context of two real-world participatory sensing applications. Our results show that the proposed reputation system outperforms existing solutions by up to a factor of six. Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, we show that DeepSense is feasible to implement on smartphones and embedded devices thanks to its moderate energy consumption and low latency. With gigabit networking becoming economically feasible and widely installed at homes, there are new opportunities to revisit in-home, personalized telehealth services. In this paper, we describe a novel telehealth eldercare service that we developed viz., "PhysicalTherapy-as-a-Service" (PTaaS) that connects a remote physical therapist at a clinic to a senior at home. The service leverages a high-speed, low-latency network connection through an interactive interface built on top of Microsoft Kinect motion sensing capabilities. The interface that is built using user-centered design principles for wellness coaching exercises is essentially a 'Synchronous Big Data' application due to its: (i) high data-in-motion velocity (i.e., peak data rate is ? 400 Mbps), (ii) considerable variety (i.e., measurements include 3D sensing, network health, user opinion surveys and video clips of RGB, skelet al and depth data), and (iii) large volume (i.e., several GB of measurement data for a simple exercise activity). The successful PTaaS delivery through this interface is dependent on the veracity analytics needed for correlation of the real-time Big Data streams within a session, in order to assess exercise balance of the senior without any bias due to network quality effects. Our experiments with PTaaS in an actual testbed involving senior homes in Kansas City with Google Fiber connections and our university clinic demonstrate the network configuration and time synchronization related challenges in order to perform online analytics. Our findings provide insights on how to: (a) enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and (b) realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner. Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9 using only a single triaxial accelerometer attached to the subject's chest. In the near future, all our everyday things and objects will be both connected to the Internet and equipped with enough sensing, acting and processing capabilities to exploit the full potential benefits of the so called Internet of Things (IoT) paradigm. Even the simplest objects will become smart because they will be interconnected to other objects to share and collect data from the environments in which they are placed thus paving the way to novel application services, computing and communication scenarios.In this context, interoperability among different standards and communication technologies is still a significant challenge that we have started to address by proposing a smartphone-based mobile gateway acting as a flexible and transparent interface between different IoT devices and the Internet. The presented unified, high-level and extendible software architecture supports opportunistic IoT devices discovery, control and management coupled with data processing, collection and diffusion functionalities.A specific testbed on common smartphones with different hardware and software capabilities was deployed to evaluate the real feasibility of the designed solution measuring the system performance in terms of energy consumption, memory and CPU usage in high and low load scenarios. According to the obtained results, the implemented software architecture for multi-standard and multi-technology interoperation presents a reduced use of hardware resources in front of a relatively high energy consumption value, mostly due to the simultaneously active radio interfaces combined with a small battery capacity, that limits the smartphone lifetime. Nevertheless, the presented general approach is still remarkable because this latter aspect will most likely be exceeded, in a short time, thanks to daily technological advancements in both batteries and radio interfaces. Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected. To enable precise detection of mental and physical states of users in a daily life, we have been developing an eyewear to measure eye and body movement in a unrestricted way. The horizontal and vertical EOG (electrooculogram) signals are measured and amplified with three met al dry electrodes placed near nasion and both sides of rhinion, of which positions correspond to the bridge and nose pads of eyewear, respectively. The user's mental states like drowsiness, sleepiness, fatigue, or interest to objects can be identified by the movements and blinking of the eyes extracted from the measured EOG. And the six-axis motion sensor (three-axis accelerometer and three-axis gyroscope) mounted in the eyewear measures the body motion. As the sensor located near the head is on the body axis, this eyewear is suitable to measure user's movement or shift of center of gravity during physical exercise with a high precision. The measured signals are used to extract various events of eye and body movement by the mounted microcontroller chip, or can be transmitted to the external devices via Bluetooth communication. This device can enable you to look into "yourself", as well as outer scenes. In this presentation, the outline of the eyewear is introduced and some possible applications are shown. The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper proposes, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various data mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture. This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.