Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power. <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> Abstract —Long-term monitoring of patients with affectivedisorders has been shown to reduce certain types of episodesand improve social functioning. However, most of the researchto date has been based on patient-reported data, which exhibitscertain deﬁciencies including failure to detect depressive aspectsof the illness. Questions have also been raised about the accuracyand completeness of patient-reported data. Enhancing patient-reported data with environmental and physiological recordingscould improve the accuracy and completeness that currentsolutions are missing. This paper describes procedures for thefusion of patient-reported data with remotely-sensed patient-centred data collected using small wireless devices placed onpatients and in their homes. I. I NTRODUCTION Bipolar disorder is a chronic mental illness that has asigniﬁcant impact on the individual and society. The diseasehas detrimental effects on patient quality of life [1] [2], and ahigh cost for social and health care 1 <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> This paper presents a networked proximity sensing and Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of body postures using a wearable sensor network. The idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a wearable sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naive threshold based mechanism, and its variations over different human subjects. <s> BIB004 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> This article describes an approach to detecting mental stress using unobtrusive wearable sensors. The approach relies on estimating the state of the autonomic nervous system from an analysis of heart rate variability. Namely, we use a non-linear system identification technique known as principal dynamic modes (PDM) to predict the activation level of the two autonomic branches: sympathetic (i.e. stress-inducing) and parasympathetic (i.e. relaxation-related). We validate the method on a discrimination problem with two psychophysiological conditions, one associated with mental tasks and one induced by relaxation exercises. Our results indicate that PDM features are more stable and less subject-dependent than spectral features, though the latter provide higher classification performance within subjects. When PDM and spectral features are combined, our system discriminates stressful events with a success rate of 83% within subjects (69% between subjects). <s> BIB005 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> This paper uses accelerometer-embedded mobile phones to monitor one's daily physical activities for sake of changing people's sedentary lifestyle. In contrast to the previous work of recognizing user's physical activities by using a single accelerometer-embedded device and placing it in a known position or fixed orientation, this paper intends to recognize the physical activities in the natural setting where the mobile phone's position and orientation are varying, depending on the position, material and size of the hosting pocket. By specifying 6 pocket positions, this paper develops a SVM based classifier to recognize 7 common physical activities. Based on 10-folder cross validation result on a 48.2 hour data set collected from 7 subjects, our solution outperforms Yang's solution and SHPF solution by 5-6%. By introducing an orientation insensitive sensor reading dimension, we boost the overall F-score from 91.5% to 93.1%. With known pocket position, the overall F-score increases to 94.8%. <s> BIB006 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> Body Area Networks integrated into mHealth systems are becoming a mature technology with unprecedented opportunities for personalized health monitoring and management. Potential applications include early detection of abnormal conditions, supervised rehabilitation, and wellness management. Such integrated mHealth systems can provide patients with increased confidence and a better quality of life, and promote healthy behavior and health awareness. Automatic integration of collected information and user's inputs into research databases can provide medical community with opportunity to search for personalized trends and group patterns, allowing insights into disease evolution, the rehabilitation process, and the effects of drug therapy. A new generation of personalized monitoring systems will allow users to customize their systems and user interfaces and to interact with their social networks. With emergence of first commercial body area network systems, a number of system design issues are still to be resolved, such as seamless integration of information and ad-hoc interaction with ambient sensors and other networks, to enable their wider acceptance. In this paper we present state of technology, discuss promising new trends, opportunities and challenges of body area networks for ubiquitous health monitoring applications. <s> BIB007 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> PURPOSE ::: Automated systems able to infer detailed measures of a person's social interactions and physical activities in their natural environments could lead to better understanding of factors influencing well-being. We assessed the feasibility of a wireless mobile device in measuring sociability and physical activity in older adults, and compared results with those of traditional questionnaires. ::: ::: ::: METHODS ::: This pilot observational study was conducted among a convenience sample of 8 men and women aged 65 years or older in a continuing care retirement community. Participants wore a waist-mounted device containing sensors that continuously capture data pertaining to behavior and environment (accelerometer, microphone, barometer, and sensors for temperature, humidity, and light). The sensors measured time spent walking level, up or down an elevation, and stationary (sitting or standing), and time spent speaking with 1 or more other people. The participants also completed 4 questionnaires: the 36-Item Short Form Health Survey (SF-36), the Yale Physical Activity Survey (YPAS), the Center for Epidemiologic Studies-Depression (CES-D) scale, and the Friendship Scale. ::: ::: ::: RESULTS ::: Men spent 21.3% of their time walking and 64.4% stationary. Women spent 20.7% of their time walking and 62.0% stationary. Sensed physical activity was correlated with aggregate YPAS scores (r(2)=0.79, P=.02). Sensed time speaking was positively correlated with the mental component score of the SF-36 (r(2)=0.86, P = .03), and social interaction as assessed with the Friendship Scale (r(2)=0.97, P = .002), and showed a trend toward association with CES-D score (r(2)=-0.75, P = .08). In adjusted models, sensed time speaking was associated with SF-36 mental component score (P = .08), social interaction measured with the Friendship Scale (P = .045), and CES-D score (P=.04). ::: ::: ::: CONCLUSIONS ::: Mobile sensing of sociability and activity is well correlated with traditional measures and less prone to biases associated with questionnaires that rely on recall. Using mobile devices to collect data from and monitor older adult patients has the potential to improve detection of changes in their health. <s> BIB008 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Human Activity Recognition <s> Context-aware applications have been the focus of extensive research yet their implementation in mobile devices usually becomes challenging due to restrictions in regards to processing power and energy. In this paper, we propose a mobile platform to provide real-time human activity recognition. Our system features (1) an efficient library, MECLA, for the mobile evaluation of classification algorithms; and (2) a mobile application for real-time human activity recognition running within a Body Area Network. The evaluation indicates that the system can be implemented in real scenarios meeting accuracy, response time, and energy consumption requirements. <s> BIB009
Recognizing human-centric activities and behaviors have been an important topic in pervasive mobile computing. Human Activity Recognition (HAR) intends to observe human related actions in order to obtain an understanding of what type of activities/routines that individuals perform within a time interval. By providing accurate information about HAR relevant data history could assist individuals on having better well-being, fitness level and situational-awareness BIB007 - BIB008 . For example, patients with diabetes, obesity, or heart disease are suggested to follow a predefined fitness program as a part of their treatment  , BIB003 . In this case, information corresponding to human postures (e.g., lying, sitting, standing, etc.) and movements (e.g., walking, running, etc.) can be inferred by a HAR system in order to provide useful feedbacks to the caregiver about a patient's behavior analysis. In addition, by the attachment of external sensor devices, e.g., Heart Rate (HR) monitor, patients with abnormal heart beats can be tracked easily and notified to caregivers in case of emergency in order to prevent undesirable BIB009 . In practical, HAR has only interest on single person activity detection; however, it can be extended to be multiple person recognition, which is called Activity of Daily Living (ADL). ADL is a way to describe functional status of a person, and his/her interaction with others. Hence, ADL becomes an essential part of community sensing especially for community health-care concerns (e.g., finding stress level in a group of people BIB005 ). Studies for HAR, as shown in Table VII , can be divided into sub-categories based on the platform that a context-aware system is built on: • A smartphone based: Activity recognition basically concerns about human beings and/or their surrounding environment. The constant monitoring of activity recognition was used to carry out by deployment of cameras with high cost or personal companion devices with no easy use. In addition, aggregation of monitored data was very complicated and impractical. However, since mobile devices are carried by people throughout the day, it makes them appear to be an ideal platform to be used in purpose of human-centric sensing. Especially, the accelerometer sensor, which can return a real-time measurement of acceleration through all coordinate spaces, is commonly used for HAR. It is employed either as a pedometer to measure steps counts and calorie consumption or as a monitor to recognize user physical activities such as postures and movements. Most measured events/actions/attributes are related to human posture or movement (e.g., using accelerometers or GPS/Wi-Fi/Cell Tower), environmental variables (e.g., using temperature and humidity sensors, microphone and cameras), or physiological signals (e.g., attachment of external devices such as heart rate or electrocardiogram, finger pulse, etc.). In this aspect, there are many studies BIB006 proposed to use smartphones to monitor users' daily physical activities according to their lifestyles. • Wearable sensors based: Wearable sensors, i.e., multiplesensor multiple-position solutions, have been put forward to recognize complex activities and gestures within the HAR concept. It basically introduces multiple-sensor placement on multiple location of human body to well capture some specific target activities (e.g., brushing teeth, arm and wrist movements while folding laundry, etc.) which a smartphone cannot detect by itself. With the use of wearable sensors, sensory context is extracted from miniature sensors integrated into garments, accessories, or straps. Especially, traditional accelerometer based HAR solutions cannot provide recognitions at finer granularities for differentiation of some postures such as sitting and lying down since there are some drawbacks observed such as mis-adjustment of device orientation and position or insufficient number of sensors to have enough spatial information. Hence, wearable sensors with utilization of heterogeneous sensor deployment has been an active research area to respond a growing demand for HAR systems in the health care domain, especially elder care support, assisting the cognitive disorders, and fitness/wellbeing management BIB004 , BIB002 - BIB001 . Heterogeneous sensors are connected to each other with wired/wireless communication (mostly Bluetooth). Smartphone can be used as a center position for external sensor attachments. Proximity sensors decide the distance between sensor nodes (i.e., topology of sensor placement) by measuring the received signal strength indication (RSSI) of radio frequency in dBm. On the other hand, the deployment of heterogeneous sensors entail high cost and brings about some constraints in computationally since it requires intensive supervised classification algorithms. These algorithms are mostly carried out in offline analysis, which also makes the solution impractical. The constraints may also stem from sensor degradation, interconnection failures, and jitter in the sensor placement. Hence, the reduction of sensor dimension is highly important for node interconnection, and make the system stay still unobtrusive. • An embedded platform based: In a HAR based system, higher classification accuracy is always desired. Especially, this implies a large number of sensor placements over the body in wearable sensors based applications. Variations in sensors and center device placements must let the system act robust to diverse feature extractions, and set specific classification models to make a context differentiation in any condition. Compared to the multiplesensor multiple-position solutions, creating a development platform consisting of multiple sensors could be a more practical way for HAR based applications since the attachment of sensors on specific body locations could return in similar reflection of feature signal characteristics on different activities, and it could lack of distinguishing diverse contexts. Therefore, like using smartphones, researchers have been studied mounting one-board based embedded hardware platforms with a predefined device position and orientation in order to investigate the effect of sensor placement on activity recognition performance, and make a better differentiation in context by fusing multiple sensor information with a priori experiment setups and unchanging conditions of training contextual data.