Research On Key Technologies Of Human Activity Monitoring And Recognition In Wireless Body Sensor Networks

A Wireless Body Area/Sensor Network (WBAN/WBSN) is a kind of wireless network which is formed by physiological parameters sensors placed in the human body, on the body surface or around the body. As a pratical and innovative approach to improve health care and the quality of life, WBSN has been in great demand in remote monitoring of the physically or mentally disabled, the elderly, and children, medical diagnosis and treatment, physical rehabilitation and therapy. WBANs with inertial-based wearable sensors can collect motion signal of human body and have a broad range of applications in activity recognition, fall detection, estimation of energy expenditure, gait analysis and sports training.To improve the energy efficiency in meeting physical activity monitoring requirements is an important challenge for the practical deployment of WBSN in continuous long time of health monitoring. In this thesis, the WBSN with several wearable inertial sensors is taken as the research object, The energy efficiency, as the main concern of this thesis, is studied via the sparse representation and compressed sensing theory from4different aspects, whichi contain signal classification and signal compression, data confution, power control. The main contributions of the thesis are summarized below:(1) The L-SRC approach is proposed for accelerometer-based hand gesture recognition based on self-learning sparse representation. Linear interpolation is used on the acceleration signals to solve the problem of different lengths of traces for the same gesture so that the task of gesture recognition is casted as one of classifying among multiple linear regression models via sparse representation. Class-specific dictionaries learning is adopted and produced in an off-line procedure. The original sparse representation classification can be reduced from a large dictionary of all training samples to a small-sized one for sparse coding after dictionary learning, thereby obtaining significant speed-up. Experiments on the database of18hand gestures validate the performance of the proposed algorithm. The results show the L-SRC achieves high recognition accuracy while reducing the computing cost and time of recognition.(2)The RP-CCall and RP-CCeach mehods of compressed classification are presented for activity recognition. In view of the time redundancy and sparsity of the motion signal, these two approaches combine data classification with compression based on sparse representation and compressed sensing to reduce the energy consumption while maintaining the sufficient recognition accuracy of activity. The two approaches firstly compress the sensing data by random projection on the sensor nodes, and then recognize activities on compressed samples after transmitting to the central node by sparse representation, which can result in reduction of the energy consumption for data transmission. The performances of the two methods are evaluated on the Wearable Action Recognition Database (WARD) using inertial sensors placed on various locations on a human body. Experimental results show that the compressed classifiers achieve comparable recognition accuracies on the compressed sensing data. The recognition accuracies of our approaches are higer than the other classifiers such as the nearest nerbour and the support vector machine.(3)The DCS-JSRC approach is proposed, which explores a novel joint sparse representation method for activity classification of multi-sensor fusion. This approach utilizes the theory of distributed compressed sensing and joint sparse representation to develop a simultaneous dimension reduction and classification approach for multi-sensor activity recognition in BSNs. Both temporal and spatial correlations of sensing data among the multiple sensors are exploited for the purpose of compression and classification. Activity recognition with multiple sensors is formulated as a multi-task joint sparse representation model to combine the strength of multiple sensors for improving the classification accuracy. A hierarchical Bayesian modeling is used for simultaneous sparse approximation of multiple related signals. This method is validated on the WARD dataset. Experimental result shows that the DRP-JSRC achieves better classification performance than the RP-CCall and RP-CCeach·(4) The PID-A mechanism for dynamic power control of WBSN is proposed, which can adapt transmit power in real-time based on feedback information in order to simultaneously satisfy the requirements of energy efficiency and link reliability. The properties of the link states by using the recived signal strength indicator under different scenarios with body posture change and dynamic body motions are first experimentally investigated. The empirical evidence of real human environment shows the rapid change of wireless link quality in WBSN. The dynamic nature of on-body links with varing body postures is characterized based on experimental results. The PID-A mechianism utilizes the feedback results of activity recognition and RSSI to adjust the optimal transmission power assignments. The performance of the PID-A is experimentally evaluated and compared with a number of static power assignment schemes. Experimental results show that the PID-A can gain higher packet delivery rate and reduce the energy consumption of a packet at the same time.(5) A prototype of WBAN is designed and developed for verifying the performance of our proposed approaches. The real gesture and activity data were collected by using our prototype platform. The proposed mehods are verified on real dataset and the recognition accuracies of our methods are derived. Energy consumption is measured and analsized based on the energy profile of node platform and the energy efficiency of our proposed approaches is demonstrated.