Department of Electrical Engineering

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    A 3D Accelerometer Based Human Activity Classification
    (COMSATS University Islamabad Lahore Campus, 2018) Aftab Paul,; FA16-REE-009; Contributor(s): Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]; LHR TP 7475
    Recently, Automated Human Activity Recognition has been extensively used for long term health monitoring of healthy individuals and to provide assisted living for the elderly. Long-term health monitoring systems have been successfully implemented for the prevention of chronic diseases like heart disease, obesity, workers syndrome, and other diseases related to sedentary lifestyle. Activities of Daily Living such as sitting, standing, walking, working in office, jogging, and running etc. can be efficiently classified using sensors such as 3D accelerometers, gyroscopes, and magnetometers. Such systems have demonstrated very high classification accuracies for activities performed for longer durations of time. However, these systems are unable to detect and classify transitory activities where the subject switches from one basic activity to another. For example, if a subject stands up from the chair to walk out of the room and then goes downstairs to reach ground floor of the office building has transitions from stationary sitting to standing and then walking, walking to going downstairs and then walking again and these transitions may not be classified correctly by existing automated human activity recognition systems since the models are trained using nonrealtime segmented data for each individual activity class. This research aims to develop a system for the detection of transitory activities. A Mobile phone-based accelerometer is used to record these activities from the chest of subject through MyNeuroHealth application. Data is collected, pre-processed, and classified into different activity classes. This data is used to train Artificial Neural Network to classify transitory activities. The proposed system achieved an accuracy of more than 50% with real-time data. Furthermore, it is also observed that using two accelerometers for collecting the movement data can enhance classification accuracy to 65%. Given that little or no work has been done in this dimension of HAR, this research may be extended to improve the accuracy of HAR for real-time automated long term health monitoring systems. X