Subject-dependent Physical Activity Recognition Model Framework With A Semi-supervised Clustering Approach

Activity recognition systems have been found to be very effective for tracking users' activities in research areas like healthcare and assisted living. Wearable accelerometers that can help in classifying Physical Activities (PA) have been made available by MEMS technology. State-of-the-art PA-classification systems use threshold-based techniques and Machine Learning (ML) algorithms. Each PA may exhibit inter-subject and intra-subject variability which is a major drawback for threshold and machine learning based techniques. Due to lack of empirical data in order to train classifier for ML clustering algorithms, there is a need to develop a mechanism which requires less training data for PA clustering. This paper describes a novel personalized PA recognition model framework based on a semi-supervised clustering approach to avoid fixed threshold techniques and traditional clustering methods by using a single accelerometer. The proposed methodology requires limited amount of data to compute (initial) centroids for PA clusters and achieved an accuracy of about 93% on average, moreover it has the potential capability of recognizing subjects' behavioral shifts and exceptional events, falls, etc.