Monitoring mobility disorders at home using 3D visual sensors and mobile sensors
Proceedings of the 4th Conference on Wireless Health
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We address the problem of automated quantitative evaluation of musculo-skeletal disorders using a 3D sensor. This enables a non-invasive home monitoring system which extracts and analyzes the subject's motion symptoms and provides clinical feedback. The subject is asked to perform several clinically validated standardized tests (e.g. sit-to-stand, repeated several times) in front of a 3D sensor to generate a sequence of skeletons (i.e. locations of 3D joints). While the complete sequence consists of multiple repeated Skeletal Action Units (SAU) (e.g. sit-to-stand, one repetition), we generate a single robust Representative Skeletal Action Unit (RSAU) which encodes the subject's most consistent spatio-temporal motion pattern. Based on the Representative Skeletal Action Unit (RSAU) we extract a series of clinical measurements (e.g. step size, swing level of hand) which are crucial for prescription and rehabilitation plan design. In this paper, we propose a Temporal Alignment Spatial Summarization (TASS) method to decouple the complex spatio-temporal information of multiple Skeletal Action Units (SAU). Experimental results from people with Parkinson's Disease (PD) and people without Parkinson's Disease (non-PD) demonstrate the effectiveness of our methodology which opens the way for many related applications.