UCLA

Human motion monitoring and activity classification, specifically in the free-living environment, are becoming increasingly important as preventative, diagnostic and rehabilitative measures in health and wellness applications.

In contrast to gait analysis, wearable sensor-based evaluation of upper body activities is not well studied. The work in this thesis tends to explore a novel system for upper limb activity monitoring and classification. The system focuses specifically on the application of motion classification to a complex task of automating rehabilitation evaluation, such as the Wolf Motor Function Test. The presented system consists of a novel wearable motion sensor platform that integrates accelerometers, gyroscopes and flex-sensors, and classification algorithms that convert motion data into an alphabet representation and form a string of primitives. String expressions are then derived for each test item and a regular expression based searching method is developed. We present results from the successful application of the proposed system to upper limb activity characterization in the context of the Wolf Motor Function Test.