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Abstract:-

Project Code:  

SA1403

Published Year:  

2014

Context Driven Prescription Based Personal Activity Classification Methodology Architecture and End to End Implementation

Descripition:

Enabling large-scale monitoring and classification of a range of motion activities is of primary importance due to the need by healthcare and fitness professionals to monitor exercises for quality and compliance. Past work has not fully addressed the unique challenges that arise from scaling. This paper presents a novel end-to-end system solution to some of these challenges. The system is built on the prescription-based context-driven activity classification methodology. First, we show that by refining the definition of context, and introducing the concept of scenarios, a prescription model can provide personalized activity monitoring. Second, through a flexible architecture constructed from interface models, we demonstrate the concept of a context-driven classifier. Context classification is achieved through a classification committee approach, and activity classification follows by means of context specific activity models. Then, the architecture is implemented in an end-to-end system featuring an Android application running on a mobile device, and a number of classifiers as core classification components. Finally, we use a series of experimental field evaluations to confirm the expected benefits of the proposed system in terms of classification accuracy, rate, and sensor operating life.

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Context Driven Prescription Based Personal Activity Classification Methodology Architecture and End to End Implementation
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