Context guided and personalized activity classification system

Abstract

Continued rapid progress in the development of embedded motion sensing enables wearable devices that provide fundamental advances in the capability to monitor and classify human motion, detect movement disorders, and estimate energy expenditure. With this progress, it is becoming possible to provide, for the first time, evaluation of outcomes of rehabilitation interventions and direct guidance for advancement of subject health, wellness, and safety. The progress in motion classification relies on both the performance of new sensor fusion methods that provide inference, and the energy efficiency of energy-constrained monitoring sensors. As will be described here, both of these objectives require advances in the capability of detecting and classifying the location and environmental context. Context directly enables both enhanced motion classification accuracy and speed through reduction in search space, and reduced energy demand through context-aware optimization of sensor sampling and operation schedules. There have been attempts to introduce context awareness into activity monitoring with limited success, due to the ambiguity in the definition of context, and the lack of a system architecture that enables the adaptation of signal processing and sensor fusion algorithms specific to the task of personalized activity monitoring. In this paper we present a novel end-to-end system that provides context guided personalized activity classification. With a refined concept of context, the system introduces interface models that feature a context classification committee, the concept of context specific activity classification, the ability to manage sensors given context, and the ability to operate in real time through web services. We also present an implementation that demonstrates accurate context classification, accurate activity classification using context specific models with improved accuracy and speed, and extended operating life through sensor energy management.