NDLI: Context guided and personalized activity classification system

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Context guided and personalized activity classification system

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Context guided and personalized activity classification system

Content Provider	ACM Digital Library
Author	Wang, Zhao Xu, James Y. Pottie, Greg J. Sun, Yuwen Kaiser, William J.
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.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450309820
DOI	10.1145/2077546.2077559
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	System architecture Activity classification Real time classification Context guided Context specific classification
Content Type	Text
Resource Type	Article

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