NDLI: Opportunistic hierarchical classification for power optimization in wearable movement monitoring systems

Content Provider	IEEE Xplore Digital Library
Author	Fraternali, F. Rofouei, M. Alshurafa, N. Ghasemzadeh, H. Benini, L. Sarrafzadeh, M.
Copyright Year	2012
Description	Author affiliation: Department of Electronic Informatic and System, University of Bologna, Italy (Fraternali, F.; Benini, L.) \|\| Department of Computer Science, University of California, Los Angeles, CA, USA (Rofouei, M.; Alshurafa, N.; Ghasemzadeh, H.; Sarrafzadeh, M.)
Abstract	Patient monitoring systems are becoming increasingly important in accurately diagnosing and treating growing worldwide chronic conditions especially the obesity epidemic. The ubiquitous nature of wearable sensors, such as the readily available embedded accelerometers in smart phones, provides physicians with an opportunity to remotely monitor their patient's daily activity. There have been several developments in the area of activity recognition using wearable sensors. However, due to power constraints, resource efficient algorithms are necessary in order to perform accurate realtime activity recognition while consuming minimal energy. In this paper, we present a two-tier architecture for optimizing power consumption in such systems. While the first tier relies on a hierarchical classification approach, the second one manages the activation and deactivation of the classification system. We demonstrate this using a series of binary Support Vector Machine classifiers. The proposed approach, however, is classifier independent. Experimenting with subjects performing different daily activities such as walking, going upstairs and down-stairs, standing and sitting, our approach achieves a power savings of 87%, while maintaining 92% classification accuracy.
Starting Page	102
Ending Page	111
File Size	1727503
Page Count	10
File Format	PDF
ISBN	9781467326858
e-ISBN	9781467326841
e-ISBN	9781467326834
DOI	10.1109/SIES.2012.6356575
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-20
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Support vector machines Accelerometers Accuracy Monitoring Classification algorithms Power demand Power Optimization Mobile Phone Accelerometer Support Vector Machines Hierarchical Classifier Activity Monitoring
Content Type	Text
Resource Type	Article

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