title: Energy-aware Activity Classification using Wearable Sensor Networks.
abstract: This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.
authors: Department of Electrical and Computer Engineering, Michigan State University.
authors: Bo
authors: B
authors: Dong
authors: Department of Kinesiology, Michigan State University.
authors: Alexander
authors: A
authors: Montoye
authors: Department of Kinesiology, Michigan State University.
authors: Rebecca
authors: R
authors: Moore
authors: Department of Kinesiology, Michigan State University.
authors: Karin
authors: K
authors: Pfeiffer
authors: Department of Electrical and Computer Engineering, Michigan State University.
authors: Subir
authors: S
authors: Biswas
keywords: Activity Analytics; Machine Learning; Neural Network; On-body Processing; Wearable Sensor Network