title: Wearable Networked Sensing for Human Mobility and Activity Analytics: A Systems Study.
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 in the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. 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.affiliation: Electrical and Computer Engineering, Michigan State University, East Lansing, MI.
authors.forename: Bo
authors.initials: B
authors.lastname: Dong
authors.affiliation: Electrical and Computer Engineering, Michigan State University, East Lansing, MI.
authors.forename: Subir
authors.initials: S
authors.lastname: Biswas
keywords: Activity Analytics; Machine Learning; Neural Network; On-body Processing; Wearable Sensor Network