Energy-efficient human activity recognition for self-powered wearable devices

Abstract

'I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.'

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

“…Human motion not only reflects current behavior, but it can also be converted into power. Some researchers such as Khalifa [13] and Lan et al [20] transform kinetic energy into mobile power. At the same time, they directly analyze the kinetic energy harvesting patterns to detect and analyze human activity.…”

“…Energy consumption is a known obstacle to wearable computing in general and to activity monitoring in particular [11][12][13][14][15]. For complex activities, however, recognition and monitoring may require an even greater energy footprint.…”