Energy-efficient Human Activity Recognition for Self-powered Wearable Devices

dc.contributor.advisor Hassan, Mahbub en_US
dc.contributor.advisor Seneviratne, Aruna en_US Khalifa, Sara en_US 2022-03-22T11:59:53Z 2022-03-22T11:59:53Z 2016 en_US
dc.description.abstract Advances in energy harvesting hardware have created an opportunity to realise self-powered wearables for continuous and pervasive Human Activity Recognition (HAR). Unfortunately, the power requirements of continuous activity sensing using accelerometer sensors and burdensome on-node classification are relatively high compared to the amount of power that can be practically harvested, which limits the usefulness of energy harvesting. This thesis makes three fundamental contributions. First, we propose HARKE, Human Activity Recognition from Kinetic Energy, a novel approach to HAR that does not use an accelerometer. Instead, HARKE employs and infers human physical activities directly from the Kinetic Energy Harvesting (KEH) patterns generated from a device that harvests kinetic energy to power the wearable device. We also show the ability of HARKE to detect related details such as the steps taken by the user in a walking activity. By not using an accelerometer, a significant percentage of the limited harvested energy can be saved. Second, we introduce a novel framework that reduces the on-node classification overhead and guarantees energy neutrality. The proposed framework transmits an unmodulated signal, called an activity pulse, and uses only the received signal strength of the activity pulse to classify human activities. Neither accelerometer nor classifier is required on the wearable device, which therefore, guarantees energy neutrality. Finally, we validate the feasibility of using KEH patterns generated from human speech as a potential new source of information for detecting hotwords, such as ``OK Google", which are used by voice control applications to differentiate user commands from background conversations. Unlike methods that use existing sensors like microphones or accelerometers, our proposal enables pervasive voice control and HAR at a minimum energy cost. We believe that the findings in this thesis will open the door for a new direction of research and development to realise the vision of pervasive self-powered HAR, moving us closer towards self-powered autonomous wearables. en_US
dc.language English
dc.language.iso EN en_US
dc.publisher UNSW, Sydney en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.subject.other Self-powered Wearable Devices en_US
dc.subject.other Human Activity Recognition en_US
dc.subject.other Energy Harvesting en_US
dc.subject.other Energy Efficiency en_US
dc.title Energy-efficient Human Activity Recognition for Self-powered Wearable Devices en_US
dc.type Thesis en_US
dcterms.accessRights open access
dcterms.rightsHolder Khalifa, Sara
dspace.entity.type Publication en_US
unsw.relation.faculty Engineering
unsw.relation.originalPublicationAffiliation Khalifa, Sara, Computer Science & Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.originalPublicationAffiliation Hassan, Mahbub, Computer Science & Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.originalPublicationAffiliation Seneviratne, Aruna, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW en_US School of Computer Science and Engineering *
unsw.thesis.degreetype PhD Doctorate en_US
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