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Claremont Graduate University

A Smart Energy-Efficient Hybrid Gait Monitoring System

Abstract

dc:description.abstract

<p>Triboelectric nanogenerators are devices that harvest mechanical energy from the environment and turn it into electricity. By coupling the effect of contact electrification and electrostatic induction between two materials that come into contact and then separate they can convert the irregular, low frequency, waste biomechanical energy of human motion into useful electrical energy to run small body-worn electronics. This has shown promising results in multiple applications such as self-powered motion and haptic sensing, self-charging micro-storage devices, neuromorphic computing, and designing batteryless circuits to power small wearables. This work will investigate a smart energy-efficient hybrid gait monitoring system that is powered by triboelectric nanogenerators integrated into the shoe insoles and employs a machine learning algorithm to perform human activity recognition. Two scenarios were evaluated. First, where the triboelectric nanogenerators are used as the sensing unit, and the output voltage of the harvested energy serves as the gait signal for the machine learning algorithm. Second, where triboelectric nanogenerators are used as the power source for an Inertial Measurement Unit and microcontroller. The results will be important in industry 4.0 and competitive sports training applications, where long-term reliable and accurate monitoring is crucial to people’s health, safety, and performance, and renewable energy sources are desired over current commercial battery-powered systems. This research should aid the advancement of work in the field with the goal of commercial adoption of the technology.</p>

Degree

thesis:*
Name thesis:degree_name
Engineering and Computational Mathematics Joint PhD with California State University Long Beach, PhD
Level thesis:degree_level
Open Access Dissertation
Discipline thesis:degree_discipline
Institute of Mathematical Sciences
Year dc:date.available
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Harris, Elsa Joy
Contributors dc:contributor
  • I-Hung Khoo
  • Marina Chugunova
  • Ali Nadim

Subjects

dc:subject × 9

Identifiers

dc:identifier.*
Repository record dc:identifier
https://scholarship.claremont.edu/cgu_etd/852
OAI identifier oai:identifier
oai:scholarship.claremont.edu:cgu_etd-1875

Chain of custody

source
Harvested from
Claremont Graduate University
Base URL
scholarship.claremont.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Harris, Elsa Joy. A Smart Energy-Efficient Hybrid Gait Monitoring System. Open Access Dissertation thesis, 2024. https://scholarship.claremont.edu/cgu_etd/852