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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Harris, Elsa Joy"],"institution":null,"degree_name":"Engineering and Computational Mathematics Joint PhD with California State University Long Beach, PhD","degree_level":"Open Access Dissertation","degree_discipline":"Institute of Mathematical Sciences","degree_department":null,"school":null,"contributors":["I-Hung Khoo","Marina Chugunova","Ali Nadim"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-01T08:00:00Z","date_published":"2024-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:49Z","subjects":["ctivity recognition","artificial intelligence","energy harvesting","human gait","smart insoles","triboelectric nanogenerator","Artificial Intelligence and Robotics","Biomedical Engineering and Bioengineering","Nanoscience and Nanotechnology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/852","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["I-Hung Khoo","Marina Chugunova","Ali Nadim"]},{"key":"dc:creator","label":"Author","values":["Harris, Elsa Joy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-03T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Institute of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Engineering and Computational Mathematics Joint PhD with California State University Long Beach, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ctivity recognition","artificial intelligence","energy harvesting","human gait","smart insoles","triboelectric nanogenerator","Artificial Intelligence and Robotics","Biomedical Engineering and Bioengineering","Nanoscience and Nanotechnology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/852"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["A Smart Energy-Efficient Hybrid Gait Monitoring System"]}]}],"canonical_facts":{"dc:contributor":["I-Hung Khoo","Marina Chugunova","Ali Nadim"],"dc:creator":["Harris, Elsa Joy"],"dc:date.available":["2024-10-03T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/852"],"dc:subject":["ctivity recognition","artificial intelligence","energy harvesting","human gait","smart insoles","triboelectric nanogenerator","Artificial Intelligence and Robotics","Biomedical Engineering and Bioengineering","Nanoscience and Nanotechnology"],"dc:title":["A Smart Energy-Efficient Hybrid Gait Monitoring System"],"thesis:degree_discipline":["Institute of Mathematical Sciences"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Engineering and Computational Mathematics Joint PhD with California State University Long Beach, PhD"]},"updated_at":"2026-07-24T01:40:49Z"}