AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor.
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Sensors (Basel), 2024, 24, (22), pp. 7370
- Issue Date:
- 2024-11-19
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Full metadata record
| Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hasan, S | |
| dc.contributor.author | D'auria, BG | |
| dc.contributor.author | Mahmud, MAP | |
| dc.contributor.author | Adams, SD | |
| dc.contributor.author | Long, JM | |
| dc.contributor.author | Kong, L | |
| dc.contributor.author | Kouzani, AZ | |
| dc.date.accessioned | 2024-12-16T00:43:15Z | |
| dc.date.available | 2024-11-14 | |
| dc.date.available | 2024-12-16T00:43:15Z | |
| dc.date.issued | 2024-11-19 | |
| dc.identifier.citation | Sensors (Basel), 2024, 24, (22), pp. 7370 | |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.uri | http://hdl.handle.net/10453/182544 | |
| dc.description.abstract | Wearable devices have revolutionized real-time health monitoring, yet challenges persist in enhancing their flexibility, weight, and accuracy. This paper presents the development of a wearable device employing a conductive polyacrylamide-lithium chloride-MXene (PLM) hydrogel sensor, an electronic circuit, and artificial intelligence (AI) for gait monitoring. The PLM sensor includes tribo-negative polydimethylsiloxane (PDMS) and tribo-positive polyurethane (PU) layers, exhibiting extraordinary stretchability (317% strain) and durability (1000 cycles) while consistently delivering stable electrical signals. The wearable device weighs just 23 g and is strategically affixed to a knee brace, harnessing mechanical energy generated during knee motion which is converted into electrical signals. These signals are digitized and then analyzed using a one-dimensional (1D) convolutional neural network (CNN), achieving an impressive accuracy of 100% for the classification of four distinct gait patterns: standing, walking, jogging, and running. The wearable device demonstrates the potential for lightweight and energy-efficient sensing combined with AI analysis for advanced biomechanical monitoring in sports and healthcare applications. | |
| dc.format | Electronic | |
| dc.language | eng | |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Sensors (Basel) | |
| dc.relation.isbasedon | 10.3390/s24227370 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering | |
| dc.subject.classification | Analytical Chemistry | |
| dc.subject.classification | 3103 Ecology | |
| dc.subject.classification | 4008 Electrical engineering | |
| dc.subject.classification | 4009 Electronics, sensors and digital hardware | |
| dc.subject.classification | 4104 Environmental management | |
| dc.subject.classification | 4606 Distributed computing and systems software | |
| dc.subject.mesh | Wearable Electronic Devices | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Hydrogels | |
| dc.subject.mesh | Gait Analysis | |
| dc.subject.mesh | Artificial Intelligence | |
| dc.subject.mesh | Neural Networks, Computer | |
| dc.subject.mesh | Gait | |
| dc.subject.mesh | Dimethylpolysiloxanes | |
| dc.subject.mesh | Polyurethanes | |
| dc.subject.mesh | Acrylic Resins | |
| dc.subject.mesh | Biosensing Techniques | |
| dc.subject.mesh | Monitoring, Physiologic | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Polyurethanes | |
| dc.subject.mesh | Dimethylpolysiloxanes | |
| dc.subject.mesh | Acrylic Resins | |
| dc.subject.mesh | Hydrogels | |
| dc.subject.mesh | Monitoring, Physiologic | |
| dc.subject.mesh | Gait | |
| dc.subject.mesh | Biosensing Techniques | |
| dc.subject.mesh | Artificial Intelligence | |
| dc.subject.mesh | Wearable Electronic Devices | |
| dc.subject.mesh | Gait Analysis | |
| dc.subject.mesh | Neural Networks, Computer | |
| dc.title | AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor. | |
| dc.type | Journal Article | |
| utslib.citation.volume | 24 | |
| utslib.location.activity | Switzerland | |
| utslib.for | 0301 Analytical Chemistry | |
| utslib.for | 0502 Environmental Science and Management | |
| utslib.for | 0602 Ecology | |
| utslib.for | 0805 Distributed Computing | |
| utslib.for | 0906 Electrical and Electronic Engineering | |
| pubs.organisational-group | University of Technology Sydney | |
| pubs.organisational-group | University of Technology Sydney/Faculty of Science | |
| pubs.organisational-group | University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences | |
| utslib.copyright.status | open_access | * |
| dc.rights.license | This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ | |
| dc.date.updated | 2024-12-16T00:43:08Z | |
| pubs.issue | 22 | |
| pubs.publication-status | Published online | |
| pubs.volume | 24 | |
| utslib.citation.issue | 22 |
Abstract:
Wearable devices have revolutionized real-time health monitoring, yet challenges persist in enhancing their flexibility, weight, and accuracy. This paper presents the development of a wearable device employing a conductive polyacrylamide-lithium chloride-MXene (PLM) hydrogel sensor, an electronic circuit, and artificial intelligence (AI) for gait monitoring. The PLM sensor includes tribo-negative polydimethylsiloxane (PDMS) and tribo-positive polyurethane (PU) layers, exhibiting extraordinary stretchability (317% strain) and durability (1000 cycles) while consistently delivering stable electrical signals. The wearable device weighs just 23 g and is strategically affixed to a knee brace, harnessing mechanical energy generated during knee motion which is converted into electrical signals. These signals are digitized and then analyzed using a one-dimensional (1D) convolutional neural network (CNN), achieving an impressive accuracy of 100% for the classification of four distinct gait patterns: standing, walking, jogging, and running. The wearable device demonstrates the potential for lightweight and energy-efficient sensing combined with AI analysis for advanced biomechanical monitoring in sports and healthcare applications.
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