In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs
An in-socket sensory system enables the monitoring of transfemoral amputee movement for a microprocessor-controlled prosthetic leg. User movement recognition from an in-socket sensor allows a powered prosthetic leg to actively mimic healthy ambulation, thereby reducing an amputee's metabolic en...
| Prif Awduron: | , , , |
|---|---|
| Fformat: | Erthygl |
| Cyhoeddwyd: |
Society of Photo-optical Instrumentation Engineers
2018
|
| Pynciau: |
| _version_ | 1825722163148095488 |
|---|---|
| author | Mohd Yusof, Nur Hidayah Hamzaid, Nur Azah Jasni, Farahiyah Lai, Khin Wee |
| author_facet | Mohd Yusof, Nur Hidayah Hamzaid, Nur Azah Jasni, Farahiyah Lai, Khin Wee |
| author_sort | Mohd Yusof, Nur Hidayah |
| collection | UM |
| description | An in-socket sensory system enables the monitoring of transfemoral amputee movement for a microprocessor-controlled prosthetic leg. User movement recognition from an in-socket sensor allows a powered prosthetic leg to actively mimic healthy ambulation, thereby reducing an amputee's metabolic energy consumption. This study established an adaptive neurofuzzy inference system (ANFIS)-based control input framework from an in-socket sensor signal for gait phase classification to derive user intention as read by in-socket sensor arrays. Particular gait phase recognition was mapped with the cadence and torque control output of a knee joint actuator. The control input framework was validated with 30 experimental gait samples of the in-socket sensory signal of a transfemoral amputee walking at fluctuating speeds of 0 to 2 km · h- 1. The physical simulation of the controller presented a realistic simulation of the actuated knee joint in terms of a knee mechanism with 95% to 99% accuracy of knee cadence and 80% to 90% accuracy of torque compared with those of normal gait. The ANFIS system successfully detected the seven gait phases based on the amputee's in-socket sensor signals and assigned accurate knee joint torque and cadence values as output. © 2018 SPIE and IS&T. |
| first_indexed | 2024-03-06T06:01:27Z |
| format | Article |
| id | um.eprints-23982 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:01:27Z |
| publishDate | 2018 |
| publisher | Society of Photo-optical Instrumentation Engineers |
| record_format | dspace |
| spelling | um.eprints-239822020-03-10T04:15:12Z http://eprints.um.edu.my/23982/ In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs Mohd Yusof, Nur Hidayah Hamzaid, Nur Azah Jasni, Farahiyah Lai, Khin Wee R Medicine TA Engineering (General). Civil engineering (General) An in-socket sensory system enables the monitoring of transfemoral amputee movement for a microprocessor-controlled prosthetic leg. User movement recognition from an in-socket sensor allows a powered prosthetic leg to actively mimic healthy ambulation, thereby reducing an amputee's metabolic energy consumption. This study established an adaptive neurofuzzy inference system (ANFIS)-based control input framework from an in-socket sensor signal for gait phase classification to derive user intention as read by in-socket sensor arrays. Particular gait phase recognition was mapped with the cadence and torque control output of a knee joint actuator. The control input framework was validated with 30 experimental gait samples of the in-socket sensory signal of a transfemoral amputee walking at fluctuating speeds of 0 to 2 km · h- 1. The physical simulation of the controller presented a realistic simulation of the actuated knee joint in terms of a knee mechanism with 95% to 99% accuracy of knee cadence and 80% to 90% accuracy of torque compared with those of normal gait. The ANFIS system successfully detected the seven gait phases based on the amputee's in-socket sensor signals and assigned accurate knee joint torque and cadence values as output. © 2018 SPIE and IS&T. Society of Photo-optical Instrumentation Engineers 2018 Article PeerReviewed Mohd Yusof, Nur Hidayah and Hamzaid, Nur Azah and Jasni, Farahiyah and Lai, Khin Wee (2018) In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs. Journal of Electronic Imaging, 28 (2). 021002. ISSN 1017-9909, DOI https://doi.org/10.1117/1.JEI.28.2.021002 <https://doi.org/10.1117/1.JEI.28.2.021002>. https://doi.org/10.1117/1.JEI.28.2.021002 doi:10.1117/1.JEI.28.2.021002 |
| spellingShingle | R Medicine TA Engineering (General). Civil engineering (General) Mohd Yusof, Nur Hidayah Hamzaid, Nur Azah Jasni, Farahiyah Lai, Khin Wee In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs |
| title | In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs |
| title_full | In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs |
| title_fullStr | In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs |
| title_full_unstemmed | In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs |
| title_short | In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs |
| title_sort | in socket sensory system with an adaptive neuro based fuzzy inference system for active transfemoral prosthetic legs |
| topic | R Medicine TA Engineering (General). Civil engineering (General) |
| work_keys_str_mv | AT mohdyusofnurhidayah insocketsensorysystemwithanadaptiveneurobasedfuzzyinferencesystemforactivetransfemoralprostheticlegs AT hamzaidnurazah insocketsensorysystemwithanadaptiveneurobasedfuzzyinferencesystemforactivetransfemoralprostheticlegs AT jasnifarahiyah insocketsensorysystemwithanadaptiveneurobasedfuzzyinferencesystemforactivetransfemoralprostheticlegs AT laikhinwee insocketsensorysystemwithanadaptiveneurobasedfuzzyinferencesystemforactivetransfemoralprostheticlegs |