SSA-LSTM-based locomotion mode recognition algorithm for the control of powered hip disarticulation prostheses
•Motion-pattern recognition for hip prostheses is investigated.•SSA-LSTM algorithm stabilises and refines single-hip-gait-pattern classification.•Dataset validation achieves over 99 % accuracy for healthy subjects and 96.4 % for hip amputees. Accurate recognition of locomotion modes is essential for...
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| Veröffentlicht in: | Biomedical signal processing and control Jg. 112; S. 108583 |
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| Sprache: | Englisch |
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01.02.2026
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| ISSN: | 1746-8094 |
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| Abstract | •Motion-pattern recognition for hip prostheses is investigated.•SSA-LSTM algorithm stabilises and refines single-hip-gait-pattern classification.•Dataset validation achieves over 99 % accuracy for healthy subjects and 96.4 % for hip amputees.
Accurate recognition of locomotion modes is essential for the effective control of lower limb prosthetics, enabling amputees to navigate various terrains with ease. Despite advancements, current prosthetics lack adaptive capabilities for complex movements, necessitating intelligent systems that can discern user intentions from sensory inputs.
This paper introduces the SSA-LSTM algorithm, which integrates the Sparrow Search Algorithm (SSA) with Long Short-Term Memory (LSTM) networks to enhance the stability and accuracy of motion pattern recognition in powered hip disarticulation prostheses.
A comprehensive dataset was constructed, capturing gait characteristics of both healthy individuals and amputees across various motion modes, including level walking, stair climbing, and ramp navigation. The SSA-LSTM algorithm optimizes the LSTM’s initial state, thereby improving convergence and learning efficiency. Its performance was bench-marked against established methods, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), ensemble learning, and LSTM.
The SSA-LSTM model achieved superior recognition accuracy, averaging over 99 % for healthy subjects and 96.4 % for hip disarticulation amputees. This model demonstrated faster convergence, underscoring the SSA’s role in enhancing the LSTM’s learning capabilities.
The SSA-LSTM model, through its integration of SSA optimization, represents a significant advancement in locomotion mode recognition. This research contributes to the development of intelligent prosthetics by providing a more precise and responsive control mechanism, which is crucial for enhancing the mobility and independence of amputees. |
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| AbstractList | •Motion-pattern recognition for hip prostheses is investigated.•SSA-LSTM algorithm stabilises and refines single-hip-gait-pattern classification.•Dataset validation achieves over 99 % accuracy for healthy subjects and 96.4 % for hip amputees.
Accurate recognition of locomotion modes is essential for the effective control of lower limb prosthetics, enabling amputees to navigate various terrains with ease. Despite advancements, current prosthetics lack adaptive capabilities for complex movements, necessitating intelligent systems that can discern user intentions from sensory inputs.
This paper introduces the SSA-LSTM algorithm, which integrates the Sparrow Search Algorithm (SSA) with Long Short-Term Memory (LSTM) networks to enhance the stability and accuracy of motion pattern recognition in powered hip disarticulation prostheses.
A comprehensive dataset was constructed, capturing gait characteristics of both healthy individuals and amputees across various motion modes, including level walking, stair climbing, and ramp navigation. The SSA-LSTM algorithm optimizes the LSTM’s initial state, thereby improving convergence and learning efficiency. Its performance was bench-marked against established methods, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), ensemble learning, and LSTM.
The SSA-LSTM model achieved superior recognition accuracy, averaging over 99 % for healthy subjects and 96.4 % for hip disarticulation amputees. This model demonstrated faster convergence, underscoring the SSA’s role in enhancing the LSTM’s learning capabilities.
The SSA-LSTM model, through its integration of SSA optimization, represents a significant advancement in locomotion mode recognition. This research contributes to the development of intelligent prosthetics by providing a more precise and responsive control mechanism, which is crucial for enhancing the mobility and independence of amputees. |
| ArticleNumber | 108583 |
| Author | Castelli, Vincenzo Parenti Sun, Zhenkun Yu, Hongliu Meng, Qiaoling Zhao, Jing |
| Author_xml | – sequence: 1 givenname: Qiaoling orcidid: 0000-0002-0240-4004 surname: Meng fullname: Meng, Qiaoling organization: Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China – sequence: 2 givenname: Zhenkun surname: Sun fullname: Sun, Zhenkun organization: Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China – sequence: 3 givenname: Jing surname: Zhao fullname: Zhao, Jing organization: Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China – sequence: 4 givenname: Vincenzo Parenti surname: Castelli fullname: Castelli, Vincenzo Parenti organization: University of Bologna, Emilia Romagna, Italy – sequence: 5 givenname: Hongliu orcidid: 0000-0002-2812-1192 surname: Yu fullname: Yu, Hongliu email: yhl_usst@outlook.com organization: Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China |
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| Cites_doi | 10.1109/TNSRE.2015.2412461 10.1109/TNSRE.2019.2909585 10.1109/JAS.2017.7510619 10.1109/MPOT.2016.2614756 10.1155/2021/5631730 10.1007/s00034-019-01116-y 10.1016/j.artmed.2024.102966 10.1088/1741-2560/11/5/056021 10.1007/s42835-020-00424-7 10.1016/j.mechatronics.2015.09.002 10.1109/LRA.2022.3183936 10.1080/21642583.2019.1708830 10.1162/neco.1997.9.8.1735 10.3390/s19204596 10.1016/j.compbiomed.2023.107124 10.1109/TASLP.2017.2769220 10.1109/TMECH.2014.2309708 10.1186/s12938-016-0284-9 10.1002/j.1538-7305.1984.tb00034.x |
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| Keywords | Sparrow search algorithm (SSA) Hip amputation Long short-term memory (LSTM) Locomotion mode recognition Lower limb prosthesis |
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| SubjectTerms | Hip amputation Locomotion mode recognition Long short-term memory (LSTM) Lower limb prosthesis Sparrow search algorithm (SSA) |
| Title | SSA-LSTM-based locomotion mode recognition algorithm for the control of powered hip disarticulation prostheses |
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