Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties
Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268050/ |
| _version_ | 1827587989608333312 |
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| author | Qi Luo Minglei Bai Shuhan Chen Kai Gao Lairong Yin Ronghua Du |
| author_facet | Qi Luo Minglei Bai Shuhan Chen Kai Gao Lairong Yin Ronghua Du |
| author_sort | Qi Luo |
| collection | DOAJ |
| description | Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a “press-without-break” task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands. |
| first_indexed | 2024-03-09T00:26:54Z |
| format | Article |
| id | doaj.art-03709eaefc39419090f530794fed6192 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-03-09T00:26:54Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-03709eaefc39419090f530794fed61922023-12-12T00:00:16ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-0112667510.1109/JTEHM.2023.332071510268050Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological PropertiesQi Luo0https://orcid.org/0009-0000-8290-5441Minglei Bai1Shuhan Chen2Kai Gao3https://orcid.org/0000-0003-4297-2978Lairong Yin4Ronghua Du5School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaProsthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a “press-without-break” task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.https://ieeexplore.ieee.org/document/10268050/Electromyography (EMG)prosthetic controlbiomimetic modelneuromorphic computationforce control |
| spellingShingle | Qi Luo Minglei Bai Shuhan Chen Kai Gao Lairong Yin Ronghua Du Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties IEEE Journal of Translational Engineering in Health and Medicine Electromyography (EMG) prosthetic control biomimetic model neuromorphic computation force control |
| title | Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties |
| title_full | Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties |
| title_fullStr | Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties |
| title_full_unstemmed | Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties |
| title_short | Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties |
| title_sort | enhancing force control of prosthetic controller for hand prosthesis by mimicking biological properties |
| topic | Electromyography (EMG) prosthetic control biomimetic model neuromorphic computation force control |
| url | https://ieeexplore.ieee.org/document/10268050/ |
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