Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output
Accurately identifying user needs in terms of assist timing and magnitude presents challenges for wearable power‐assist limb devices. Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400278 |
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| _version_ | 1832592558213038080 |
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| author | Junyi Shen Tetsuro Miyazaki Swaninda Ghosh Toshihiro Kawase Kenji Kawashima |
| author_facet | Junyi Shen Tetsuro Miyazaki Swaninda Ghosh Toshihiro Kawase Kenji Kawashima |
| author_sort | Junyi Shen |
| collection | DOAJ |
| description | Accurately identifying user needs in terms of assist timing and magnitude presents challenges for wearable power‐assist limb devices. Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compromise wearability and impose high computational demands. Physical reservoir computing (PRC), which utilizes the inherent nonlinearity of physical systems for data processing, offers a promising alternative. This study proposes a novel self‐estimated physical reservoir computing (SEPRC) model that improves traditional PRC models for gait perception using a wearable pneumatic physical reservoir. A core feature of the new model is the self‐estimation structure, wherein the outputs of the physical reservoir are mutually estimated. Experimental evaluations indicate that the SEPRC model outperforms traditional PRC in clustering time‐series reservoir output sequences with the same dimensionality. This enhanced clustering performance is subsequently leveraged in gait perception by incorporating Takagi–Sugeno fuzzy logic for joint angle estimation and a softmax activation function for walking condition recognition. The newly proposed time‐sequence processing approach facilitates the traditional PRC model to achieve higher accuracy in gait perception and greater robustness against the user's walking pattern variations while preserving PRC's hardware simplicity. |
| format | Article |
| id | doaj-art-3371b170e7e3497293024b5aa1cdfef9 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-3371b170e7e3497293024b5aa1cdfef92025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400278Gait Perception via Actual and Estimated Pneumatic Physical Reservoir OutputJunyi Shen0Tetsuro Miyazaki1Swaninda Ghosh2Toshihiro Kawase3Kenji Kawashima4Department of Information Physics and Computing The University of Tokyo Tokyo 113‐8654 JapanDepartment of Information Physics and Computing The University of Tokyo Tokyo 113‐8654 JapanDepartment of Information Physics and Computing The University of Tokyo Tokyo 113‐8654 JapanSchool of Engineering Department of Information and Communication Engineering Tokyo Denki University Tokyo 120‐8551 JapanDepartment of Information Physics and Computing The University of Tokyo Tokyo 113‐8654 JapanAccurately identifying user needs in terms of assist timing and magnitude presents challenges for wearable power‐assist limb devices. Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compromise wearability and impose high computational demands. Physical reservoir computing (PRC), which utilizes the inherent nonlinearity of physical systems for data processing, offers a promising alternative. This study proposes a novel self‐estimated physical reservoir computing (SEPRC) model that improves traditional PRC models for gait perception using a wearable pneumatic physical reservoir. A core feature of the new model is the self‐estimation structure, wherein the outputs of the physical reservoir are mutually estimated. Experimental evaluations indicate that the SEPRC model outperforms traditional PRC in clustering time‐series reservoir output sequences with the same dimensionality. This enhanced clustering performance is subsequently leveraged in gait perception by incorporating Takagi–Sugeno fuzzy logic for joint angle estimation and a softmax activation function for walking condition recognition. The newly proposed time‐sequence processing approach facilitates the traditional PRC model to achieve higher accuracy in gait perception and greater robustness against the user's walking pattern variations while preserving PRC's hardware simplicity.https://doi.org/10.1002/aisy.202400278fuzzy systemgait perceptionphysical reservoir computingpneumatic reservoirsoft roboticswearable robotics |
| spellingShingle | Junyi Shen Tetsuro Miyazaki Swaninda Ghosh Toshihiro Kawase Kenji Kawashima Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output Advanced Intelligent Systems fuzzy system gait perception physical reservoir computing pneumatic reservoir soft robotics wearable robotics |
| title | Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output |
| title_full | Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output |
| title_fullStr | Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output |
| title_full_unstemmed | Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output |
| title_short | Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output |
| title_sort | gait perception via actual and estimated pneumatic physical reservoir output |
| topic | fuzzy system gait perception physical reservoir computing pneumatic reservoir soft robotics wearable robotics |
| url | https://doi.org/10.1002/aisy.202400278 |
| work_keys_str_mv | AT junyishen gaitperceptionviaactualandestimatedpneumaticphysicalreservoiroutput AT tetsuromiyazaki gaitperceptionviaactualandestimatedpneumaticphysicalreservoiroutput AT swanindaghosh gaitperceptionviaactualandestimatedpneumaticphysicalreservoiroutput AT toshihirokawase gaitperceptionviaactualandestimatedpneumaticphysicalreservoiroutput AT kenjikawashima gaitperceptionviaactualandestimatedpneumaticphysicalreservoiroutput |