DataVXAdvanced MaterialsPaper
journal article·2025-03

Bioinspired Smart Triboelectric Soft Pneumatic Actuator‐Enabled Hand Rehabilitation Robot

Wei Li, Feiling Luo, Yuan Liu, Yongxiang Zou, Linhong Mo, Qiguang He, Ping‐Ju Lin, Quan Xu, Aixian Liu, Chi Zhang, Jia Cheng, Long Cheng, Linhong Ji

Advanced Materials· Volume 37· Issue 9
Publisher page
Source abstract

Abstract

Abstract Quantitative assessment for post‐stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment depending heavily on rehabilitation physicians. To address these challenges, a high‐force‐output triboelectric soft pneumatic actuator (TENG‐SPA) inspired by a lobster tail is developed. The bioinspired TENG‐SPA can generate approximately 20 N at 0.1 MPa, providing sufficient stretching force for spastic fingers. The anti‐interference, durability, and electrical output characteristics of the TENG‐SPA under varying conditions—such as different air pressures, bending frequencies, and simulated spastic finger stretching—are explored, demonstrating TENG‐SPA's ability to sense resistance during the stretching process. Furthermore, a TENG‐SPA‐enabled hand rehabilitation robot system integrated with the convolutional neural network (CNN) is further developed, which is tested in a clinical trial involving 15 stroke patients. The results have demonstrated that a classification accuracy for the levels of finger spasticity reaches 93.3% and the MAS scores predicted by the CNN regression model exhibit a strong linear relationship with the actual MAS ( R 2 = 0.8451, p < 0.01). This study presents promising potential applications in digital rehabilitation medicine, human–machine interaction, biomedicine, and related fields.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Botulinum Toxin and Related Neurological Disorders · Advanced Sensor and Energy Harvesting Materials · Muscle activation and electromyography studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Division of Intelligent and Biomechanical Systems State Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Haidian Beijing 100084 China

Beijing Key Laboratory of Micro‐nano Energy and Sensor Center for High‐Entropy Energy and Systems Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 China

School of Electromechanical and Automotive Engineering YanTai University Shandong 264005 China

Neurological Rehabilitation Center Beijing Rehabilitation Hospital Affiliated to Capital Medical University Beijing 100144 China

Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong 999077 China

Department of Rehabilitation Medicine Beijing Tsinghua Changgung Hospital School of Clinical Medicine Tsinghua University Beijing 102218 China

School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China

Funding

National Natural Science Foundation of China · National Key Research and Development Program of China · Beijing Municipal Natural Science Foundation · Beijing Nova Program

References (43)

Show source-provided references
1. 10.1212/WNL.000000000000896410.1212/WNL.0000000000008964 ↗
2. 10.1186/s12984-019-0612-y10.1186/s12984-019-0612-y ↗
3. 10.1186/s12984-021-00937-w10.1186/s12984-021-00937-w ↗
4. 10.3389/fneur.2020.61609710.3389/fneur.2020.616097 ↗
5. 10.1186/s12984-019-0559-z10.1186/s12984-019-0559-z ↗
6. 10.3389/fneur.2023.112132310.3389/fneur.2023.1121323 ↗
7. 10.1016/j.robot.2014.08.01410.1016/j.robot.2014.08.014 ↗
8. 10.1089/soro.2020.009010.1089/soro.2020.0090 ↗
9. 10.1109/TBME.2020.298400310.1109/TBME.2020.2984003 ↗
10. 10.1186/s12984-018-0350-610.1186/s12984-018-0350-6 ↗
11. 10.1002/aisy.20210014010.1002/aisy.202100140 ↗
12. 10.1109/LRA.2023.326473410.1109/LRA.2023.3264734 ↗
13. 10.3389/fneur.2019.0083610.3389/fneur.2019.00836 ↗
14. 10.1038/s41551-021-00767-010.1038/s41551-021-00767-0 ↗
15. 10.1126/scirobotics.adh785210.1126/scirobotics.adh7852 ↗
16. 10.1002/adma.20170638310.1002/adma.201706383 ↗
17. 10.1016/j.device.2024.10028810.1016/j.device.2024.100288 ↗
18. 10.1126/scirobotics.aai752910.1126/scirobotics.aai7529 ↗
19. 10.1038/s41467-020-19059-310.1038/s41467-020-19059-3 ↗
20. 10.1016/j.sna.2024.11536410.1016/j.sna.2024.115364 ↗
21. 10.1002/aisy.20200022310.1002/aisy.202000223 ↗
22. 10.1021/nn404614z10.1021/nn404614z ↗
23. 10.1016/j.nanoen.2023.10878710.1016/j.nanoen.2023.108787 ↗
24. 10.1016/j.nanoen.2023.10824510.1016/j.nanoen.2023.108245 ↗
25. 10.1002/adma.20180111410.1002/adma.201801114 ↗
26. 10.1002/adma.20200146610.1002/adma.202001466 ↗
27. 10.1002/advs.20210521910.1002/advs.202105219 ↗
28. 10.1021/acsnano.2c1199610.1021/acsnano.2c11996 ↗
29. 10.1016/j.nanoen.2020.10550810.1016/j.nanoen.2020.105508 ↗
30. 10.1016/j.nanoen.2022.10717910.1016/j.nanoen.2022.107179 ↗
31. Y.Chen S.Le Q. C.Tan O.Lau F.Wan C.Song in2017 IEEE Int. Conf. on Robotics and Automation (ICRA)2017 IEEE Piscataway NJpp.4782–4787 https://doi.org/10.1109/ICRA.2017.7989556.10.1109/ICRA.2017.7989556 ↗
32. Y.Chen S.Le Q. C.Tan O.Lau C.Song Proceedings of the ASME Design Engineering Technical Conference 2017 5B‐2017 p.V05BT08A023 https://doi.org/10.1115/DETC2017‐68082.
33. 10.1016/j.nanoen.2024.11011210.1016/j.nanoen.2024.110112 ↗
34. 10.1016/j.nanoen.2022.10772810.1016/j.nanoen.2022.107728 ↗
35. 10.1109/TNSRE.2022.319247910.1109/TNSRE.2022.3192479 ↗
36. 10.1038/nature1453910.1038/nature14539 ↗
37. 10.3390/toxins1011042610.3390/toxins10110426 ↗
38. 10.3390/jpm1111107410.3390/jpm11111074 ↗
39. 10.1016/j.neuroscience.2017.06.00910.1016/j.neuroscience.2017.06.009 ↗
40. 10.3389/fnhum.2018.0033210.3389/fnhum.2018.00332 ↗
41. 10.1016/j.neuroimage.2023.11993710.1016/j.neuroimage.2023.119937 ↗
42. 10.1161/01.STR.32.5.113410.1161/01.STR.32.5.1134 ↗
43. 10.1038/nrneurol.2014.16210.1038/nrneurol.2014.162 ↗