This paper proposes an air pocket glove for finger rehabilitation and diagnosis of hemiplegic patients after stroke. This device consists of pneumatic actuators that expand when air is injected from a pump motor, silicone flexors that act as artificial finger tendons, film-type bending sensors, and a pressure sensor. As air enters the glove, the actuators are expanded, thus stretching out paralyzed fingers. We designed two different rehabilitation modes: continuous passive motion (CPM) mode and master-slave mode, where the motions of the unaffected fingers are duplicated in the affected fingers. We conducted an experiment to test the validity of the device for each mode. In CPM mode, the patient’s spasticity level was estimated from finger angle and air pressure. Our results showed that spasticity level decreased 13% from the initial level after rehabilitation. With the master-slave mode, EMG signals were additionally measured and compared to those found during conventional therapy, which revealed a positive effect stemming from voluntary involvement in the exercise. As a result, EMG energy was shown to increase up to 18% during master-slave mode.
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Ergonomic glove pattern drafting method for hand assistive devices: considering 3D hand dimensions and finger mobility Soo-Min Lee, Juyeon Park Fashion and Textiles.2024;[Epub] CrossRef
A Wearable Soft Robot for Stroke Patients’ Finger Occupational Therapy and Quantitative Measures on the Joint Paralysis Jihun Kim, Geonhui Lee, Hanjin Jo, Wookhyun Park, Yu Shin Jin, Ho Dong Kim, Jaehyo Kim International Journal of Precision Engineering and Manufacturing.2020; 21(12): 2419. CrossRef
Conventional prosthetic hands require users to activate designated muscles or press buttons to select among predefined grasping patterns. These methods are time-consuming and increase muscle fatigue. This study proposes a regression model that differentiates multiple muscle activation patterns allowing the user to select a desired grasping pattern. We classified four hand primitives and three force intensities, which can reflect the intention of prosthetic hand users. An 8-channel band-type sEMG sensor was used to measure myoelectric signals from an amputated upper-arm. To acquire the sEMG data, the amputee was instructed to imagine four hand primitives (fist, open hand, flexion, and extension) with three levels of force intensity (low, medium, and high). Time-domain features (mean average value, variance, waveform length, and root mean square) were extracted from the sEMG signal and classified using a Support Vector Machine. The hand primitives and force intensities had accuracies of 95% and 90%, respectively. Results indicate the regression model reflected the user’s intention to select different grasping patterns, and is thus expected to improve the quality of life of amputees.
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Continuous grip force estimation from surface electromyography using generalized regression neural network He Mao, Peng Fang, Yue Zheng, Lan Tian, Xiangxin Li, Pu Wang, Liang Peng, Guanglin Li Technology and Health Care.2023; 31(2): 675. CrossRef
Design of Prosthetic Robot Hand and Electromyography-Based Hand Motion Recognition Ho Myoung Jang, Jung Woo Sohn Journal of the Korean Society for Precision Engineering.2020; 37(5): 339. CrossRef
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