2.5D Laser-Cutting-Based Customized Fabrication of Long-Term Wearable Textile sEMG Sensor: From Design to Intention Recognition

Jeong, Hwayeong; Feng, Jirou; Kim, Jung

doi:10.1109/lra.2022.3190620

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…When using the typical pre-gelled Ag/AgCl adhesive electrodes, their positions can be changed when reattaching the electrodes. Therefore, the sEMG electrodes can be embedded inside customized clothing for individual users after finding proper electrode positions [44], [45], to eliminate repositioning electrodes for daily use.…”

Section: E Practicality and Robustness Of The Ea Strategymentioning

confidence: 99%

sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton

Heo

Feng

Kim

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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To prevent lower back pain (LBP) in the industrial workplace, various powered back support exoskeletons (BSEs) have been developed. However, conventional kinematics-triggered assistance (KA) strategies induce latency, degrading assistance efficiency. Therefore, we proposed and experimentally evaluated a surface electromyography (sEMG)-triggered assistance (EA) strategy. Nine healthy subjects participated in the lifting experiments: 1) external loads test, 2) extra latency test, and 3) repetitive lifting test. In the external loads test, subject performed lifting with four different external loads (0 kg, 7.5 kg, 15 kg, and 22.5 kg). The assistance was triggered earlier by EA compared to KA from 114 ms to 202 ms, 163 ms to 269 ms for squat and stoop lifting respectively, as external loads increased from 0 kg to 22.5 kg. In the extra latency test, the effects of extra latency (manual switch, 0 ms, 100 ms and 200 ms) in EA on muscle activities were investigated. Muscle activities were minimized in the fast assistance (0 ms and 100 ms) condition and increased with extra latency. In the repetitive lifting test, the EA strategy significantly reduced L1 muscle fatigue by 70.4% in stoop lifting, compared to KA strategy. Based on the experimental results, we concluded that fast assistance triggered by sEMG improved assistance efficiency in BSE and was particularly beneficial in heavy external loads situations. The proposed assistive strategy can be used to prevent LBP by reducing back muscle fatigue and is easily applicable to various industrial exoskeleton applications.

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Section: E Practicality and Robustness Of The Ea Strategymentioning

confidence: 99%

sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton

Heo

Feng

Kim

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…These systems place multiple types of sensors on different carriers, and the data is collected and analyzed by a central processor ( Nascimento et al, 2020 ; Yadav et al, 2021 ). Common sensors used in wrist rehabilitation systems include pressure sensors ( Zhang et al, 2019 ; Atitallah et al, 2020 ; Guo et al, 2021 ; Pierre Claver and Zhao, 2021 ; Xu et al, 2021 ), surface electromyographic(sEMG) sensors ( Prakash et al, 2019 ; Cheng et al, 2021 ; Dong et al, 2021 ; Moin et al, 2021 ; Copaci et al, 2022 ; Jeong et al, 2022 ), inertial sensors ( Kim et al, 2019 ; Weygers et al, 2020 ; Bilius et al, 2023 ), and specialized sensors (e.g., acoustic sensors ( Xiao et al, 2022 ), strain sensors ( Gao et al, 2023 ). Currently, the main researched rehabilitation training devices usually include multiple sensors to achieve multimodal and more accurate training movement analysis, and the main presentation of wrist movement recognition devices is the rehabilitation glove.…”

Section: Introductionmentioning

confidence: 99%

Wearable rehabilitation wristband for distal radius fractures

Zha,

Xu,

Cai

et al. 2023

Front. Neurosci.

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BackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients’ rehabilitation.MethodsA wearable rehabilitation training wristband was proposed, which could be used in the patient’s daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.ResultsExperimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.ConclusionThe developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.

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