Reference Trajectory Generation for Closed-Loop Control of Electrical Stimulation for Rehabilitation of Upper Limb

Karak, Tarun; Tiwari, Laxmi Kant; Sengupta, Somnath; Nag, Sayan

doi:10.1016/j.ifacol.2020.12.710

Cited by 3 publications

(1 citation statement)

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“…As for hand opening, just the COV from bilateral LiDAR can not tell how the gesture is performed since hand opening with the maximum motion amplitude on the hemiplegic side still significantly differs from that on the healthy side. Tarun Karak et al utilized data gloves signals as position and velocity feedback for closed-loop control of FES, achieving prosperous and stable grasping [40]. Zhou et al proposed a closed-loop FES system based on sEMG bias, also for stable grasping [13].…”

Section: Discussionmentioning

confidence: 99%

LiDAR-Based Hand Contralateral Controlled Functional Electrical Stimulation System

Huang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Contralateral controlled functional electrical stimulation (CCFES) can induce simultaneous movements in patients' bilateral hands. It has been clinically proven to be effective in improving hand motor control and dexterity. sEMG and bending sensor-based data gloves for detecting patients' motor intent have been developed with limitations. sEMG sensor signals are unstable and susceptible to noise. Data gloves composed of bending sensors require complicated calibration and tend to have data drift. In this paper, a LiDAR-based system for hand CCFES is proposed. The method utilized LiDAR to detect the patient's motion intention without contact in CCFES systems. It has been clinically proven that LiDARs can effectively distinguish the different motion amplitudes of hand gestures as quantitative evaluation sensors of functional electrical stimulation (FES). Training data for classifiers were collected from 9 healthy individuals and 15 stroke patients performing 4 gestures, including hand opening, fist clenching, wrist extension, and wrist flexion. The support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (kNN) were verified for their classification performance in offline hand gesture recognition tests. Experiments were also conducted on 6 stroke volunteers to evaluate gestures triggered by FES. The SVM classifier showed excellent classification performance for four hand gestures, with an average F1-score of 0.97±0.05 in offline tests. As for online gesture recognition, an average F1-score of 0.92±0.09 was obtained.In the evaluation experiments, between data from 50% and 100% movement amplitude, paired t-tests showed significant differences. The experimental results indicated that the proposed system showed promise for hand rehabilitation.

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Section: Discussionmentioning

confidence: 99%