Literature DB >> 34082406

The effect of calibration parameters on the control of a myoelectric hand prosthesis using EMG feedback.

Jack Tchimino1, Marko Markovic2, Jakob Lund Dideriksen1, Strahinja Dosen1.   

Abstract

Objective.The implementation of somatosensory feedback in upper limb myoelectric prostheses is an important step towards the restoration of lost sensory-motor functions. EMG feedback is a recently proposed method for closing the control loop wherein the myoelectric signal that drives the prosthesis is also used to generate the feedback provided to the user. Therefore, the characteristics of the myoelectric signal (variability and sensitivity) are likely to significantly affect the ability of the subject to utilize this feedback for online control of the prosthesis.Approach.In the present study, we investigated how the cutoff frequency of the low-pass filter (0.5, 1 and 1.5 Hz) and normalization value (20%, 40% and 60% of the maximum voluntary contraction (MVC)), that are used for the generation of the myoelectric signal, affect the quality of closed-loop control with EMG feedback. Lower cutoff and normalization decrease the intrinsic variability of the EMG but also increase the time lag between the contraction and the feedback (cutoff) as well as the sensitivity of the myoelectric signal (normalization). Ten participants were asked to generate three grasp force levels with a myoelectric prosthetic hand, while receiving five-level vibrotactile EMG feedback, over nine experimental runs (all parameter combinations).Main results.The outcome measure was the success rate (SR) in achieving the appropriate level of myoelectric signal (primary outcome) and grasping force (secondary outcome). Overall, the experiments demonstrated that EMG feedback provided robust control across conditions. Nevertheless, the performance was significantly better for the lowest cutoff (0.5 Hz) and higher normalization (40% and 60%). The highest SR for the EMG was 71.9%, achieved in the condition (40% MVC and 0.5 Hz), and this was 24.1% higher than that in the condition (20% MVC and 1.5 Hz), which resulted in the lowest performance. The SR for the force followed a similar trend.Significance.This is the first study that systematically explored the parameter space for the calibration of EMG feedback, which is a critical step for the future clinical application of this approach.
© 2021 IOP Publishing Ltd.

Entities:  

Keywords:  EMG feedback; closed-loop control; grasping force control; myoelectric prosthesis; somatosensory feedback; vibrotactile stimulation

Year:  2021        PMID: 34082406     DOI: 10.1088/1741-2552/ac07be

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  4 in total

1.  Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke.

Authors:  Xinyu Song; Shirdi Shankara van de Ven; Shugeng Chen; Peiqi Kang; Qinghua Gao; Jie Jia; Peter B Shull
Journal:  Front Physiol       Date:  2022-06-03       Impact factor: 4.755

2.  Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements.

Authors:  Manuela Gomez-Correa; David Cruz-Ortiz
Journal:  Sensors (Basel)       Date:  2022-08-09       Impact factor: 3.847

3.  Combined spatial and frequency encoding for electrotactile feedback of myoelectric signals.

Authors:  Sara Nataletti; Fabrizio Leo; Jakob Dideriksen; Luca Brayda; Strahinja Dosen
Journal:  Exp Brain Res       Date:  2022-07-25       Impact factor: 2.064

4.  EMG feedback outperforms force feedback in the presence of prosthesis control disturbance.

Authors:  Jack Tchimino; Jakob Lund Dideriksen; Strahinja Dosen
Journal:  Front Neurosci       Date:  2022-09-20       Impact factor: 5.152

  4 in total

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