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Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units
- Source :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 28(12)
- Publication Year :
- 2020
-
Abstract
- Estimation of muscle contraction force based on the macroscopic feature of surface electromyography (SEMG) has been widely reported, but the use of microscopic neural drive information has not been thoroughly investigated. In this study, a novel method is proposed to process individual motor unit (MU) activities (firing sequences and action potential waveforms) derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation. In the proposed method, a supervised machine learning approach was conducted to determine the twitch force of each MU according to its action potential waveforms, which enables separate calculation of every MU’s contribution to force. Thus, the muscle force was predicted through a physiologically meaningful muscle force model. In the experiment, HD-SEMG data were recorded from the abductor pollicis brevis muscles of eight healthy subjects during their performance of thumb abduction with the force increasing gradually from zero to four force levels (10%, 20%, 30%, 40% of the maximal voluntary contraction), while the true muscle force was measured simultaneously. When the proposed method was used, the root mean square difference (RMSD) of the error of the estimated force with respect to the measured force was reported to be 8.3% ± 2.8%. The proposed method also significantly outperformed the other four common methods for force estimation (RMSD: from 11.7% to 20%, ${p} ), demonstrating its effectiveness. This study offers a useful tool for exploiting the neural drive information towards muscle force estimation with improved precision. The proposed method has wide applications in precise motor control, sport and rehabilitation medicine.
- Subjects :
- 0206 medical engineering
Biomedical Engineering
Action Potentials
02 engineering and technology
Electromyography
Thumb
03 medical and health sciences
0302 clinical medicine
Control theory
Internal Medicine
medicine
Waveform
Humans
Muscle, Skeletal
Muscle force
Mathematics
Mechanical Phenomena
medicine.diagnostic_test
General Neuroscience
Rehabilitation
Motor control
020601 biomedical engineering
Motor unit
medicine.anatomical_structure
Feature (computer vision)
medicine.symptom
030217 neurology & neurosurgery
Muscle contraction
Muscle Contraction
Subjects
Details
- ISSN :
- 15580210
- Volume :
- 28
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
- Accession number :
- edsair.doi.dedup.....b2425c382a03c0ca13839b1123c1a5da