Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.
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http://dx.doi.org/10.1109/TNSRE.2024.3438770 | DOI Listing |
Am J Phys Med Rehabil
November 2024
Department of Physical Therapy, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan.
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January 2025
Department of Biomedical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Toayuan City, 32023, Taiwan, 886 32564507.
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View Article and Find Full Text PDFSensors (Basel)
December 2024
Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4805-017 Guimarães, Portugal.
Muscle fatigue is a risk factor for injuries in athletes and workers. This brings relevance to the study of this biochemical process to allow for its identification and prevention. This paper presents a novel dataset for muscle fatigue analysis comprising surface electromyography data from upper-limbs and the subject's self-perceived fatigue level.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Université de Lyon, UCBL1 Laboratoire Inter Universitaire de Biologie de la Motricité, EA 7424, 69100 Villeurbanne Cedex, France.
This study investigated muscle activation, shocks, and vibrations of the upper extremities during tennis serves between junior and adult tennis players. Thirty-five well-trained tennis players (15 juniors and 20 adults) performed 10 maximal successful tennis serves. Two triaxial accelerometers recorded the shock and vibration on the racket and the hand on the dominant side.
View Article and Find Full Text PDFMedicina (Kaunas)
November 2024
Department of Sports Medicine, Medical University of Lublin, 20-093 Lublin, Poland.
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