Abstract
Objective: With aging, there are various changes in the autonomic nervous system and a simultaneous decline in the motor functional abilities of the human body. This study falls within the framework improvement of the clinical tools dedicated to the robust evaluation of motor function efficiency with aging. Method: Analysis of HD-sEMG signals recorded from 32 channels during Sit To Stand (STS) test are used for the functional assessment of body muscles. For this purpose, five primary characteristic features, iEMG, ARV, RMS, Skewness, Kurtosis, are employed for the study. A channel clustering approach is proposed based on the parameters using Non Negative Matrix Factorization (NMF) technique. Results: The NMF based clustering of the HD-sEMG channels seems to be sensitive to modifications of the muscle activation strategy with aging during STS test.
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Acknowledgements
The authors acknowledges the funding received from the EIT Health BP2018 under the project CHRONOS.
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Banerjee, S., Imrani, L., Kinugawa, K., Laforet, J., Boudaoud, S. (2020). Analysis of HD-sEMG Signals Using Channel Clustering Based on Time Domain Features For Functional Assessment with Ageing. In: Tavares, J., Dey, N., Joshi, A. (eds) Biomedical Engineering and Computational Intelligence. BIOCOM 2018. Lecture Notes in Computational Vision and Biomechanics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-21726-6_8
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DOI: https://doi.org/10.1007/978-3-030-21726-6_8
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