TY - GEN
T1 - Developing a Fuzzy C-Means Inference System for Muscle Strength Prediction Using sEMG
AU - Lin, Yimo
AU - Tan, Poh Thong
AU - Lin, Yang Cheng
AU - Yang, Tai Hua
AU - Lien, Wei Chih
AU - Yang, Yi Ching
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The research involved the development of a fuzzy inference system (FIS) to predict grip strength through the analysis of sEMG signals. Fuzzy rules were generated using fuzzy c-means (FCM) clustering. In an experiment focused on lifting motions and including nine participants, the FIS demonstrated promising results. Specifically, in the non-weight condition, the FIS achieved a Mean Squared Error (MSE) of 0.1246 and an R-squared value of 0.3357 for grip strength prediction. However, when an 11-lb weight load was introduced, the accuracy of the FIS dropped significantly, leading to less reliable predictions. This was reflected in an increased MSE of 0.1804 and a reduced R-squared value of 0.0379. These outcomes underscore the potential of wearable sEMG devices coupled with a fuzzy inference system for grip strength prediction. The research also highlights the need for further research in this evolving field.
AB - The research involved the development of a fuzzy inference system (FIS) to predict grip strength through the analysis of sEMG signals. Fuzzy rules were generated using fuzzy c-means (FCM) clustering. In an experiment focused on lifting motions and including nine participants, the FIS demonstrated promising results. Specifically, in the non-weight condition, the FIS achieved a Mean Squared Error (MSE) of 0.1246 and an R-squared value of 0.3357 for grip strength prediction. However, when an 11-lb weight load was introduced, the accuracy of the FIS dropped significantly, leading to less reliable predictions. This was reflected in an increased MSE of 0.1804 and a reduced R-squared value of 0.0379. These outcomes underscore the potential of wearable sEMG devices coupled with a fuzzy inference system for grip strength prediction. The research also highlights the need for further research in this evolving field.
UR - http://www.scopus.com/inward/record.url?scp=85179759566&partnerID=8YFLogxK
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U2 - 10.1109/GCCE59613.2023.10315627
DO - 10.1109/GCCE59613.2023.10315627
M3 - Conference contribution
AN - SCOPUS:85179759566
T3 - GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
SP - 1028
EP - 1032
BT - GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Global Conference on Consumer Electronics, GCCE 2023
Y2 - 10 October 2023 through 13 October 2023
ER -