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Pregled bibliografske jedinice broj: 1255459

A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography


Xu, Pan; Yang, Xudong; Ma, Wei; He, Wanting; Lučev Vasić, Željka; Cifrek, Mario; Gao, Yueming
A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography // IEEE journal of electromagnetics, RF and microwaves in medicine and biology., 7 (2023), 1; 90-98 doi:10.1109/JERM.2023.3241769 (međunarodna recenzija, članak, znanstveni)


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Naslov
A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography

Autori
Xu, Pan ; Yang, Xudong ; Ma, Wei ; He, Wanting ; Lučev Vasić, Željka ; Cifrek, Mario ; Gao, Yueming

Izvornik
IEEE journal of electromagnetics, RF and microwaves in medicine and biology. (2469-7249) 7 (2023), 1; 90-98

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Handgrip force prediction ; electrical impedance myography ; long short-term memory ; prosthetic control ; muscle rehabilitation

Sažetak
Handgrip force prediction is widely used in the rehabilitation of the arm and prosthetic control. To investigate the effects of different measurement positions and feature parameters on the results of handgrip force prediction, a model based on electrical impedance myography (EIM) and long short-term memory (LSTM) networks was proposed to compare and determine a better scheme for handgrip force prediction. We conducted the signal acquisition experiments of impedance and handgrip force on the anterior forearm muscles and brachioradialis muscle. Afterwards, three evaluationmetrics were introduced to compare the prediction results of various models, and the variability between models was analyzed using paired sample t-tests. The results showed that the model of handgrip force prediction based on anterior forearm muscles exhibited better performance in predicting. The evaluation metrics of R^2, explained variance score (EVS) and normalized mean square error (NMSE) for the model fusing the feature parameters resistance (R) and reactance (X) were 0.9023, 0.9173 and 0.0114, respectively. Therefore, the feature parameters fusing R and X are the optimal input for the handgrip force prediction model. The anterior forearm muscles are the preferred position for impedance measurement over the brachioradialis muscle. This paper validated the feasibility of EIM for handgrip force prediction and provided a new reference and implementation scheme for muscle rehabilitation training and prosthetic control.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Mario Cifrek (autor)

Avatar Url Željka Lučev Vasić (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Xu, Pan; Yang, Xudong; Ma, Wei; He, Wanting; Lučev Vasić, Željka; Cifrek, Mario; Gao, Yueming
A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography // IEEE journal of electromagnetics, RF and microwaves in medicine and biology., 7 (2023), 1; 90-98 doi:10.1109/JERM.2023.3241769 (međunarodna recenzija, članak, znanstveni)
Xu, P., Yang, X., Ma, W., He, W., Lučev Vasić, Ž., Cifrek, M. & Gao, Y. (2023) A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography. IEEE journal of electromagnetics, RF and microwaves in medicine and biology., 7 (1), 90-98 doi:10.1109/JERM.2023.3241769.
@article{article, author = {Xu, Pan and Yang, Xudong and Ma, Wei and He, Wanting and Lu\v{c}ev Vasi\'{c}, \v{Z}eljka and Cifrek, Mario and Gao, Yueming}, year = {2023}, pages = {90-98}, DOI = {10.1109/JERM.2023.3241769}, keywords = {Handgrip force prediction, electrical impedance myography, long short-term memory, prosthetic control, muscle rehabilitation}, journal = {IEEE journal of electromagnetics, RF and microwaves in medicine and biology.}, doi = {10.1109/JERM.2023.3241769}, volume = {7}, number = {1}, issn = {2469-7249}, title = {A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography}, keyword = {Handgrip force prediction, electrical impedance myography, long short-term memory, prosthetic control, muscle rehabilitation} }
@article{article, author = {Xu, Pan and Yang, Xudong and Ma, Wei and He, Wanting and Lu\v{c}ev Vasi\'{c}, \v{Z}eljka and Cifrek, Mario and Gao, Yueming}, year = {2023}, pages = {90-98}, DOI = {10.1109/JERM.2023.3241769}, keywords = {Handgrip force prediction, electrical impedance myography, long short-term memory, prosthetic control, muscle rehabilitation}, journal = {IEEE journal of electromagnetics, RF and microwaves in medicine and biology.}, doi = {10.1109/JERM.2023.3241769}, volume = {7}, number = {1}, issn = {2469-7249}, title = {A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography}, keyword = {Handgrip force prediction, electrical impedance myography, long short-term memory, prosthetic control, muscle rehabilitation} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus


Citati:





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