<p>The abundant muscle tissues of the forearm determine the movements of the wrist, hand and fingers together. However, linking wrist kinematics and forearm muscle activation is still a challenging. There may exist blindness in the rehabilitation therapy of forearm muscles, due to the lack of the physiological characteristics of muscle activation and sequences. An armband with eight channels was used to collect surface electromyographic signals (sEMGs) of a specific section of the forearm under the different wrist movements, palm postures, and external loads, based on the image of magnetic resonance imaging (MRI). The collected cross-sectional muscles covered almost all surface muscles. The muscle activation could be expressed clearly by enveloping the sEMG signals of 8 muscles within a single cycle. The root mean square (RMS) and the average peak value <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10033_2025_1296_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="25" /> </InlineMediaObject> <EquationSource Format="TEX">$\overline{V}_{P}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mover> <mi>V</mi> <mo>¯</mo> </mover> <mi>P</mi> </msub> </math></EquationSource> </InlineEquation> were used to evaluate the activation intensities of dominant muscles. The activation sequences and the absolute times of dominant muscles were obtained from the envelopes of their raw sEMGs, and not influenced by the palm postures and external loads. In addition, their RMS and <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10033_2025_1296_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="25" /> </InlineMediaObject> <EquationSource Format="TEX">$\overline{V}_{P}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mover> <mi>V</mi> <mo>¯</mo> </mover> <mi>P</mi> </msub> </math></EquationSource> </InlineEquation> under each wrist movement increased approximate linearly with external loads. The corresponding contribution ratios were first calculated to evaluate the role played by each muscle. The well-defined data of forearm muscles could provide standard references for the rehabilitation therapy of forearm muscles.</p>

Physiological Characteristics of Forearm Muscles During Different Movement Patterns of Wrist

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2025年8月5日

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Leiyu Zhang,
Xu Sun,
Peng Su,
Jianfeng Li,
Yawei Chang,
Yongjian Gao,
Li Zhang

摘要

The abundant muscle tissues of the forearm determine the movements of the wrist, hand and fingers together. However, linking wrist kinematics and forearm muscle activation is still a challenging. There may exist blindness in the rehabilitation therapy of forearm muscles, due to the lack of the physiological characteristics of muscle activation and sequences. An armband with eight channels was used to collect surface electromyographic signals (sEMGs) of a specific section of the forearm under the different wrist movements, palm postures, and external loads, based on the image of magnetic resonance imaging (MRI). The collected cross-sectional muscles covered almost all surface muscles. The muscle activation could be expressed clearly by enveloping the sEMG signals of 8 muscles within a single cycle. The root mean square (RMS) and the average peak value $\overline{V}_{P}$ ${\bar{V}}_{P}$ were used to evaluate the activation intensities of dominant muscles. The activation sequences and the absolute times of dominant muscles were obtained from the envelopes of their raw sEMGs, and not influenced by the palm postures and external loads. In addition, their RMS and $\overline{V}_{P}$ ${\bar{V}}_{P}$ under each wrist movement increased approximate linearly with external loads. The corresponding contribution ratios were first calculated to evaluate the role played by each muscle. The well-defined data of forearm muscles could provide standard references for the rehabilitation therapy of forearm muscles.