A method for gait rehabilitation training using EMG fatigue analysis

Kim, Yoohyun; Kim, Seo-jun; Shim, Hyeon-Min; Lee, Sang‐Min; Kim, Kyu-sung

doi:10.1109/ictc.2013.6675305

Cited by 7 publications

(1 citation statement)

References 7 publications

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“…Dengan menggunakan EMG murah, mahasiswa olahraga dapat memperoleh pemahaman yang lebih baik tentang bagaimana fungsi otot seorang atlet dan bagaimana meningkatkan performa mereka dengan mengamati aktivitas otot mereka selama latihan dan permainan. Seperti sEMG digunakan untuk memantau efektivitas latihan kekuatan atau daya tahan atau rehabilitasi cedera olahraga (Holtermann et al, 2010;Kim et al, 2013). Pemahaman dan pengalaman mahasiswa akan semakin tinggi ketika dalam proses pembelajaran mahasiswa dibekali dengan praktikum pengukuran potensial otot ketika berkontraksi menggunakan SEMG.…”

Section: Abstracts : Surface Electromyography (Semg) Is An Applicatio...unclassified

Effect of Low-Cost Emg for Learning Muscle Contractions in Sport College Students

Pramono

Siantoro

Marsudi

et al. 2023

JSES

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Surface electromyography (SEMG) is an application that helps coaches and athletes recognize and understand muscle contractions during practice and matches. Unfortunately, this tool is very expensive, so it is necessary to develop SEMG, which is cheap but has the potential to detect muscle contractions during movement. This research aims to develop an inexpensive SEMG for detecting muscle contractions. 10 sports students participated in this study, and they were active in carrying out measured and programmed physical activities four times a week. This research is an experimental study; all students will do barbell squats at 80% of the maximum load. Differences in muscle contractions on the SEMG sensor during concentric and eccentric contractions will be analyzed using the paired sample t-test. The results of this study found a difference in the amplitude of the two muscle contractions with p <0.05; besides that, for the first test, this tool was successful in describing a picture of the amplitude that continues to decrease in muscle contractions in fatigued conditions, although there needs to be an additional indicator in assessing the condition tired for SEMG. This study concludes that the SEMG sensor can detect muscle contractions due to a sports movement. Initial experiments in this study successfully detected muscle contraction signals due to different movements used low cost SEMG; then, it needs to be developed better to reduce noise due to electronic devices' influence around SEMG.

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Section: Abstracts : Surface Electromyography (Semg) Is An Applicatio...unclassified

Effect of Low-Cost Emg for Learning Muscle Contractions in Sport College Students

Pramono

Siantoro

Marsudi

et al. 2023

JSES

View full text Add to dashboard Cite

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Electromyograph as a Tool for Patient Feedback in the Field of Rehabilitation and Targeted Muscle Training

Prochazka,

Kasik

2022

Advances in Intelligent Systems and Computing

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Depth-aware pose estimation using deep learning for exoskeleton gait analysis

Wang,

Pei,

Wang

et al. 2023

Sci Rep

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In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human–machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients’ walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.

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A method for gait rehabilitation training using EMG fatigue analysis

Cited by 7 publications

References 7 publications

Effect of Low-Cost Emg for Learning Muscle Contractions in Sport College Students

Effect of Low-Cost Emg for Learning Muscle Contractions in Sport College Students

Electromyograph as a Tool for Patient Feedback in the Field of Rehabilitation and Targeted Muscle Training

Depth-aware pose estimation using deep learning for exoskeleton gait analysis

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