Knee Joint Protection: Effect of Foot Pressure Position on Rectus Femoris EMG during Squats
- Authors
- Choi, Jintak; Shin, Dongbin; Kang, Kyungtae
- Issue Date
- Dec-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Backward and side pressure (BSP) squat; Electromyography (EMG); Foot pressure; Machine learning
- Citation
- Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, pp 2844 - 2850
- Pages
- 7
- Indexed
- SCOPUS
- Journal Title
- Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
- Start Page
- 2844
- End Page
- 2850
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118894
- DOI
- 10.1109/BIBM58861.2023.10385884
- Abstract
- The muscles around the knee deteriorate with age, putting more strain on the knee joint. It is important to strengthen the knee muscles through exercises such as squats even after knee surgery. However, an unstable squatting posture can lead to lower back and knee injuries; thus, it is important to maintain the correct posture. This study proposes the correct squat posture to strengthen the knee muscles using a cost-effective and easily applicable method based on previous research. The squat posture that protects the knee joint involves activating the quadriceps femoris muscle, which is the main muscle in the thigh area that supports weight from below the pelvis, passing through the knee ligaments and reaching the tibia. This method involves maintaining the squat posture while keeping the center of gravity of the body shifted backward and to the sides. We examine general squat and backward and side pressure (BSP) squat muscle recruitment strategies using ten participants. We attach electromyography sensors to the rectus femoris muscle and use foot pressure sensors to confirm that the participants performed both types of squats with precision. The results showed that the specific squat method had a 24% to 31% (paired t-test p=0.0029) increase in muscle strength for the half squat and a 23% to 27% (paired t-test p=0.0475) increase for the full squat, compared to the general squat, in significant intervals in the electromyography sensor data. These results suggest that maintaining a posture that maximizes activation of the rectus femoris muscle is useful for designing training methods that aim to strengthen and protect knee muscles during squats. This study proposes a new statistical analysis method using the k-means algorithm of machine learning after saving the signals from the time series analog as comma separated value (CSV) files.
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