Smart Wearable Untuk Klasifikasi Pose Latihan Squat Menggunakan Algoritme Random Forest Berbasis ESP32-S3
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Naskah ini akan diterbitkan di Jurnal Intelligent Decision Technologies
Referensi
Adugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sensing, 14(3), 574. https://doi.org/10.3390/rs14030574
Blandeau, M., Guichard, R., Hubaut, R., & Leteneur, S. (2022). Two-Step Validation of a New Wireless Inertial Sensor System: Application in the Squat Motion. Technologies, 10(3), 72. https://doi.org/10.3390/technologies10030072
Blandeau, M., Guichard, R., Hubaut, R., & Leteneur, S. (2023). IMU positioning affects range of motion measurement during squat motion analysis. Journal of Biomechanics, 153, 111598. https://doi.org/10.1016/j.jbiomech.2023.111598
Horschig, A., Sonthana, K., & Neff, T. (2017). The squat bible: The ultimate guide to mastering the squat and finding your true strength. Squat University LLC.
Li, X., Liu, J., Huang, Y., Wang, D., & Miao, Y. (2022). Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion. Micromachines, 13(8), 1205. https://doi.org/10.3390/mi13081205
Mohd Sharif, N. A., Goh, S.-L., Usman, J., & Wan Safwani, W. K. Z. (2017). Biomechanical and functional efficacy of knee sleeves: A literature review. Physical Therapy in Sport, 28, 44–52. https://doi.org/10.1016/j.ptsp.2017.05.001
Myer, G. D., Kushner, A. M., Brent, J. L., Schoenfeld, B. J., Hugentobler, J., Lloyd, R. S., Vermeil, A., Chu, D. A., Harbin, J., & McGill, S. M. (2014). The Back Squat: A Proposed Assessment of Functional Deficits and Technical Factors That Limit Performance. Strength & Conditioning Journal, 36(6), 4–27. https://doi.org/10.1519/SSC.0000000000000103
Ravizza, M., Giani, L., Sheiban, F. J., Pedrocchi, A., DeWitt, J., & Ferrigno, G. (2023). IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off. PLOS ONE, 18(8), e0289777. https://doi.org/10.1371/journal.pone.0289777
Rosero-Montalvo, P. D., Dibujes, A., Vásquez-Ayala, C., Umaquinga-Criollo, A., Michilena, J. R., Suaréz, L., Flores, S., & Jaramillo, D. (2019). Intelligent System of Squat Analysis Exercise to Prevent Back Injuries. In M. Botto-Tobar, L. Barba-Maggi, J. González-Huerta, P. Villacrés-Cevallos, O. S. Gómez, & M. I. Uvidia-Fassler (Eds.), Information and Communication Technologies of Ecuador (TIC.EC) (Vol. 884, pp. 193–205). Springer International Publishing. https://doi.org/10.1007/978-3-030-02828-2_15
Strömbäck, E., Aasa, U., Gilenstam, K., & Berglund, L. (2018). Prevalence and Consequences of Injuries in Powerlifting: A Cross-sectional Study. Orthopaedic Journal of Sports Medicine, 6(5), 2325967118771016. https://doi.org/10.1177/2325967118771016
Whelan, D. F., O’Reilly, M. A., Ward, T. E., Delahunt, E., & Caulfield, B. (2017). Technology in Rehabilitation: Comparing Personalised and Global Classification Methodologies in Evaluating the Squat Exercise with Wearable IMUs. Methods of Information in Medicine, 56(05), 361–369. https://doi.org/10.3414/ME16-01-0141
William, P., Lanke, G. R., Bordoloi, D., Shrivastava, A., Srivastavaa, A. P., & Deshmukh, S. V. (2023). Assessment of Human Activity Recognition based on Impact of Feature Extraction Prediction Accuracy. 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 1–6. https://doi.org/10.1109/ICIEM59379.2023.10166247
Zheng, Q., Fu, X., Li, Y., & Cai, S. (2023). Adaptive Real-time Rectifying Exercise Porsture of Sport Rehabilitation System Based on MediaPipe. 2023 2nd International Conference on Health Big Data and Intelligent Healthcare (ICHIH), 176–181. https://doi.org/10.1109/ICHIH60370.2023.10396651
Zink, A. J., Whiting, W. C., Vincent, W. J., & Mclaine, A. J. (2001). The Effects of a Weight Belt on Trunk and Leg Muscle Activity and Joint Kinematics During the Squat Exercise.
Adugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sensing, 14(3), 574. https://doi.org/10.3390/rs14030574
Blandeau, M., Guichard, R., Hubaut, R., & Leteneur, S. (2022). Two-Step Validation of a New Wireless Inertial Sensor System: Application in the Squat Motion. Technologies, 10(3), 72. https://doi.org/10.3390/technologies10030072
Blandeau, M., Guichard, R., Hubaut, R., & Leteneur, S. (2023). IMU positioning affects range of motion measurement during squat motion analysis. Journal of Biomechanics, 153, 111598. https://doi.org/10.1016/j.jbiomech.2023.111598
Horschig, A., Sonthana, K., & Neff, T. (2017). The squat bible: The ultimate guide to mastering the squat and finding your true strength. Squat University LLC.
Li, X., Liu, J., Huang, Y., Wang, D., & Miao, Y. (2022). Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion. Micromachines, 13(8), 1205. https://doi.org/10.3390/mi13081205
Mohd Sharif, N. A., Goh, S.-L., Usman, J., & Wan Safwani, W. K. Z. (2017). Biomechanical and functional efficacy of knee sleeves: A literature review. Physical Therapy in Sport, 28, 44–52. https://doi.org/10.1016/j.ptsp.2017.05.001
Myer, G. D., Kushner, A. M., Brent, J. L., Schoenfeld, B. J., Hugentobler, J., Lloyd, R. S., Vermeil, A., Chu, D. A., Harbin, J., & McGill, S. M. (2014). The Back Squat: A Proposed Assessment of Functional Deficits and Technical Factors That Limit Performance. Strength & Conditioning Journal, 36(6), 4–27. https://doi.org/10.1519/SSC.0000000000000103
Ravizza, M., Giani, L., Sheiban, F. J., Pedrocchi, A., DeWitt, J., & Ferrigno, G. (2023). IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off. PLOS ONE, 18(8), e0289777. https://doi.org/10.1371/journal.pone.0289777
Rosero-Montalvo, P. D., Dibujes, A., Vásquez-Ayala, C., Umaquinga-Criollo, A., Michilena, J. R., Suaréz, L., Flores, S., & Jaramillo, D. (2019). Intelligent System of Squat Analysis Exercise to Prevent Back Injuries. In M. Botto-Tobar, L. Barba-Maggi, J. González-Huerta, P. Villacrés-Cevallos, O. S. Gómez, & M. I. Uvidia-Fassler (Eds.), Information and Communication Technologies of Ecuador (TIC.EC) (Vol. 884, pp. 193–205). Springer International Publishing. https://doi.org/10.1007/978-3-030-02828-2_15
Strömbäck, E., Aasa, U., Gilenstam, K., & Berglund, L. (2018). Prevalence and Consequences of Injuries in Powerlifting: A Cross-sectional Study. Orthopaedic Journal of Sports Medicine, 6(5), 2325967118771016. https://doi.org/10.1177/2325967118771016
Whelan, D. F., O’Reilly, M. A., Ward, T. E., Delahunt, E., & Caulfield, B. (2017). Technology in Rehabilitation: Comparing Personalised and Global Classification Methodologies in Evaluating the Squat Exercise with Wearable IMUs. Methods of Information in Medicine, 56(05), 361–369. https://doi.org/10.3414/ME16-01-0141
William, P., Lanke, G. R., Bordoloi, D., Shrivastava, A., Srivastavaa, A. P., & Deshmukh, S. V. (2023). Assessment of Human Activity Recognition based on Impact of Feature Extraction Prediction Accuracy. 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 1–6. https://doi.org/10.1109/ICIEM59379.2023.10166247
Zheng, Q., Fu, X., Li, Y., & Cai, S. (2023). Adaptive Real-time Rectifying Exercise Porsture of Sport Rehabilitation System Based on MediaPipe. 2023 2nd International Conference on Health Big Data and Intelligent Healthcare (ICHIH), 176–181. https://doi.org/10.1109/ICHIH60370.2023.10396651
Zink, A. J., Whiting, W. C., Vincent, W. J., & Mclaine, A. J. (2001). The Effects of a Weight Belt on Trunk and Leg Muscle Activity and Joint Kinematics During the Squat Exercise.
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