Sistem Monitoring Volitional Fatigue Menggunakan Metode Random Forest dengan Fitur Root Mean Square dan Integrated Electromyogram
Kata Kunci:
electromyography, volitional fatigue, kelelahan otot, RMS, IEMG, random forestAbstrak
Naskah ini akan diterbitkan pada SIET 2024
Referensi
Ashfaq, A., Cronin, N. & Müller, P., 2022. Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction: A review. Informatics in Medicine Unlocked , Volume 28.
Chen, S. et al., 2021. Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas. Computers in Biology and Medicine, Volume 133.
de Cheveigné, A. & Nelken, I., 2019. Filters: When, Why, and How (Not) to Use Them. Neuron, 102(2), pp. 280-293.
Ebied, A., Awadallah, A. M., Abbass, M. A. & El-Sharkawy, Y., 2020. Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG. Giza, IEEE.
Elshafei, M. & Shihab, E., 2021. Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables. Sensors, 21(3).
Felton, R., 2022. 11 Mistakes Personal Trainers Make. [Online] Tersedia di: https://www.origym.co.uk/blog/mistakes-personal-trainers-make/ [Diakses 23 Juli 2024].
Gavin, J., 1996. Personal Trainers' Perceptions of Role Responsibilities, Conflicts, and Boundaries. Ethics & Behavior, 6(1), pp. 55-69.
González-Izal, M., Malanda, A., Gorostiaga, E. & Izquierdo, M., 2012. Electromyographic models to assess muscle fatigue. Journal of Electromyography and Kinesiology, 22(4), pp. 501-512.
Li, N. et al., 2024. Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey. ACM Computing Surveys.
Melnikov, A. & Fette, I., 2011. The WebSocket Protocol. [Online] Tersedia di: https://www.rfc-editor.org/rfc/rfc6455.txt [Diakses 25 Juli 2024].
Merletti, R. & Cerone, G. L., 2020. Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices. Journal of Electromyography and Kinesiology, p. 102440.
Micromodeler, 2013. Introduction to Digital Signal Processing with MicroModeler DSP. [Online] Tersedia di: https://www.micromodeler.com/articles/IntroductionToDSP/ [Diakses 11 Juli 2024].
Morton, R. W., Phillips, S. M. & Phillips, S. M., 2019. Training for strength and hypertrophy: an evidence-based approach. Current Opinion in Physiology, Volume 10, pp. 90-95.
Myoware, 2022. Myoware 2.0 Advanced Guide. [Online] Tersedia di: https://myoware.com/wp-content/uploads/2022/03/MyoWare_v2_AdvancedGuide-Updated.pdf [Diakses 7 Juli 2024].
Newsome, A. M. et al., 2024. 2024 ACSM Worldwide Fitness Trends: Future Directions of the Health and Fitness Industry. ACSM's Health & Fitness Journal, 28(1), pp. 14-26.
Phinyomark, A., Phukpattaranont, P. & Limsakul, C., 2012. Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), pp. 7420-7431.
Snow, E. L. & Lanik, W. E., 2024. Structural and functional analysis of bilateral five-headed biceps brachii. Translational Research in Anatomy, Volume 35.
Subasi, A., 2020. Machine learning techniques. In: Practical Machine Learning for Data Analysis Using Python. s.l.:Academic Press, pp. 91-202.
Sun, J. et al., 2022. Application of Surface Electromyography in Exercise Fatigue: A Review. Frontiers in Systems Neuroscience, Volume 16.
Susto, G. A., Cenedese, A. & Terzi, M., 2018. Time-Series Classification Methods: Review and Applications to Power Systems Data. Big Data Application in Power Systems, pp. 179-220.
Viecelli, C. & Aguayo, D., 2022. May the Force and Mass Be With You—Evidence-Based Contribution of Mechano-Biological Descriptors of Resistance Exercise. Frontiers in Physiology, Volume 12.
Ashfaq, A., Cronin, N. & Müller, P., 2022. Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction: A review. Informatics in Medicine Unlocked , Volume 28.
Chen, S. et al., 2021. Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas. Computers in Biology and Medicine, Volume 133.
de Cheveigné, A. & Nelken, I., 2019. Filters: When, Why, and How (Not) to Use Them. Neuron, 102(2), pp. 280-293.
Ebied, A., Awadallah, A. M., Abbass, M. A. & El-Sharkawy, Y., 2020. Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG. Giza, IEEE.
Elshafei, M. & Shihab, E., 2021. Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables. Sensors, 21(3).
Felton, R., 2022. 11 Mistakes Personal Trainers Make. [Online] Tersedia di: https://www.origym.co.uk/blog/mistakes-personal-trainers-make/ [Diakses 23 Juli 2024].
Gavin, J., 1996. Personal Trainers' Perceptions of Role Responsibilities, Conflicts, and Boundaries. Ethics & Behavior, 6(1), pp. 55-69.
González-Izal, M., Malanda, A., Gorostiaga, E. & Izquierdo, M., 2012. Electromyographic models to assess muscle fatigue. Journal of Electromyography and Kinesiology, 22(4), pp. 501-512.
Li, N. et al., 2024. Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey. ACM Computing Surveys.
Melnikov, A. & Fette, I., 2011. The WebSocket Protocol. [Online] Tersedia di: https://www.rfc-editor.org/rfc/rfc6455.txt [Diakses 25 Juli 2024].
Merletti, R. & Cerone, G. L., 2020. Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices. Journal of Electromyography and Kinesiology, p. 102440.
Micromodeler, 2013. Introduction to Digital Signal Processing with MicroModeler DSP. [Online] Tersedia di: https://www.micromodeler.com/articles/IntroductionToDSP/ [Diakses 11 Juli 2024].
Morton, R. W., Phillips, S. M. & Phillips, S. M., 2019. Training for strength and hypertrophy: an evidence-based approach. Current Opinion in Physiology, Volume 10, pp. 90-95.
Myoware, 2022. Myoware 2.0 Advanced Guide. [Online] Tersedia di: https://myoware.com/wp-content/uploads/2022/03/MyoWare_v2_AdvancedGuide-Updated.pdf [Diakses 7 Juli 2024].
Newsome, A. M. et al., 2024. 2024 ACSM Worldwide Fitness Trends: Future Directions of the Health and Fitness Industry. ACSM's Health & Fitness Journal, 28(1), pp. 14-26.
Phinyomark, A., Phukpattaranont, P. & Limsakul, C., 2012. Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), pp. 7420-7431.
Snow, E. L. & Lanik, W. E., 2024. Structural and functional analysis of bilateral five-headed biceps brachii. Translational Research in Anatomy, Volume 35.
Subasi, A., 2020. Machine learning techniques. In: Practical Machine Learning for Data Analysis Using Python. s.l.:Academic Press, pp. 91-202.
Sun, J. et al., 2022. Application of Surface Electromyography in Exercise Fatigue: A Review. Frontiers in Systems Neuroscience, Volume 16.
Susto, G. A., Cenedese, A. & Terzi, M., 2018. Time-Series Classification Methods: Review and Applications to Power Systems Data. Big Data Application in Power Systems, pp. 179-220.
Viecelli, C. & Aguayo, D., 2022. May the Force and Mass Be With You—Evidence-Based Contribution of Mechano-Biological Descriptors of Resistance Exercise. Frontiers in Physiology, Volume 12.
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