Sistem Wearable untuk Deteksi Postur Awal Badan pada Latihan Deadlift Menggunakan Sensor MPU6050 dengan Metode Support Vector Machine (SVM)
Kata Kunci:
wearable, deadlift, MPU6050, Support Vector Machine, postur badanAbstrak
Olahraga rutin terbukti efektif dalam menjaga kebugaran, meningkatkan kekuatan fisik, dan mengurangi risiko penyakit fatal. Salah satu olahraga yang mendukung hal tersebut adalah weightlifting atau latihan angkat beban. Latihan angkat beban, khususnya gerakan deadlift, efektif untuk meningkatkan kekuatan dan ketahanan tubuh. Namun, kesalahan postur saat melakukan deadlift, terutama pada posisi awal, dapat meningkatkan risiko cedera, terutama pada punggung bawah. Penelitian ini bertujuan untuk mengembangkan sistem wearable yang dapat mendeteksi dan mengklasifikasikan postur badan pada posisi awal deadlift guna mengurangi risiko cedera. Sistem wearable ini menggunakan sensor MPU6050 yang memanfaatkan akselerometer dan giroskop untuk memperoleh data postur tubuh, yang kemudian diolah menggunakan metode Support Vector Machine (SVM) dengan validasi Stratified K-Fold Cross-Validation. Sistem ini diintegrasikan dengan mikrokontroler ESP32 untuk melakukan klasifikasi postur yang benar, salah, atau idle saat melakukan posisi awal deadlift. Hasil penelitian menunjukkan bahwa model SVM hanya dengan data akselerometer mencapai akurasi 99,23%, sedangkan implementasi sistem wearable yang diintegrasikan dalam ESP32 menunjukkan akurasi 94,44%. Sistem ini juga menunjukkan performa yang baik dalam pembacaan data sensor, komunikasi data, dan waktu komputasi dengan rata-rata waktu 2 ms.
Referensi
Ahuja, A. & Mathpal, D., 2022. An Analysis of Health Benefits of Exercise. International Journal of Innovative Research in Engineering & Management, 9(1), pp. 129-133.
Cesari, P. et al., 2023. Towards Posture and Gait Evaluation through Wearable-Based. Electronics, 12(3), p. 644.
Conforti, I., Mileti, I., Prete, Z. D. & Palermo., E., 2020. Measuring biomechanical risk in lifting load tasks through wearable system and machine-learning approach. Sensors, 20(6), p. 1557.
Fischer, S. C., Calley, D. Q. & Hollman, J. H., 2021. Effect of an Exercise Program That Includes Deadlifts. Journal of Sport Rehabilitation, 30(4), pp. 672-675.
Flandez, J. et al., 2020. A systematic review on the muscular activation on the lower limbs with five different variations of the deadlift exercise. Journal of Human Sport and Exercise, 15(Proc4), pp. S1262-S1276.
Ghosh, S., Dasgupta, A. & Swetapadma., A., 2019. A study on support vector machine based linear and non-linear pattern classification. s.l., IEEE (Institute of Electrical and Electronics Engineers).
Gupta, J. et al., 2021. Collection and Classification of Human Posture Data using Wearable Sensors. Piscataway, NJ, IEEE.
Jadhao, M. & Gharote, G., 2024. Prevalence of Injuries in Junior Conventional Powerlifters in Maharashtra Population. International Journal For Multidisciplinary Research.
Kaliappan, J. et al., 2023. Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise. Diagnostics, 13(10), p. 1692.
Martín-Fuentes, I., Oliva-Lozano, J. M. & Muyor, J. M., 2020. Electromyographic activity in deadlift exercise and its variants. A systematic review. PloS One, 15(2).
Michaud, F., Soto, M. P., Lugrís, U. & Cuadrado, J., 2021. Lower back injury prevention and sensitization of hip hinge with neutral spine using wearable sensors during lifting exercises. Sensors, 21(16), p. 5487.
Ozsahin, D. U. et al., 2022. Impact of feature scaling on machine learning models for the diagnosis of diabetes. Lefkosa, Cyprus, IEEE.
Prasanth, N. et al., 2023. Gesture-based mouse control system based on MPU6050 and Kalman filter technique. International Journal of Intelligent Systems Technologies and Applications, 21(1), pp. 56-71.
Zhang, D., 2020. Support Vector Machine. In: In: A Matrix Algebra Approach to Artificial Intelligence. Singapore: Springer, pp. 617-679.
Ahuja, A. & Mathpal, D., 2022. An Analysis of Health Benefits of Exercise. International Journal of Innovative Research in Engineering & Management, 9(1), pp. 129-133.
Cesari, P. et al., 2023. Towards Posture and Gait Evaluation through Wearable-Based. Electronics, 12(3), p. 644.
Conforti, I., Mileti, I., Prete, Z. D. & Palermo., E., 2020. Measuring biomechanical risk in lifting load tasks through wearable system and machine-learning approach. Sensors, 20(6), p. 1557.
Fischer, S. C., Calley, D. Q. & Hollman, J. H., 2021. Effect of an Exercise Program That Includes Deadlifts. Journal of Sport Rehabilitation, 30(4), pp. 672-675.
Flandez, J. et al., 2020. A systematic review on the muscular activation on the lower limbs with five different variations of the deadlift exercise. Journal of Human Sport and Exercise, 15(Proc4), pp. S1262-S1276.
Ghosh, S., Dasgupta, A. & Swetapadma., A., 2019. A study on support vector machine based linear and non-linear pattern classification. s.l., IEEE (Institute of Electrical and Electronics Engineers).
Gupta, J. et al., 2021. Collection and Classification of Human Posture Data using Wearable Sensors. Piscataway, NJ, IEEE.
Jadhao, M. & Gharote, G., 2024. Prevalence of Injuries in Junior Conventional Powerlifters in Maharashtra Population. International Journal For Multidisciplinary Research.
Kaliappan, J. et al., 2023. Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise. Diagnostics, 13(10), p. 1692.
Martín-Fuentes, I., Oliva-Lozano, J. M. & Muyor, J. M., 2020. Electromyographic activity in deadlift exercise and its variants. A systematic review. PloS One, 15(2).
Michaud, F., Soto, M. P., Lugrís, U. & Cuadrado, J., 2021. Lower back injury prevention and sensitization of hip hinge with neutral spine using wearable sensors during lifting exercises. Sensors, 21(16), p. 5487.
Ozsahin, D. U. et al., 2022. Impact of feature scaling on machine learning models for the diagnosis of diabetes. Lefkosa, Cyprus, IEEE.
Prasanth, N. et al., 2023. Gesture-based mouse control system based on MPU6050 and Kalman filter technique. International Journal of Intelligent Systems Technologies and Applications, 21(1), pp. 56-71.
Zhang, D., 2020. Support Vector Machine. In: In: A Matrix Algebra Approach to Artificial Intelligence. Singapore: Springer, pp. 617-679.
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