Implementasi Wearable Device Untuk Sistem Pendeteksi Kelelahan Otot Biceps Menggunakan Metode Support Vector Machine
Kata Kunci:
Wearable Device, Pendeteksi kelelahan otot, Support Vector Machine (SVM), Electromyography (EMG), Root Mean Square (RMS), Otot Biceps, Sistem Pencegahan Cedera, Akurasi deteksi kelelahanAbstrak
Kelelahan adalah gejala umum yang dialami oleh banyak orang dan berhubungan dengan banyak kondisi kesehatan. Ini adalah kondisi dimana seseorang mengalami perasaan lemah, kekurangan energi yang signifikan dan merasa sulit untuk melakukan aktivitas sehari-hari. Kelelahan otot adalah penurunan kekuatan maksimal atau produksi tenaga karena aktivitas kontraktil, dan dapat disebabkan oleh berbagai gangguan neurologis, otot, kardiovaskular, penuaan, dan kelemahan. Tujuan dari penelitian ini adalah mengevaluasi tingkat akurasi pembacaan Myoware Muscle Sensor dalam membaca aktivitas otot untuk mendeteksi kelelahan otot. Evaluasi akurasi sensor dibutuhkan karena hasil pembacaan sensor akan langsung mempengaruhi validitas temuan serta efektivitas keseluruhan sistem yang digunakan pada penelitian ini. Hasil penelitian menunjukkan Myoware Muscle Sensor mampu membaca aktivitas otot biceps dengan baik sebesar 100%, mengandalkan elektromyografi (EMG) untuk mendeteksi sinyal listrik yang dihasilkan oleh kontraksi otot. Penggunaan algoritma Support Vector Machine (SVM) dengan fitur Root Mean Square (RMS) dalam sistem pendeteksi kelelahan otot biceps menunjukkan tingkat akurasi yang tinggi sebesar 84%. Waktu komputasi mikrokontroler dalam mengolah data EMG dan menjalankan algoritma SVM cukup efisien untuk kebutuhan real-time dengan rata-rata 650ms. Sistem keseluruhan yang dirancang untuk mendeteksi kelelahan otot, mulai dari sensor input, pemrosesan sinyal, hingga output hasil klasifikasi, 100% bekerja dengan baik dan sesuai harapan.
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Available at: https://udvabony.com/product/self-lock-3-pin-square-8x8x12mm-3pin-dip-0-1a/
Ülkir, O., Gökmen, G. & Kaplanoğlu, E., 2017. Emg Signal Classification Using Fuzzy Logic. BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 5(2), pp. 97-101.
Wang, S., Tang, H., Wang, B. & Mo, J., 2021. Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy. Biomedical Signal Processing and Control, pp. 1-9.
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Widasari, E. R., Miyauchi, R., Tamura, H. & Tanno, K., 2015. A Wireless Surface Electromyogram Monitoring System Using Smartphone and Its Application to Maintain Biceps Muscle. IEEE International Conference on Systems, Man, and Cybernetics, pp. 2378-2383.
Ye, S., Zhang, Y. & Yu, P., 2019. Applications of titanium in the electronic industry. Titanium for Consumer Applications, pp. 269-278.
Yousif, H. A. et al., 2019. Assessment of Muscles Fatigue Based on Surface EMG Signals Using Machine Learning and Statistical Approaches:. Materials Science and Engineering, pp. 1-9.
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