Deteksi Gerakan Tangan menggunakan Support Vector Machine pada Dumbbell Berbasis Raspberry Pi Zero
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ABSTRAK
Olahraga yang dilakukan di mana saja, dapat menggunakan alat sederhana seperti dumbbell. Latihan yang dilakukan harus sesuai usia dan kondisi kesehatan secara umum. Sebuah sistem diperlukan untuk mendeteksi gerakan sehingga membantu pemakai dalam menggunakan dumbbell. Dumbbell dilengkapi oleh sensor Inertial Measurement Unit (IMU), single board computer Raspberry Pi Zero W, dan LED RGB. Gerakan akan dideteksi oleh sensor Inertial Measurement Unit (IMU) yang dikirim ke Raspberry Pi Zero W untuk dilakukan preprocessing data. Algoritma Support Vector Machine (SVM) digunakan untuk mendapatkan model pendeteksi gerakan olahraga pada dumbbell. Bila gerakan terdeteksi maka Raspberry Pi Zero W akan memberi perintah LED RGB untuk menyalakan warna tertentu sesuai dengan deteksi gerakan. Berdasarkan hasil uji coba yang dilakukan kepada 5 orang dengan gerakan yang dideteksi sebanyak 6 gerakan, dumbbell pendeteksi gerakan memiliki tingkat keberhasilan sebesar 90%-94%.
Kata kunci: dumbbell, deteksi gerakan, Raspberry Pi Zero W, SVM
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ABSTRACT
Exercises that can be done anywhere, can use simple tools such as dumbbells. Exercises should be appropriate for age and general health conditions. A system is needed to detect motion so as to assist the wearer in using the dumbbells. The dumbbell is equipped with an Inertial Measurement Unit (IMU) sensor, a single board computer Raspberry Pi Zero W, and RGB LEDs. Movement will be detected by the Inertial Measurement Unit (IMU) sensor which is sent to the Raspberry Pi Zero W for preprocessing the data. The Support Vector Machine (SVM) algorithm is used to obtain a sports motion detection model on dumbbells. If motion is detected, the Raspberry Pi Zero W will give an RGB LED command to turn on certain colors according to motion detection. Based on the results of trials conducted on 5 people with 6 movements detected, motion detection dumbbells have a success rate of 90%-94%.
Keywords: dumbbell, motion detection, Raspberry Pi Zero W, SVM
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Douglass, M. J. J. (2020). Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition by Aurélien Géron. In Physical and Engineering Sciences in Medicine (Vol. 43, Nomor 3). https://doi.org/10.1007/s13246-020-00913-z
Fisher, J. P., Steele, J., Gentil, P., Giessing, J., & Westcott, W. L. (2017). A minimal dose approach to resistance training for the older adult; the prophylactic for aging. Experimental Gerontology, 99(September), 80–86. https://doi.org/10.1016/j.exger.2017.09.012
Kumar, G., Banerjee, R., Kr Singh, D., Choubey, N., & Arnaw. (2020). Mathematics for Machine Learning. Journal of Mathematical Sciences & Computational Mathematics, 1(2), 229–238. https://doi.org/10.15864/jmscm.1208
Marom, N. D., Rokach, L., & Shmilovici, A. (2010). Using the confusion matrix for improving ensemble classifiers. 2010 IEEE 26th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2010, 555–559. https://doi.org/10.1109/EEEI.2010.5662159
Medar, R., Rajpurohit, V. S., & Rashmi, B. (2018). Impact of Training and Testing Data Splits on Accuracy of Time Series Forecasting in Machine Learning. 2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017, 1–6. https://doi.org/10.1109/ICCUBEA.2017.8463779
Nasrulloh, A., Prasetyo, Y., & Apriyanto, K. D. (2018). Dasar-dasar Latihan Beban. In UNY Press. https://docplayer.info/163394993-Dasar-dasar-latihan-beban-ahmad-nasrulloh-yudik-prasetyo-krisnanda-dwi-apriyanto.html
Saloman, J.-B. (2013). Probability and Statistics (Fourth Edition). In Chance, 26(3). https://doi.org/10.1080/09332480.2013.845457
Sam, K. M., & Chatwin, C. R. (2015). Evaluating the Effectiveness of Online Product Planning and Layout Tools in Online Apparel Shopping. In Proceedings of the 2015 IEEE IEEM Perceived, (pp. 635–639).
Sartika, E. M., Gany, A., & Yuvens, V. (2020). Implementasi Sensor IMU untuk mengetahui Sudut Elevasi Kendaraan menggunakan Metode Least Square. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8(2), 301. https://doi.org/10.26760/elkomika.v8i2.301
Sensortec, B. (2016). BNO055: Intelligent 9-axis absolute orientation sensor. https://aebst.resource.bosch.com/media/_tech/media/datasheets/BST_BNO055_ DS000_14.pdf
Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. V. (2017). A review of machine learning techniques using decision tree and support vector machine. Proceedings - 2nd International Conference on Computing, Communication, Control and Automation, ICCUBEA 2016. https://doi.org/10.1109/ICCUBEA.2016.7860040
Sukvichai, K., Uthaisang, P., Chuengsutthiwong, P., & Maolanon, P. (2018). Hidden Dot Patterns Recognition using CNNs on Raspberry Pi Zero W. 2018 International Conference on Embedded Systems and Intelligent Technology and International Conference on Information and Communication Technology for Embedded Systems, ICESIT-ICICTES 2018, (pp. 1–5). https://doi.org/10.1109/ICESIT-ICICTES.2018.8442050
Tanaka, M., Horiuchi, T., & Tominaga, S. (2011). Color control of a lighting system using RGBW LEDs. Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications, 7866, 78660W. https://doi.org/10.1117/12.872374
Velloso, E., Bulling, A., Gellersen, H., Ugulino, W., & Fuks, H. (2013). Qualitative activity recognition of weight lifting exercises. ACM International Conference Proceeding Series, December 2014, (pp. 116–123). https://doi.org/10.1145/2459236.2459256
Wang, F., Yan, L., & Xiao, J. (2019). Human gait recognition system based on support vector machine algorithm and using wearable sensors. Sensors and Materials, 31(4), 1335–1349. https://doi.org/10.18494/SAM.2019.2288
Wang, Y., Zhao, Y., Xia, G., Qiu, A., Shi, Q., & Liu, R. (2018). A Scale Factor Self-Calibration Method for a Batch of MEMS Gyroscopes Based on Virtual Coriolis Force. 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology , (pp. 1–3). https://doi.org/10.1109/ICSICT.2018.8564926
DOI: https://doi.org/10.26760/elkomika.v10i1.105
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