Sistem Deteksi Wearable pada Training Angkat Beban untuk Standing Dumbbell Curl dengan Rekognisi Sequence Motion Menggunakan Arsitektur LSTM
Abstrak
Naskah ini akan diterbitkan di International Journal of Computer Applications in Technology
Referensi
Schoenfeld, B. J., Grgic, J., Ogborn, D., & Krieger, J. W. (2017). "Strength and Hypertrophy Adaptations Between Low- vs. High-Load Resistance Training: A Systematic Review and Meta-analysis." Journal of Strength and Conditioning Research, 31(12), 3508–3523.
Hochreiter, S., & Schmidhuber, J. (1997). "Long Short-Term Memory." Neural Computation, 9(8), 1735–1780.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). "Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change." JAMA, 313(5), 459–460.
Muzayyin, A., Syauqy, D., & Putri, R. R. M. . (2024). Sistem Bantu Wearable pada Latihan Angkat Beban untuk Otot Biceps menggunakan Sensor MPU6050 dengan Metode Random Forest. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 8(14).
Jmal, A., Barioul, R., Meddeb Makhlouf, A., Fakhfakh, A., Kanoun, O. (2020). An Embedded ANN Raspberry PI for Inertial Sensor Based Human Activity Recognition. In: Jmaiel, M., Mokhtari, M., Abdulrazak, B., Aloulou, H., Kallel, S. (eds) The Impact of Digital Technologies on Public Health in Developed and Developing Countries. ICOST 2020. Lecture Notes in Computer Science(), vol 12157. Springer, Cham.
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2015). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
Zhang, Z., Lv, Z., Gan, C., & Zhu, Q. (2020). Human Action Recognition using Convolutional LSTM and Fully-Connected LSTM with Different Attentions. Neurocomputing, 410, 304–316.
Math. StudySmarter UK. (2024) https://www.studysmarter.co.uk/explanations/math/calculus/dynamical-systems/#:~:text=Dynamical%20systems%20provide%20a%20mathematical,systems%20are%20vast%20and%20varied.
Milenkoski, M., Trivodaliev, K., Kalajdziski, S., Jovanov, M., & Stojkoska, B. R. (2018). Real time human activity recognition on smartphones using LSTM Networks. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).
Urazayev D., Eduard A., Ahsan M., & Zorbas D. (2023). Indoor Performance Evaluation of ESP-NOW.
Smagulova K. & James, A. (2019). A Survey on LSTM Memristive Neural Network Architectures and Applications. The European Physical Journal Special Topics. 228.
Pasic, R., Kuzmanov, I., & Atanasovski, K. (2021). ESP-NOW communication protocol with ESP32. Journal of Universal Excellence, 6(1), 53-60.
Nakashima, M., Ohgi, Y., Akiyama, E., & Kazami, N. (2010). Development of a swimming motion display system for athlete swimmers’ training using a wristwatch-style acceleration and gyroscopic sensor device. Procedia Engineering, 2(2), 3035-3040.
Pyun, K. R., Kwon, K., Yoo, M. J., Kim, K. K., Gong, D., Yeo, W. H., & Ko, S. H. (2024). Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications. National Science Review, 11(2), nwad298.
Seçkin, A. Ç., Ateş, B., & Seçkin, M. (2023). Review on Wearable Technology in sports: Concepts, Challenges and opportunities. Applied Sciences, 13(18), 10399.
Coratella, G., Tornatore, G., Longo, S., Toninelli, N., Padovan, R., Esposito, F., & Cè, E. (2023). Biceps Brachii and Brachioradialis Excitation in Biceps Curl Exercise: Different Handgrips, Different Synergy. Sports, 11(3), 64.
Haff, G., & Triplett, N. T. (2016). National Strength & Conditioning Association (US). Essentials of strength training and conditioning, 4.
Schoenfeld, B. J., Grgic, J., Ogborn, D., & Krieger, J. W. (2017). "Strength and Hypertrophy Adaptations Between Low- vs. High-Load Resistance Training: A Systematic Review and Meta-analysis." Journal of Strength and Conditioning Research, 31(12), 3508–3523.
Hochreiter, S., & Schmidhuber, J. (1997). "Long Short-Term Memory." Neural Computation, 9(8), 1735–1780.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). "Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change." JAMA, 313(5), 459–460.
Muzayyin, A., Syauqy, D., & Putri, R. R. M. . (2024). Sistem Bantu Wearable pada Latihan Angkat Beban untuk Otot Biceps menggunakan Sensor MPU6050 dengan Metode Random Forest. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 8(14).
Jmal, A., Barioul, R., Meddeb Makhlouf, A., Fakhfakh, A., Kanoun, O. (2020). An Embedded ANN Raspberry PI for Inertial Sensor Based Human Activity Recognition. In: Jmaiel, M., Mokhtari, M., Abdulrazak, B., Aloulou, H., Kallel, S. (eds) The Impact of Digital Technologies on Public Health in Developed and Developing Countries. ICOST 2020. Lecture Notes in Computer Science(), vol 12157. Springer, Cham.
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2015). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
Zhang, Z., Lv, Z., Gan, C., & Zhu, Q. (2020). Human Action Recognition using Convolutional LSTM and Fully-Connected LSTM with Different Attentions. Neurocomputing, 410, 304–316.
Math. StudySmarter UK. (2024) https://www.studysmarter.co.uk/explanations/math/calculus/dynamical-systems/#:~:text=Dynamical%20systems%20provide%20a%20mathematical,systems%20are%20vast%20and%20varied.
Milenkoski, M., Trivodaliev, K., Kalajdziski, S., Jovanov, M., & Stojkoska, B. R. (2018). Real time human activity recognition on smartphones using LSTM Networks. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).
Urazayev D., Eduard A., Ahsan M., & Zorbas D. (2023). Indoor Performance Evaluation of ESP-NOW.
Smagulova K. & James, A. (2019). A Survey on LSTM Memristive Neural Network Architectures and Applications. The European Physical Journal Special Topics. 228.
Pasic, R., Kuzmanov, I., & Atanasovski, K. (2021). ESP-NOW communication protocol with ESP32. Journal of Universal Excellence, 6(1), 53-60.
Nakashima, M., Ohgi, Y., Akiyama, E., & Kazami, N. (2010). Development of a swimming motion display system for athlete swimmers’ training using a wristwatch-style acceleration and gyroscopic sensor device. Procedia Engineering, 2(2), 3035-3040.
Pyun, K. R., Kwon, K., Yoo, M. J., Kim, K. K., Gong, D., Yeo, W. H., & Ko, S. H. (2024). Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications. National Science Review, 11(2), nwad298.
Seçkin, A. Ç., Ateş, B., & Seçkin, M. (2023). Review on Wearable Technology in sports: Concepts, Challenges and opportunities. Applied Sciences, 13(18), 10399.
Coratella, G., Tornatore, G., Longo, S., Toninelli, N., Padovan, R., Esposito, F., & Cè, E. (2023). Biceps Brachii and Brachioradialis Excitation in Biceps Curl Exercise: Different Handgrips, Different Synergy. Sports, 11(3), 64.
Haff, G., & Triplett, N. T. (2016). National Strength & Conditioning Association (US). Essentials of strength training and conditioning, 4.
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