Abstract
In a music game, a player interacts with a piece of music or a type of musical score. The progression of the game is based on the player’s actions in time with the music. Many music games have a called timing adjustment function, which optimizes the reaction of the user in advance by adjusting the timing of button presses according to the user’s reaction. This study utilizes myoelectric sensors to perform real-time timing adjustments using the degree of fatigue of the user’s muscles as an indicator during a music game. By utilizing the changes in myoelectricity when muscles become fatigued, the control of music games can be improved.
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References
Kanal, V., Brady, J., Nambiappan, H., Kyrarini, M., Wylie, G., Makedon, F.: Towards a serious game based human-robot framework for fatigue assessment. In: Proceedings of the 13th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA ’20). Association for Computing Machinery, New York, NY, USA, Article 74, 1–6. https://doi.org/10.1145/3389189.3398744
Asghari Oskoei, M., Hu, H., Gan, J.Q.: Manifestation of fatigue in myoelectric signals of dynamic contractions produced during playing PC games. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 315–318 (2008) https://ieeexplore.ieee.org/document/4649153
Merlo, A., Farina, D., Merletti, R.: A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Trans. Biomed. Eng. 50(3), 316–323 (2003). https://doi.org/10.1109/TBME.2003.808829
Schwartz, F.P., Nascimento, F.A.O., Bottaro, M., Celes, R.S.: The behavior of action potential conduction velocity on isokinetic knee extension tests. Annual Int. Conf. IEEE Eng. Med. Biol. 2010, 1348–1351 (2010). https://doi.org/10.1109/IEMBS.2010.5626751
Koutsos, E., Cretu, V., Georgiou, P.: A muscle fibre conduction velocity tracking ASIC for local fatigue monitoring. IEEE Trans. Biomed. Circuits Syst. 10(6), 1119–1128 (2016). https://doi.org/10.1109/TBCAS.2016.2520563
Farina, D., Pozzo, M., Merlo, E., Bottin, A., Merletti, R.: Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions. IEEE Trans. Biomed. Eng. 51(8), 1383–1393 (2004). https://doi.org/10.1109/TBME.2004.827556
Montoya, M., Henao, O., Muñoz, J.: Muscle fatigue detection through wearable sensors: a comparative study using the myo armband. In: Proceedings of the XVIII International Conference on Human Computer Interaction (Interacción ’17). Association for Computing Machinery, New York, NY, USA, Article 30, 1–2 (2004). https://doi.org/10.1145/3123818.3123855
MyoWare. www.sparkfun.com/products/18977 Accessed Apr 27 2022
Bansal, T., Khan, A.A.: Fatigue Assessment of Bicep Brachii Muscle Using Surface EMG Signals Obtained from Isometric Contraction. In: Proceedings of the 2020 8th International Conference on Communications and Broadband Networking (ICCBN ’20). Association for Computing Machinery, New York, NY, USA, 1–8 (2020). doi: 10.1145/3390525.3390533
Ebied, A., Awadallah, M., Abbass, M.A., El-Sharkawy, Y.: Upper limb muscle fatigue analysis using multi-channel surface EMG. In: 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), pp. 423–427 (2020) https://doi.org/10.1109/NILES50944.2020.9257909
Zhang, G., Morin, E., Zhang, Y., Etemad, S.A.: In:Non-invasive detection of low-level muscle fatigue using surface EMG with wavelet decomposition. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5648–5651 (2018). https://doi.org/10.1109/EMBC.2018.8513588
Zwarts, M.J., Bleijenberg, G., Van Engelen, B.G.M.: Clinical neurophysiology of fatigue. Clin. Neurophysiol. 119(1), 2–10 (2008). https://doi.org/10.1016/j.clinph.2007.09.126
Roberto, M., De Luca, C.J.: New techniques in surface electromyography. Comput. Aided Electromyograph. Expert Syst. 2, 115–124 (1989)
Auccahuasi, W., et al.: Analysis of a mechanism to evaluate upper limb muscle activity based on surface electromyography using the MYO-EMG device. In: Proceedings of the 5th International Conference on Communication and Information Processing (ICCIP ’19). Association for Computing Machinery, New York, NY, USA, 144–148 (2019). https://doi.org/10.1145/3369985.3370016
Miyata, H., Sadoyama, T., Katsuta, S.: Muscle fiber conduction velocity in human vastus lateralis during isometric contractions–relation to muscle fiber composition–. Jpn. J. Phys. Fitness Sports Med. 34(4), 231–238 (1985). https://doi.org/10.7600/jspfsm1949.34.231
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This work was supported by JSPS KAKENHI Grant Numbers 20K11780.
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Zhou, S., Segawa, N. (2022). Method for Music Game Control Using Myoelectric Sensors. In: Göbl, B., van der Spek, E., Baalsrud Hauge, J., McCall, R. (eds) Entertainment Computing – ICEC 2022. ICEC 2022. Lecture Notes in Computer Science, vol 13477. Springer, Cham. https://doi.org/10.1007/978-3-031-20212-4_19
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DOI: https://doi.org/10.1007/978-3-031-20212-4_19
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