Predicting Hand Grip Force Based on Muscle Electromyographic Activity Using Artificial Intelligence and Neural Networks

Yıl 2024, Cilt: 7 Sayı: (Special Issue 2): The Second International Scientific Conference: Sports for Health and Sustainable Development, (SHSD, 2024), 233 - 240, 20.05.2024

Jalal Abood Ammar Sameer Mohammed Safaa Ismaeel , Mohammed Hassan

https://doi.org/10.33438/ijdshs.1423907

Öz

This study aimed to establish predictive values for hand grip strength based on electromyographic activity while exploring disparities between measured and predicted grip strength among 12 proficient handball players. Grip strength was quantified using a specialized device recording Newton force in real-time at a 0.1-second sampling window, synchronized with muscle electromyographic activity (sEMG) recorded using the Noraxon myoMOTION technique. Various electromyographic parameters were assessed, including peak activity, root mean square, time to peak, and area under the curve. Grip strength measurements were taken at three stages (50%, 75%, 100%) and maintained for 3 seconds each. The data were analyzed using IBM Statistical software, implementing neural networks and artificial intelligence methods. The results revealed statistically insignificant differences between recorded and anticipated grip strength (p>0.05), indicating a high level of predictive accuracy. Minor disparities were observed, suggesting potential avenues for further investigation. This study contributes to our understanding of predictive modeling for grip strength and highlights the importance of electromyographic activity in assessing muscular performance.

Anahtar Kelimeler

EMG, AI, Handgrip force, Handball, Prediction force, Neural network.

Kaynakça

• Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2018). Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 28, 167–182. [CrossRef]
• Bonato, P., Roy, S. H., Knaflitz, M., & De Luca, C. J. (2001). Time frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Transactions on Biomedical Engineering, 48(7), 745–753. [CrossRef]
• Chaves, S. F., Pereira Marques, N., Lemos E Silva, R., Santos Rebouças, N., Monteiro De Freitas, L., Olavo, P., Lima, P., & Ribeiro De Oliveira, R. (2012). Neuromuscular efficiency of the vastus medialis obliquus and postural balance in professional soccer athletes after anterior cruciate ligament reconstruction. In Ligaments and Tendons Journal (Vol. 2, Issue 2).
• Duque, J., Masset, D., & Malchaire, J. (1995). Evaluation of handgrip force from EMG measurements. Applied Ergonomics, 26(1), 61–66. [CrossRef]
• Houglum, P. A. (2016). Exercise Musculoskeletal Injuries.
• Ismaeel, S. (n.d.). Differences in biomechanics and EMG variables at jump vs land phase during spike in volleyball . Ismaeel, S. A., & fenjan, falih. (2020). Special exercises using the strength training balanced rate according to some kinematic variables and their impact in the muscular balance and pull young weightlifters. International Journal of Psychosocial Rehabilitation, 24(1), 7612–7617.
• Ismaeel, S., Abdulwahab Ismaeel, S., Habib Kaddouri, R., & Ali Hassan, A. (2015). An analytical study of some kenmatical variables and summit of electrical activity of the striking arm muscles of the straight transmission in tennis. In The Swedish Journal of Scientific.
• Jan, M. M. S., Schwartz, M., & Benstead, T. J. (1999). EMG related anxiety and pain: A prospective study. Canadian Journal of Neurological Sciences, 26(4), 294–297. [CrossRef]
• Journal, I., Rehabilitation, P., Ismaeel, S. A., Fenjan, F. H., & Qadori, R. H. (2020). Biomechanical analysis of some variables and EMG of the muscles during the performance of the snatch lift in weightlifting. 24(05), 8234–8240.
• Kunc, V., Stulpa, M., & Feigl. (2019). Accessory flexor carpi ulnaris muscle with associated anterior interosseous artery variation: case report with the definition of a new type and review of concomitant variants. Surg Radiol Anat, 41, 1315–1318.
• Morales-Sánchez, V., Falcó, C., Hernández-Mendo, A., & Reigal, R. E. (2022). Efficacy of Electromyographic Biofeedback in Muscle Recovery after Meniscectomy in Soccer Players. [PubMed]
• Navarro, E., Chorro, D., Torres, G., Navandar, A., Rueda, J., & Veiga, S. (2021). Electromyographic Activity of Quadriceps and Hamstrings of a Professional Football Team During Bulgarian Squat and Lunge Exercises. Journal of Human Sport and Exercise, 16(3), 581–594. [CrossRef]
• Rufo, J. B., Callegari Ferreira, M. E., Camargo, B. L., & Rodrigues Martinho Fernandes, L. F. (2021). Changes in electromyographic activity of deltoid muscles in women with shoulder pain during a functional task. Journal of Bodywork and Movement Therapies, 27, 420–425. [CrossRef]
• Safaa Abdulwahab Ismaeel, A., Falih Hashim Fenjan, A., & Rafid Habib Qadori, L. (n.d.). Biomechanical analysis of some variables and EMG of the muscles during the performance of the snatch lift in weightlifting. International Journal of Psychosocial Rehabilitation.
• Scurr, J. C., Abbott, V., & Ball, N. (2011). Quadriceps EMG muscle activation during accurate soccer instep kicking. Journal of Sports Sciences, 29(3), 247–251. [CrossRef]
• Selvanayagam, V. S., Riek, S., & Carroll, T. J. (2012). A systematic method to quantify the presence of cross-talk in stimulus-evoked EMG responses: Implications for TMS studies. Journal of Applied Physiology, 112(2), 259–265. [CrossRef]
• Sidek, S. N., & Haja Mohideen, A. J. (2012). Mapping of EMG signal to hand grip force at varying wrist angles. 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012, 648–653. [CrossRef]
• Wang M, Zhao C, Barr A, Fan H, Yu S, Kapellusch J, Harris Adamson C. Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network. Hum Factors. 2023 May;65(3):382-402. doi: 10.1177/00187208211016695. Epub 2021 May 18. PMID: 34006135.
• Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O. W., & Li, G. (2023). Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition. Scientific Data, 10(1). [CrossRef]