Modeling the Relationship Between Electromyographic Activity and Grip Force Using AI: A Sports Biomechanics Approach

Rafid Habeb Qaduri; Noora Mohammed Mustafa; Safaa Abdulwahab Ismaeel; Ahmed Naseef  Jasim; Nawras Najeeb Ahmed

doi:10.47134/jpo.v3i1.2119

Authors

Rafid Habeb Qaduri Department of Physical Education, University of Diyala
Noora Mohammed Mustafa Department of Physical Education, University of Diyala
Safaa Abdulwahab Ismaeel Department of Sport Biomechanics, College of Sport Sciences, University of Diyala
Ahmed Naseef Jasim Al-Kut University, College of Physical Education and Sports Science
Nawras Najeeb Ahmed Diyala University, College of Basic Education

DOI:

https://doi.org/10.47134/jpo.v3i1.2119

Keywords:

Electromyography (EMG), Artificial Intelligence (AI), Handgrip Force, Handball, Prediction, Neural Networks

Abstract

This study aimed to develop a predictive model of hand grip strength based on electromyographic (EMG) signals and to examine the variance between predicted and actual grip force values. The research involved a sample of 12 advanced-level handball players with verified medical histories. Grip strength was assessed using a customized device capable of capturing force output in Newtons at 0.1-second intervals, synchronized with EMG data recorded via the Noraxon myoMOTION system (400 Hz, 8 channels). Key EMG metrics analyzed included peak amplitude, root mean square (RMS), and time to peak activation, among others. Participants were tested at three intensity levels (50%, 75%, and 100%), each sustained for 3 seconds. Data analysis and variable selection were conducted using IBM statistical tools and an Artificial Neural Network (ANN) model specifically designed to predict grip strength based on EMG features. Findings indicated that, although the predicted grip strength values closely mirrored the actual readings, the minor differences observed were not statistically significant.

References

Ahmed, M. (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. 24(01), 7612–7617.

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. https://doi.org/10.1016/J.JOCM.2018.07.002 DOI: https://doi.org/10.1016/j.jocm.2018.07.002

Amiri-Khorasani, M., Abu Osman, N. A., & Yusof, A. (2010). Electromyography assessments of the vastus medialis muscle during soccer instep kicking between dynamic and static stretching. Journal of Human Kinetics, 24(1), 35–41. https://doi.org/10.2478/v10078-010-0017-2 DOI: https://doi.org/10.2478/v10078-010-0017-2

Cara, R., Dhoska, K., Cara, F., Kayusi, F., & Juma, L. (2025). An Artificial Intelligence Model for Predicting Hospital Readmission Using Electronic Health Records Data. Mesopotamian Journal of Artificial Intelligence in Healthcare, 2025, 116–123. DOI: https://doi.org/10.58496/MJAIH/2025/012

Chuang, L. L., Wu, C. Y., Lin, K. C., & Lur, S. Y. (2012). Quantitative mechanical properties of the relaxed biceps and triceps brachii muscles in patients with subacute stroke: A reliability study of the Myoton-3 myometer. Stroke Research and Treatment. https://doi.org/10.1155/2012/617694 DOI: https://doi.org/10.1155/2012/617694

Dos, T., Santos, ’, Thomas, · Christopher, Comfort, P., & Jones, P. A. (2019). The Effect of Training Interventions on Change of Direction Biomechanics Associated with Increased Anterior Cruciate Ligament Loading: A Scoping Review. 49, 1837–1859. https://doi.org/10.1007/s40279-019-01171-0 DOI: https://doi.org/10.1007/s40279-019-01171-0

El-Kenawy, E.-S. M., Eid, M. M., Elshewey, A. M., & Osman, A. M. (2024). An optimized Framework for kidney disease Detection and Prediction Using DL Techniques. Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024, 118–127. DOI: https://doi.org/10.58496/MJAIH/2024/014

Elliott, B. (1999). Biomechanics: An integral part of sport science and sport medicine. Journal of Science and Medicine in Sport, 2(4), 299–310. https://doi.org/10.1016/S1440-2440(99)80003-6 DOI: https://doi.org/10.1016/S1440-2440(99)80003-6

Gabriel, D. A., Christie, A., Inglis, J. G., & Kamen, G. (2011). Experimental and modelling investigation of surface EMG spike analysis. Medical Engineering and Physics, 33(4), 427–437. https://doi.org/10.1016/j.medengphy.2010.11.010 DOI: https://doi.org/10.1016/j.medengphy.2010.11.010

Hikmat Salman Lect Turki Hilal Kadhim, S., & Abdel Wahab Ismail Alaa Khalaf Haider, S. (2023). THE EFFECT OF SPECIFIC EXERCISES ACCORDING TO THE PLAYING AREAS IN DEVELOPING THE SKILLFUL AND TACTICAL PERFORMANCE OF THE PASSING SKILL IN FUTSAL. In International Journal of Central (Vol. 19, Issue 1).

Hoelbling, D., Grafinger, M., Smiech, M. M., Cizmic, D., Dabnichki, P., & Baca, A. (2024). Acute response on general and sport specific hip joint flexibility to training with novel sport device. Sports Biomechanics, 23(9), 1107–1122. https://doi.org/10.1080/14763141.2021.1922742 DOI: https://doi.org/10.1080/14763141.2021.1922742

Hosseini, E., Daneshjoo, A., Sahebozamani, M., & Behm, D. (2024). The effects of fatigue on knee kinematics during unanticipated change of direction in adolescent girl athletes: a comparison between dominant and non-dominant legs. Sports Biomechanics, 23(9), 1219–1228. https://doi.org/10.1080/14763141.2021.1925732 DOI: https://doi.org/10.1080/14763141.2021.1925732

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 Research (Vol. 2). www.sjsr.se

Kavitha, T., Amirthayogam, G., Hephzipah, J. J., Suganthi, R., & Chelladurai, T. (2024). Healthcare analysis based on diabetes prediction using a cuckoo-based deep convolutional long-term memory algorithm. Babylonian Journal of Artificial Intelligence, 2024, 64–72. DOI: https://doi.org/10.58496/BJAI/2024/009

Knebel, A., Daher, M., Singh, M., Fisher, L., Daniels, A. H., & Diebo, B. G. (2024). Sagittal Spinal Alignment Measurements and Evaluation: Historical Perspective. North American Spine Society Journal (NASSJ), 100519. https://doi.org/10.1016/j.xnsj.2024.100519 DOI: https://doi.org/10.1016/j.xnsj.2024.100519

Komissarov, S. S. (2022). Dynamics of carving runs in alpine skiing. I. The basic centrifugal pendulum. Sports Biomechanics, 21(8), 890–911. https://doi.org/10.1080/14763141.2019.1710559 DOI: https://doi.org/10.1080/14763141.2019.1710559

Lee, J., & Yang, C. (2022). Deep neural network and meta-learning-based reactive sputtering with small data sample counts. Journal of Manufacturing Systems, 62, 703–717. https://doi.org/10.1016/J.JMSY.2022.02.004 DOI: https://doi.org/10.1016/j.jmsy.2022.02.004

Lindstedt, S. L. (2016). Skeletal muscle tissue in movement and health: Positives and negatives. Journal of Experimental Biology, 219(2), 183–188. https://doi.org/10.1242/jeb.124297 DOI: https://doi.org/10.1242/jeb.124297

Megahed, M., & Tarek, Z. . (2023). Improving spatio-temporal stride parameters, lower limb muscles activity and race walkers’ records after 12-weeks special exercises using rhythmic auditory. SPORT TK-EuroAmerican Journal of Sport Sciences, 12, 7. https://doi.org/10.6018/sportk.506961 DOI: https://doi.org/10.6018/sportk.506961

Miletić, A., Kezić, A., & Kalinski, S. D. (2023). IDENTIFICATION OF TOPOLOGICALLY CHARACTERISTIC MUSCULOSKELETAL PAIN OCCURRENCE AMONG YOUNG FEMALE ATHLETES. Science of Gymnastics Journal, 15(2), 191–204. https://doi.org/10.52165/sgj.15.2.191-204 DOI: https://doi.org/10.52165/sgj.15.2.191-204

Mohamed Saleh, A., Mohamed Badaway, H., Mahgoub Mostafa, M. S. E., & Hesham Abouelenein, M. (2024). Effect of different cranio-cervical positions on abdominal and back muscles activities from different postures: A cross-section study. SPORT TK-EuroAmerican Journal of Sport Sciences, 13, 2. https://doi.org/10.6018/sportk.603821 DOI: https://doi.org/10.6018/sportk.603821

Morales-Sánchez, V., Falcó, C., Hernández-Mendo, A., & Reigal, R. E. (2022). Efficacy of Electromyographic Biofeedback in Muscle Recovery after Meniscectomy in Soccer Players. https://doi.org/10.3390/s22114024 DOI: https://doi.org/10.3390/s22114024

Nema, N. S., & Ismaeel, S. A. (2022). THE EFFECT OF SPECIAL EXERCISES IN LEARNING SOME BASIC SKILLS IN VOLLEYBALL AND SOME BIOMECHANICAL VARIABLES ACCORDING TO MAGNETIC RESONANCE MEASUREMENTS OF THE UPPER LIMBS. Revista Iberoamericana de Psicologia Del Ejercicio y El Deporte, 17(3), 143–145. DOI: https://doi.org/10.38005/0880-000-997-001

Rice, P. E., Nishikawa, K., & Nimphius, S. (2024). Strength and power capabilities predict weighted parameter ranking of saut de chat leaping performance in dancers. Sports Biomechanics, 23(9), 1176–1192. https://doi.org/10.1080/14763141.2021.1933580 DOI: https://doi.org/10.1080/14763141.2021.1933580

Rigoni, I., Bonci, T., Bifulco, P., & Fratini, A. (2022). Characterisation of the transient mechanical response and the electromyographical activation of lower leg muscles in whole body vibration training. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-10137-8 DOI: https://doi.org/10.1038/s41598-022-10137-8

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, 24, 2020.

Sami Elsaharty, K., Hamed Elmarakby, S. M., Nabil Alagamy, N., Ibrahim, M. G., Abdelnabi Mousa, N. M., & Hussien, R. N. (2024). Impact of handedness on electromyographic activity of hand muscles and nerve conduction velocity during mobile phone use in adolescents. SPORT TK-EuroAmerican Journal of Sport Sciences, 13, 54. https://doi.org/10.6018/sportk.637271 DOI: https://doi.org/10.6018/sportk.637271

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. https://doi.org/10.1152/japplphysiol.00558.2011 DOI: https://doi.org/10.1152/japplphysiol.00558.2011

Sheela, M. S., Amirthayogam, G., Hephzipah, J. J., Gopalakrishnan, S., & Chand, S. R. (2024). Machine learning based lung disease prediction using convolutional neural network algorithm. Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024, 50–58. DOI: https://doi.org/10.58496/MJAIH/2024/008

Shirzadi, M., Mirshamsi, A., Esrefoglu, A., Rojas-Martinez, M., Marateb, H. R., Angel Mananas, M., & Vieira, T. (2023). A new force profile signal for a convex solution of muscle force estimation from electromyographic signals. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2023, 1–4. https://doi.org/10.1109/EMBC40787.2023.10340594 DOI: https://doi.org/10.1109/EMBC40787.2023.10340594

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. https://doi.org/10.1109/IECBES.2012.6498069 DOI: https://doi.org/10.1109/IECBES.2012.6498069

Wight, J. T., Garman, J. E. J., Hooper, D. R., Robertson, C. T., Ferber, R., & Boling, M. C. (2022). Distance running stride-to-stride variability for sagittal plane joint angles. Sports Biomechanics, 21(8), 966–980. https://doi.org/10.1080/14763141.2020.1713207 DOI: https://doi.org/10.1080/14763141.2020.1713207

Wu, C., Yan, Y., Cao, Q., Fei, F., Yang, D., Lu, X., Xu, B., Zeng, H., & Song, A. (2020). SEMG Measurement Position and Feature Optimization Strategy for Gesture Recognition Based on ANOVA and Neural Networks. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2982405 DOI: https://doi.org/10.1109/ACCESS.2020.2982405