Predicting Hand Grip Force Based on Muscle Electromyographic Activity Using Artificial Intelligence and Neural Networks
Abstract
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.
Keywords
EMG, AI, Handgrip force, Handball, Prediction force, Neural network.
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