The Evaluation of Sports Performance in Tennis Based on Flexible Piezoresistive Pressure Sensing Technology
Kebao Zhang, Kehu Zhang, Liu Wang
Abstract
In recent years, significant research progress has been made in the field of flexible piezoresistive pressure sensors (FPPS) due to advancements in structure, materials, and manufacturing strategies. These sensors have found applications in various fields, including artificial intelligence (AI) and future sports. This study introduces MXene with conductive material and melamine sponge as substrate (MMSS, a type of FPPS) designed as a detection device to evaluate tennis performance. The device assesses four sports skills (forehand, backhand, serve, and volley) for elite and intermediate players during training and competition. Four indicators related to grip pressure exerted on the handle (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\mathbf {{1}}}$ </tex-math></inline-formula>: time difference between pressure occurrence and backswing time of lead racket, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\mathbf {{2}}}$ </tex-math></inline-formula>: time difference between pressure disappearance and end time of follow-through, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\mathbf {{3}}}$ </tex-math></inline-formula>: pressure duration time, I: pressure peak) are collected, analyzed, and identified by AI. Significant differences in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\mathbf {{1}}}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\mathbf {{2}}}$ </tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}_{\mathbf {{3}}}$ </tex-math></inline-formula> are observed between intermediate and elite groups (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${P} \lt 0.05$ </tex-math></inline-formula>), while I showed no significant difference (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${P} \gt 0.05$ </tex-math></inline-formula>). The AI recognition accuracy for the elite group in training and competition is 97.49%. The device demonstrates that improved sports performance corresponds to shorter force exertion time in a single hit, and greater flexibility in grip strength and hand relaxation in various situations. This research offers a new perspective for evaluating sports performance in hand-held equipment, events, and presents a feasible direction for addressing challenges in flexible wearable technology in practice.