Garcia Sofia, Ortega Ethan, Alghamaz Mohammad, Ibrahim Alwathiqbellah, Chong En-Tze
UT Tyler Academy, The University of Texas at Tyler, 3900 University Blvd., Tyler, TX 75799, USA.
Department of Mechanical Engineering, The University of Texas at Tyler, 3900 University Blvd., Tyler, TX 75799, USA.
Micromachines (Basel). 2025 Jun 30;16(7):775. doi: 10.3390/mi16070775.
This study presents a self-powered smart wrist brace integrated with a piezoelectric sensor for real-time biomechanical monitoring during weightlifting activities. The system was designed to quantify wrist flexion across multiple loading conditions (0 kg, 0.5 kg, and 1.0 kg), leveraging mechanical strain-induced voltage generation to capture angular displacement. A flexible PVDF film was embedded within a custom-fitted wrist brace and tested on male and female participants performing controlled wrist flexion. The resulting voltage signals were analyzed to extract root-mean-square (RMS) outputs, calibration curves, and sensitivity metrics. To interpret the experimental results analytically, a lumped-parameter cantilever beam model was developed, linking wrist flexion angles to piezoelectric voltage output based on mechanical deformation theory. The model assumed a linear relationship between wrist angle and induced strain, enabling theoretical voltage prediction through simplified material and geometric parameters. Model-predicted voltage responses were compared with experimental measurements, demonstrating a good agreement and validating the mechanical-electrical coupling approach. Experimental results revealed consistent voltage increases with both wrist angle and applied load, and regression analysis demonstrated strong linear or mildly nonlinear fits with high R2 values (up to 0.994) across all conditions. Furthermore, surface plots and strain sensitivity analyses highlighted the system's responsiveness to simultaneous angular and loading changes. These findings validate the smart wrist brace as a reliable, low-power biomechanical monitoring tool, with promising applications in injury prevention, rehabilitation, and real-time athletic performance feedback.
本研究展示了一种集成压电传感器的自供电智能腕部护具,用于在举重活动期间进行实时生物力学监测。该系统旨在量化多种负载条件(0千克、0.5千克和1.0千克)下的腕部弯曲情况,利用机械应变诱导电压产生来捕捉角位移。将柔性聚偏氟乙烯(PVDF)薄膜嵌入定制的腕部护具中,并在进行受控腕部弯曲的男性和女性参与者身上进行测试。对产生的电压信号进行分析,以提取均方根(RMS)输出、校准曲线和灵敏度指标。为了从分析角度解释实验结果,基于机械变形理论开发了一个集总参数悬臂梁模型,将腕部弯曲角度与压电电压输出联系起来。该模型假设腕部角度与诱导应变之间存在线性关系,通过简化的材料和几何参数实现理论电压预测。将模型预测的电压响应与实验测量结果进行比较,显示出良好的一致性,并验证了机电耦合方法。实验结果表明,随着腕部角度和施加负载的增加,电压持续升高,回归分析表明在所有条件下均具有很强的线性或轻度非线性拟合,R2值很高(高达0.994)。此外,表面图和应变敏感性分析突出了该系统对角度和负载同时变化的响应能力。这些发现验证了智能腕部护具作为一种可靠的、低功耗生物力学监测工具的有效性,在预防损伤、康复和实时运动表现反馈方面具有广阔的应用前景。