Wen Yuzhang, Sun Fengxin, Xie Zhenning, Zhang Mengqi, An Zida, Liu Bing, Sun Yuning, Wang Fei, Mao Yupeng
Physical Education Department, Northeastern University, Shenyang 110819, China.
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.
iScience. 2024 Mar 29;27(4):109615. doi: 10.1016/j.isci.2024.109615. eCollection 2024 Apr 19.
In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultra-fast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K-nearest neighbor), good classification results are achieved for ground-jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare.
在智能时代，基于传感器单元的大数据分析在智能运动中至关重要。在本研究中，开发了一种基于可回收柔性摩擦电纳米发电机（RF-TENG）传感器模块、数据处理硬件模块和上位机智能分析模块的舞蹈运动与损伤监测系统（DIMS），以促进智能运动。所得的RF-TENG具有17毫秒的超快响应时间，在超过4200个操作循环中表现出强大的稳定性，输出电压变化6%。DIMS通过提供运动状态的视觉反馈并与虚拟游戏交互，实现沉浸式训练。结合机器学习（K近邻），对地面跳跃技术取得了良好的分类结果。此外，它在运动损伤预测（即脚踝扭伤、膝盖过度伸展）方面显示出一些潜力。总体而言，本研究设计的传感系统在智能运动和医疗保健的未来应用中具有广阔前景。
