Sanya Oceanographic Institution, College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.
Sensors (Basel). 2022 Dec 12;22(24):9739. doi: 10.3390/s22249739.
Rope jumping, as a fitness exercise recommended by many sports medicine practitioners, can improve cardiorespiratory capacity and physical coordination. Existing rope jump monitoring systems have limitations in terms of convenience, comfort, and exercise intensity evaluation. This paper presents a rope jump monitoring system using passive acoustic sensing. Our system exploits the off-the-shelf smartphone and headphones to capture the user's rope-jumping sound and breathing sound after exercise. Given the captured acoustic data, the system uses a short-time energy-based approach and the high correlation between rope jumping cycles to detect the rope-jumping sound frames, then applies a dual-threshold endpoint detection algorithm to calculate the number of rope jumps. Finally, our system performs regression predictions of exercise intensity based on features extracted from the jumping speed and the mel spectrograms of the user's breathing sound. The significant advantage of the system lies in the solution of the problem of poorly characterized mel spectrograms. We employ an attentive mechanism-based GAN to generate optimized breathing sound mel spectrograms and apply domain adversarial adaptive in the network to improve the migration capability of the system. Through extensive experiments, our system achieves (on average) 0.32 and 2.3% error rates for the rope jumping count and exercise intensity evaluation, respectively.
跳绳作为许多运动医学从业者推荐的健身运动，可以提高心肺能力和身体协调性。现有的跳绳监测系统在便利性、舒适性和运动强度评估方面存在局限性。本文提出了一种使用被动声学感知的跳绳监测系统。我们的系统利用现成的智能手机和耳机来捕捉用户跳绳后的声音和呼吸声。根据捕获到的声学数据，系统使用基于短时能量的方法和跳绳周期之间的高度相关性来检测跳绳声帧，然后应用双阈值端点检测算法来计算跳绳次数。最后，我们的系统根据从用户呼吸声的跳跃速度和梅尔频谱图中提取的特征对运动强度进行回归预测。该系统的显著优势在于解决了梅尔频谱图特征描述不佳的问题。我们采用基于注意机制的 GAN 来生成优化的呼吸声梅尔频谱图，并在网络中应用域对抗自适应来提高系统的迁移能力。通过广泛的实验，我们的系统在跳绳计数和运动强度评估方面的平均错误率分别为 0.32%和 2.3%。
