Song Xingzhe, Li Hongshuai, Gao Wei
Department of Electrical and Computer Engineering, University of Pittsburgh, USA.
Department of Orthopaedic Surgery, University of Pittsburgh, USA.
Smart Health (Amst). 2021 Mar;19. doi: 10.1016/j.smhl.2020.100175. Epub 2020 Dec 15.
Muscle fatigue is common among humans and also a crucial indicator of many muscular diseases such as muscular dystrophy and disorders. Timely evaluation of muscle fatigue, hence, is important to track disease progress and avoid disease exacerbations. However, convenient tools for evaluating muscle fatigue out of clinic are still missing. In this paper, we present a new technique that uses commodity smartphones to evaluate muscle fatigue through simple and daily muscle exercises. The basic idea of our technique is to mimic an active sonar system with the smartphone's built-in microphone and speaker, and use this sonar system to evaluate muscle fatigue from the muscle's surface characteristics that can be measured from the transmitted acoustic signal. More specifically, our technique first measures the acoustic channel disturbances caused by fatigue-induced muscle tremor via channel estimation, and then derives quantitative fatigue levels from the variation of acoustic channel estimation. By using the arm bicep muscle as our primary target, we designed the exercise protocol and implemented a smartphone app for fatigue evaluation. Experiment results verified that our technique can precisely evaluate the speed of muscle fatigue accumulation, as well as identifying the actual fatigue occurrence. This technique, hence, could be used in practical home settings for effective fatigue evaluation on a daily basis.
肌肉疲劳在人类中很常见,也是许多肌肉疾病(如肌肉萎缩症和失调症)的关键指标。因此,及时评估肌肉疲劳对于跟踪疾病进展和避免疾病恶化很重要。然而,目前仍缺乏方便的门诊外肌肉疲劳评估工具。在本文中,我们提出了一种新技术,该技术利用普通智能手机通过简单的日常肌肉锻炼来评估肌肉疲劳。我们技术的基本思想是用智能手机的内置麦克风和扬声器模拟一个有源声纳系统,并利用这个声纳系统从可以从传输的声学信号中测量的肌肉表面特征来评估肌肉疲劳。更具体地说,我们的技术首先通过信道估计测量由疲劳引起的肌肉震颤所导致的声信道干扰,然后从声信道估计的变化中得出定量的疲劳水平。以肱二头肌作为主要目标,我们设计了锻炼方案并实现了一个用于疲劳评估的智能手机应用程序。实验结果证实,我们的技术可以精确评估肌肉疲劳积累的速度,以及识别实际的疲劳发生情况。因此,这项技术可用于实际家庭环境中,以便每天进行有效的疲劳评估。