Motor Learning, Assistive and Rehabilitation Robotics Lab, Robotics, Brain and Cognitive Sciences unit, Istituto Italiano di Tecnologia, Genoa, Italy.
Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS),University of Genoa, Genoa, Italy.
J Neuroeng Rehabil. 2018 Dec 17;15(1):119. doi: 10.1186/s12984-018-0463-y.
Several neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.
40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects' effort throughout the task, the energy consumption was estimated.
From the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.
The robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.
几种神经肌肉疾病都以肌肉疲劳为典型症状。因此,一种可靠的疲劳评估方法对于了解特定疾病特征随时间的演变以及制定有效的康复策略可能至关重要。不幸的是,尽管其重要性,临床实践中缺乏标准化、可靠和客观的疲劳测量方法,本工作即探讨了一种实用的解决方案。
40 名健康的年轻成年人使用机器人操纵器进行触觉到达任务。要求受试者在粘性弹性阻力力场中尽可能多次地进行腕部弯曲和伸展运动,直到测量的肌肉(主要是桡侧腕屈肌和伸肌)出现疲劳迹象。为了分析两种肌肉的行为和特征,将受试者分为两组:在第一组中,机器人仅在弯曲运动期间施加阻力,而在第二组中,仅在伸展运动期间施加阻力。采集了桡侧腕屈肌和伸肌的表面肌电图(sEMG)信号。提出并评估了一种新的指标,即均方频率来定义疲劳发作(OF)。此外,作为整个任务中受试者努力的衡量标准,估计了能量消耗。
从任务开始到结束,正如预期的那样,所有受试者在参与抵抗力量运动的肌肉的均方频率都有所下降。对于 OF 指标,受试者在疲劳时间上具有一致性;此外,伸肌和屈肌的 OF 时间相似。代谢分析显示能量消耗非常低,从行为角度来看,该测试被受试者很好地耐受。
本研究提出的机器人辅助评估测试被证明是一种简单易行、快速可靠的方法,可用于客观测量健康人群的肌肉疲劳。这项工作为评估开发了一个框架,可以在患有神经肌肉疾病的患者中进行临床实践。考虑到低代谢需求,临床人群可能会很好地耐受所要求的努力。