通过肌电图可穿戴设备和主观用户报告进行身体疲劳检测:一种面向适应性康复的机器学习方法

Michalis Papakostas, Varun Kanal, M. Abujelala, K. Tsiakas, F. Makedon
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引用次数: 15

摘要

由于肌肉衰竭引起的身体疲劳是日常生活中非常常见的症状。然而,疲劳有时可以怀疑更严重的疾病,如多发性硬化症,需要适当评估。尽管需要监测疲劳,但由于涉及很大程度的主观性,以客观和可量化的方式描述疲劳仍然是一个悬而未决的问题。在这项工作中,我们提出了一种检测身体疲劳的新方法。我们基于客观的肌电图测量来设计我们的方法,我们的目标是根据主观的用户报告来识别身体疲劳的存在。基于我们的分析,我们强调了我们发现的重要性,并讨论了基于机器学习的建模如何对理解疲劳和设计适应性康复方案有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physical fatigue detection through EMG wearables and subjective user reports: a machine learning approach towards adaptive rehabilitation
Physical fatigue due to muscle exhaustion is a symptom that can be very common in daily life. However fatigue can sometimes be suspect of more severe diseases such as multiple sclerosis and needs to be assessed appropriately. Despite the need to monitor fatigue, describing it in an objective and quantifiable manner is still an open problem due to the great levels of subjectivity involved. In this work we propose a novel method towards detecting physical fatigue. We design our approach based on objective EMG measurements and we aim to identify the presence of physical fatigue based on subjective user-reports. Based on our analysis we highlight the significance of our findings and we discuss how machine learning based modeling can become useful towards understanding fatigue and designing adaptive rehabilitation scenarios.
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