Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil.
Federal Institute of Espírito Santo (IFES), Serra 29040-780, Brazil.
Sensors (Basel). 2023 Nov 20;23(22):9291. doi: 10.3390/s23229291.
Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.
肌肉疲劳被定义为在自愿收缩过程中维持最大力量的能力降低。它与影响执行重复活动的工人的肌肉骨骼疾病有关，影响他们的表现和幸福感。尽管肌电图仍然是测量肌肉疲劳的金标准，但它在长期工作中的局限性促使人们使用可穿戴设备。本文提出了一种使用可穿戴和非侵入性设备（如光纤传感器 (OFS) 和惯性测量单元 (IMU)）结合主观 Borg 量表估算肌肉疲劳的计算模型。肌电图 (EMG) 传感器用于观察它们在估计肌肉疲劳和比较不同传感器组合性能方面的重要性。本研究涉及 30 名受试者用优势臂进行重复举重活动，直到达到肌肉疲劳。测量肌肉活动、肘部角度以及角速度和线速度等，以提取多个特征。不同的机器学习算法获得了一个模型，该模型可以估计三种疲劳状态（低、中、高）。结果表明，在使用所有传感器 33 个特征的分类任务中，LightGBM 等机器学习分类器的准确率为 96.2%，而仅使用 OFS 和 IMU 传感器 13 个特征的准确率为 95.4%。这表明肘部角度、手腕速度、加速度变化和代偿性颈部运动对于估计肌肉疲劳至关重要。总之，所得到的模型可用于估计工作环境中重物搬运时的疲劳，有可能在长时间工作轮班期间监测和预防肌肉疲劳。
