Generalizing Perceived Fatigue Estimation Across Diverse Upper Limb Tasks Using Minimal Wearable Sensors

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Malik Muhammad Qirtas;Marco Sica;Merve Nur Yasar;Patricia O'Sullivan;Brendan O'Flynn;Salvatore Tedesco;Matteo Menolotto;Andrea Visentin
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引用次数: 0

Abstract

Accurately estimating perceived fatigue from wearable sensor data is a challenge, especially across diverse tasks. This letter presents a generalized framework to predict estimated fatigue scores (measured using the Borg scale) using combined electromyography and inertial measurement units data collected from two independent upper limb datasets. Our best model achieved a mean absolute error of 2.35 and a mean absolute percentage error of 18.60% using only five strategically placed sensors. A broad set of biomechanical features was extracted to capture both kinematic and neuromuscular indicators of fatigue. Vertical acceleration of the upper arm and shoulder, along with spectral features from deltoid EMG, emerged as the most consistent predictors across tasks. These findings support interpretable and generalizable fatigue detection and provide a foundation for real-time monitoring systems in sports, rehabilitation, and occupational health.
基于最小可穿戴传感器的不同上肢任务感知疲劳估计
从可穿戴传感器数据中准确估计感知疲劳是一个挑战,特别是在不同的任务中。这封信提出了一个通用的框架来预测估计的疲劳评分(使用博格量表测量),使用从两个独立的上肢数据集收集的肌电图和惯性测量单元数据。我们的最佳模型实现了2.35的平均绝对误差和18.60%的平均绝对百分比误差,仅使用5个策略性放置的传感器。提取了一组广泛的生物力学特征,以捕获疲劳的运动学和神经肌肉指标。上臂和肩部的垂直加速度,以及三角肌肌电图的频谱特征,是跨任务最一致的预测指标。这些发现支持了可解释和可推广的疲劳检测,并为运动、康复和职业健康的实时监测系统提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
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