Baghdadi Amir, Megahed Fadel M, Esfahani Ehsan T, Cavuoto Lora A
a Department of Industrial and Systems Engineering , University at Buffalo, The State University of New York , Buffalo , NY , USA.
b Department of Mechanical and Aerospace Engineering , University at Buffalo, The State University of New York , Buffalo , NY , USA.
Ergonomics. 2018 Aug;61(8):1116-1129. doi: 10.1080/00140139.2018.1442936. Epub 2018 Mar 2.
The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities. Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.
本研究的目的是提供一种方法，用于对手动搬运物料后的非疲劳状态与疲劳状态进行分类。将用于特征提取的模板匹配模式识别方法（$1识别器）以及用于分类的支持向量机模型应用于通过我们基于逐步搜索的分割算法分割出的步态周期运动学数据。使用了脚踝上的单个惯性测量单元，为监测提供了一种侵入性最小且成本低廉的工具。分类器利用识别器基于距离的得分和步长持续时间来区分不同状态。疲劳检测结果显示，在20名招募受试者的数据中，准确率达到90%。该方法仅利用来自一个低成本传感器的最少数据量和特征，通过一种简单的机器学习算法，可靠地对由实际制造任务引起的疲劳状态进行分类，并且作为一种未来将在制造设施中应用的技术，可以扩展到实时疲劳监测。从业者总结：我们基于模拟的手动物料搬运任务，研究了使用可穿戴传感器检测与疲劳相关的步态变化。基于足部加速度和位置轨迹的分类准确率达到90%。该方法为预测实际疲劳水平提供了一个实用框架。
