摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx记者在纽约罗切斯特的新闻报道，研究表明，"在用外骨骼执行任务时监测身体需求可以有助于了解它们对工业任务的适应性"。新闻记者从罗切斯特理工学院获得了一段研究的引文：“这项研究旨在开发一种使用可穿戴传感器的背部支撑工业外骨骼(BSIEs)疲劳水平预测模型。10名参与者在穿着BSIE时，进行了一组包含静态、持续和动态活动的间歇性躯干弯曲任务循环，直到他们达到中等-高疲劳水平。三种分类算法，Suppo RT向量机(SVM)、随机森林(RF)和XGBoost(XGB)，利用4个带集成惯性测量单元(IMUs)的可穿戴无线肌电图(EMG)传感器的特征对背部和腿部的感知疲劳水平进行了预测，通过比较预测性能，考察了最佳分组和传感器组合，结果表明，在腿部和背部疲劳的二元分类中，预测准确率为95%(2个EMG+IMU传感器)和82%(单个IMU传感器)。分别。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Rochester, New York, by NewsRx journalists, research stated, “Monitoring physical demands during task ex ecution with exoskeletons can be instrumental in understanding their suitability for industrial tasks.” The news correspondents obtained a quote from the research from Rochester Instit ute of Technology: “This study aimed at developing a fatigue level prediction mo del for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fou rteen participants performed a set of intermittent trunk-flexion task cycles con sisting of static, sustained, and dynamic activities, until they reached medium- high fatigue levels, while wearing BSIEs. Three classification algorithms, Suppo rt Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features f rom four wearable wireless Electromyography (EMG) sensors with integrated Inerti al Measurement Units (IMUs). We examined the best grouping and sensor combinatio ns by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IM U sensors) and 82% (single IMU sensor) accuracy, respectively.”
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