首页|New Findings from Rochester Institute of Technology in the Area of Machine Learn ing Described (Machine Learning-Based Fatigue Level Prediction for Exoskeleton-A ssisted Trunk Flexion Tasks Using Wearable Sensors)
New Findings from Rochester Institute of Technology in the Area of Machine Learn ing Described (Machine Learning-Based Fatigue Level Prediction for Exoskeleton-A ssisted Trunk Flexion Tasks Using Wearable Sensors)
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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.”
Rochester Institute of TechnologyRoche sterNew YorkUnited StatesNorth and Central AmericaCyborgsEmerging Tech nologiesMachine Learning
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