首页|Chang Gung University Researchers Yield New Study Findings on Robotics (Myoelect ric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral A rm Exercises with Varying Resistances)

Chang Gung University Researchers Yield New Study Findings on Robotics (Myoelect ric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral A rm Exercises with Varying Resistances)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics have been pr esented. According to news originating from Taoyuan, Taiwan, by NewsRx correspon dents, research stated, "Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showin g significant enhancements with increased repetitions." Funders for this research include National Science And Technology Council, Taiwa n; Chang Gung Memorial Hospital, Taiwan. Our news correspondents obtained a quote from the research from Chang Gung Unive rsity: "However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, lowresistance workouts a nd short-duration, high-resistance exercises. Various measures, including surfac e electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating o f Perceived Exertion scale, were employed to assess fatigue levels and the impac ts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of he moglobin, indicating increased muscle fatigue and a higher metabolic demand to c ope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, e ngaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity."

Chang Gung UniversityTaoyuanTaiwanAsiaEmerging TechnologiesMachine LearningRobotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.7)