卧推训练中机械关节旋转摩擦监测方法研究

Xie Yong, Qingliang Zhang, Ravindra Luhach, Muhammed Alshehri
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引用次数: 0

摘要

卧推训练似乎是运动员、健身爱好者和健康爱好者中最常见的增强上肢力量和控制力的运动。卧推通常是躺在长凳上,背部、肩膀和臀部相互接触。随着技术的进步,卧推训练监控系统在研究领域很少见到。因此,本文提出了一种利用物联网传感器评估机械关节旋转摩擦的新型卧推训练监测方法(BPTMM)。卧推是一种常见的上半身力量锻炼和肌肉锻炼。卧推、深蹲和硬举是力量举重比赛中的三个主要动作。人工智能有助于风险预测,并建议可能的立场。实验结果表明,监测和分类准确率为96.8%。
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Research on Monitoring Method of Mechanical Joint Rotational Friction in Bench Press Training
Bench press training seems to be the most common exercise for increasing upper-body strength and control among athletes, fitness enthusiasts, and wellness buffs. Bench presses are usually done by lying down on the bench with the back, shoulders, and buttocks in touch. The bench press training surveillance systems with technical advancements are rarely seen in the research domain. Therefore, this paper presents a novel bench press training monitoring method (BPTMM) by evaluating mechanical joint rotational friction using Internet of Things (IoT) sensors. The bench press is a common upper body strength-building and muscle-building conditioning exercise. The bench press and the squat and deadlift are the three primary lifts performed in powerlifting competitions. Artificial intelligence aids in risk prediction and suggests possible positions. There is a 96.8% accuracy rate in surveillance and categorization, according to the findings of the experiments.
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