家庭下肢康复:运动评估调查及运动状态识别生物反馈的初步研究

Seanglidet Yean, Bu-Sung Lee, C. Yeo
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引用次数: 0

摘要

衰老会导致肌肉力量的丧失,尤其是下肢,从而导致在功能性活动中受伤的风险更高。康复的途径是通过物理治疗和采用定制的康复运动来帮助患者。因此,降低在家不正确运动的风险涉及到对物理康复患者使用生物反馈和临床物理治疗的定量报告。近年来,由于人口快速老龄化和临床专家数量有限,这一研究课题受到了广泛关注。本文对运动评估和状态识别方面的研究现状进行了综述。评价结果表明,使用所提出的原始信号对运动状态进行分类的准确率平均为95.83%。验证了原始信号在状态识别预测模型中比使用传感器融合欧拉和关节角的影响更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lower-Limb Rehabilitation at Home: A Survey on Exercise Assessment and Initial Study on Exercise State Identification Toward Biofeedback
Ageing causes loss of muscle strength, especially on the lower limbs, resulting in higher risk to injuries during functional activities. The path to recovery is through physiotherapy and adopt customized rehabilitation exercise to assist the patients. Hence, lowering the risk of incorrect exercise at home involves the use of biofeedback for physical rehabilitation patients and quantitative reports for clinical physiotherapy. This research topic has garnered much attention in recent years owing to the fast ageing population and the limited number of clinical experts. In this paper, the authors survey the existing works in exercise assessment and state identification. The evaluation results in the accuracy of 95.83% average classifying exercise motion state using the proposed raw signal. It confirmed that raw signals have more impact than using sensor-fused Euler and joint angles in the state identification prediction model.
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