通过sEMG通过多DOF合动态运动下对脚主动施加的扭矩进行在线估计框架
概括
在机器人辅助康复期间准确估计脚关节扭矩,可以提高患者的参与度. 本研究提出了一种使用生物机械模型和校准来精确估计多DOF扭矩的新框架,提高训练效率.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 康复工程 康复工程
背景情况:
- 脚康复机器人提供个性化培训,但需要准确测量患者的努力.
- 从多度自由度 (DOF) 脚关节综合体 (AJC) 估计积极施加的扭矩对于有效的康复至关重要.
- 在AJC中的合运动需要先进的方法来精确估计扭矩.
研究的目的:
- 开发和验证一个框架,准确估计多DOF AJC. 活跃施加的扭矩.
- 为了提高人机交互和机器人辅助脚康复的有效性.
- 为临床诊断和治疗提供可靠的神经信息和关节扭矩数据.
主要方法:
- 集成基于Hill的表面电肌图 (sEMG) 力量模型用于肌肉力量估计.
- 开发了一种结合了解剖学和生物力学特征的脚肌肉骨动态解模型.
- 应用参数识别-校准策略,包括单个DOF预试验,用于模型参数化.
主要成果:
- 拟议的框架实现了单个DOF扭矩估计的10.29%±2.86%的正常化根平均平方误差 (NRMSE).
- 多DOF扭矩估计得出的NRMSE值为11.35%±4.51%,显示出高精度.
- 该模型成功地将估计的肌肉力量与AJC在各种运动中积极施加的扭矩相关.
结论:
- 开发的框架可以在机器人辅助康复期间准确估计多DOF脚关节扭矩.
- 这项技术可以显著提高患者参与度和康复计划的有效性.
- 该框架为早期疾病诊断和个性化患者特定治疗方案提供了有价值的数据.
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