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使用地面反应力数据对多位置运动任务进行连续步行阶段估计.

Ji Su Park1, Choong Hyun Kim2

  • 1Safety Component R&D Center, Gyeonggi Regional Division, Korea Automotive Technology Institute, Siheung-si 15014, Republic of Korea.

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概括
此摘要是机器生成的。

本研究引入了一种使用力感应电阻 (FSR) 和Bi-LSTM模型的新步态相估算算法. 该算法在各种行走条件下实现了超过90%的准确度,可以实时估计不同步态阶段.

关键词:
双向长期短期记忆 双向长期短期记忆连续步行阶段估计持续步行阶段估计强力传感电阻器的电阻器步态分析 步态分析地面反应力是什么?内部鞋装置的装置是什么?

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科学领域:

  • 生物力学 生物力学
  • 可穿戴技术可穿戴技术
  • 机器学习 机器学习

背景情况:

  • 步行阶段估计对于理解人类运动至关重要.
  • 现有的方法通常依赖于惯性测量单位 (IMU),并且仅限于特定的行走条件.
  • 需要在各种环境中进行强大的步态分析.

研究的目的:

  • 开发和验证一个实时步行阶段估计算法.
  • 为了评估算法性能跨多样化和具有挑战性的行走条件.
  • 为了证明算法的实际应用潜力.

主要方法:

  • 使用强力传感电阻 (FSR) 集成到内.
  • 采用双向长期短期记忆 (Bi-LSTM) 深度学习模型.
  • 对十名健康成年人进行了各种行走条件 (平坦地面,楼梯,坡道) 的实验.

主要成果:

  • 实现了超过90%的平均步态估计准确度.
  • 报告了0.794.4的低根平均平方误差 (RMSE).
  • 获得0.906的高R平方 (R2) 评分,表明强大的模型适合.
  • 在所有测试的行走条件中表现出强大的性能.

结论:

  • 拟议的基于FSR的Bi-LSTM算法提供了准确和实时的步态阶段估计.
  • 该算法显示了在各种基于内的应用中广泛使用的巨大潜力.
  • 应用包括步态分析,辅助设备控制和运动能力评估.