侧面行走步态相位识别关节外骨通过denoising自编码器-LSTM
Mingxiang Luo1, Xiaoli Dong2, Hongliu Yu3
1Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China.
Computational and structural biotechnology journal
|March 3, 2025
概括
一个新的denoising自编码器-LSTM算法准确地识别外骨应用的横向行走步态阶段. 这种方法实现了高精度和强度,在识别部绑架器强化练习的步态方面超过了以前的模型.
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
- 机器学习 机器学习
背景情况:
- 横向阻力行走有效地加强部绑架肌肉.
- 准确地识别横行走势对于外骨架辅助的康复和运动至关重要.
- 现有的步态识别方法可能缺乏实时外骨控制所需的准确性和稳定性.
研究的目的:
- 提出和评估一个新的Denoising Autoencoder-LSTM (DAE-LSTM) 算法,用于横行步行方式的识别.
- 将DAE-LSTM的性能与传统机器学习模型进行比较.
- 评估算法的准确性,识别时间和强度,用于外骨应用.
主要方法:
- 收集了来自十名受试者的惯性测量单元 (IMU) 数据,跨越了三种速度和步伐.
- 实施了一种用于步态阶段识别的Denoising Autoencoder-LSTM (DAE-LSTM) 模型.
- 将DAE-LSTM与随机森林 (RF),支持矢量机 (SVM),K-最近邻居 (KNN) 和神经网络 (NN) 模型进行比较.
主要成果:
- DAE-LSTM实现了90.2%的平均跨主题识别准确度,超过了其他模型.
- 该算法显示高精度 (>90%) 即使有显著的噪音 (SNR>100:1).
- 每的DAE-LSTM平均识别时间为0.383毫秒,符合实际要求.
结论:
- 拟议的DAE-LSTM算法提供了准确而强大的横向行走方式识别.
- 该算法满足了在外骨架系统中实时应用的性能要求.
- DAE-LSTM为增强外骨架辅助康复和训练练习提供了一个有前途的解决方案.
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