使用3度自由度加速数据和人工神经网络预测步态动力学
Carina Gempfer1, Gilmar F Santos1, Alexander Hobein1
1Hannover Medical School, Department of Orthopaedic Surgery, DIAKOVERE Annastift, Anna von Borries Str. 1-7, 30625 Hannover, Germany.
Clinical biomechanics (Bristol, Avon)
|February 7, 2026
概括
可穿戴式传感器和神经网络准确地预测了实验室外的运动步行参数. 这项技术可以帮助诊断骨科疾病并改善外置假肢.
科学领域:
- 生物力学 生物力学
- 可穿戴技术是可穿戴的技术.
- 人工智能的人工智能是人工智能.
背景情况:
- 传统的运动分析是准确的,但资源密集.
- 可穿戴传感器为步态分析提供了一个便携式替代方案.
- 对已建立的系统进行验证对于可穿戴传感器技术至关重要.
研究的目的:
- 用可穿戴传感器和神经网络来预测运动步行参数.
- 评估传统实验室环境之外的步态分析的可行性.
- 支持骨科和外置假肢的临床决策.
主要方法:
- 32名健康的成年人参与了这项研究.
- 使用常规运动分析设置收集动力学和动力学数据.
- 使用可穿戴传感器记录线性加速,并输入长期短期记忆 (LSTM) 和多层感知器 (MLP) 神经网络模型.
主要成果:
- 无论是LSTM还是MLP模型,都显示出与参考数据的高度一致.
- 皮尔森相关系数在0.79 (膝关节功率) 到0.99 (前后地面反应力) 之间.
- 规范化根的平均平方误差在3.1%和10.3%之间,表明有希望的预测准确性.
结论:
- 可穿戴式传感器和神经网络可以在实验室外准确估计运动步行参数.
- 这种方法有可能对骨科疾病进行步态评估的标准化.
- 未来的应用包括动态外置假肢调整和治疗结果评估.
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