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基于触觉传感器的压力估计系统的身体中心使用监督深度学习模型

Jaehyeon Baik¹, Yunho Choi², Kyung-Joong Kim³

¹Department of Control and Robot Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea.

Sensors (Basel, Switzerland)

|January 10, 2026

概括

一个新的触觉传感器系统使用深度学习来准确估计压力中心 (CoP),提供一个具有成本效益的平衡评估工具. 这项技术改进了以前的方法,减少错误,以便更好地评估跌倒风险.

科学领域:

生物力学和生物医学工程
传感器技术传感器技术
机器学习机器学习

背景情况:

压力中心 (CoP) 对于平衡和跌倒风险评估至关重要.
传统的强力板是昂贵和不切实际的;当前的低成本替代品有局限性.
之前的机器学习模型使用稀疏传感器进行COP估计,显示了显著的中侧/前后 (ML/AP) NRMSE差异 (3.2-4.7%).

研究的目的:

开发和评估一个成本效益高的基于触觉传感器的系统,以使用深度学习来估计CoP.
与以前的方法相比,提高COP估计的准确性和减少方向不平衡.
通过不同的平衡协议来评估系统的性能.

主要方法:

提出了采用深度学习模型 (CNN/ResNet编码器与Bi-LSTM) 的触觉传感器系统,以分析2D压力分布图像和时间模式.
收集了23名健康成年人的数据,他们执行了四个平衡协议.
将ResNet-Bi-LSTM和CNN-Bi-LSTM模型与基线CNN-LSTM和Bi-LSTM进行了比较,使用交叉验证 (LOOCV),用RMSE,NRMSE和R2进行评估.

主要成果:

具有角度特征的ResNet-Bi-LSTM模型实现了最佳性能,产生了18.63 ± 4.57 mm (ML) 和17.65 ± 3.48 mm (AP) 的RMSE值.
显著降低了ML/APNRMSE差异的1.3%,比之前报告的3.2-4.7%大幅改善.
在各种模型中,特别是在动态平衡协议下,在RMSE最低的情况下表现出卓越的性能.

结论:

与先进的深度学习集成的基于触觉传感器的系统提供了一个有希望的,具有成本效益的替代方案,用于CoP测量的强力板.
拟议的ResNet-Bi-LSTM模型显著提高了COP估计的准确性,并减少了方向偏差.
潜在的应用包括步态分析,实时平衡监测和跌倒风险评估,计划在未来对患者群体进行验证.

关键词:

美国有线电视新闻网-Bi-LSTM 这就是ResNet-Bi-LSTM.这是一个平衡的平衡,平衡的平衡.压力中心压力中心估计估计估计的估计.监督学习学习监督学习触觉传感器是一种触觉传感器.

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