Individualized Learning-Based Ground Reaction Force Estimation in People Post-Stroke Using Pressure Insoles
Individualized Learning-Based Ground Reaction Force Estimation in People Post-Stroke Using Pressure Insoles
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DOI：
10.1109/icorr58425.2023.10304695
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发表时间：
2023-09
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影响因子：
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通讯作者：
Gregoire Bergamo;K. Swaminathan;Daekyum Kim;Andrew Chin;Christopher Siviy;Ignacio Novillo;Teresa C. Baker;Nicholas Wendel;Terry D. Ellis;Conor J. Walsh
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作者：
Gregoire Bergamo;K. Swaminathan;Daekyum Kim;Andrew Chin;Christopher Siviy;Ignacio Novillo;Teresa C. Baker;Nicholas Wendel;Terry D. Ellis;Conor J. Walsh
中风是步态残疾的主要原因，导致丧失独立性和整体生活质量。临床生物力学领域的目的是研究如何最好地提供康复给个人的损伤。然而，在生物力学分析和临床中使用的评估工具之间仍然存在脱节。特别是，三维地面反作用力（3D GRF）用于量化关键步态特征，但需要基于实验室的设备，如测力板。最近的努力表明，可穿戴传感器，如压力鞋垫，可以估计在现实世界环境中的GRF。然而，对于这些方法在中风后步态高度不均匀的人群中的表现，人们的理解有限。在这里，我们评估了三种特定于主题的机器学习方法，以在不同速度下估计中风后人群中使用压力鞋垫的3D GRF。我们发现，基于卷积神经网络的方法实现了最低的估计误差为0.75 ± 0.24，1.13 ± 0.54，和4.79 ± 3.04%体重的内侧，前后和垂直GRF分量，分别。另外，估计的力分量与地面实况测量值强相关（$R^{2}> 0.85$）。最后，我们表现出高的估计精度为三个临床相关的点度量的瘫痪肢体。这些结果表明，个性化的机器学习方法有可能转化为现实世界的临床应用。
Stroke is a leading cause of gait disability that leads to a loss of independence and overall quality of life. The field of clinical biomechanics aims to study how best to provide rehabilitation given an individual's impairments. However, there remains a disconnect between assessment tools used in biomechanical analysis and in clinics. In particular, 3-dimensional ground reaction forces (3D GRFs) are used to quantify key gait characteristics, but require lab-based equipment, such as force plates. Recent efforts have shown that wearable sensors, such as pressure insoles, can estimate GRFs in real-world environments. However, there is limited understanding of how these methods perform in people post-stroke, where gait is highly heterogeneous. Here, we evaluate three subject-specific machine learning approaches to estimate 3D GRFs with pressure insoles in people post-stroke across varying speeds. We find that a Convolutional Neural Network-based approach achieves the lowest estimation errors of 0.75 ± 0.24, 1.13 ± 0.54, and 4.79 ± 3.04 % bodyweight for the medio-lateral, antero-posterior, and vertical GRF components, respectively. Estimated force components were additionally strongly correlated with the ground truth measurements ($R^{2}> 0.85$). Finally, we show high estimation accuracy for three clinically relevant point metrics on the paretic limb. These results suggest the potential for an individualized machine learning approach to translate to real-world clinical applications.