Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable
Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable
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DOI：
10.1017/wtc.2023.3
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发表时间：
2023-02
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通讯作者：
Samaneh Davarzani;D. Saucier;Purva Talegaonkar;Erin Parker;Alana Turner;Carver Middleton;William O. Carroll;J. Ball;A. Gurbuz;H. Chander;Reuben F. Burch;Brian K. Smith;A. Knight;Charles E. Freeman
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作者：
Samaneh Davarzani;D. Saucier;Purva Talegaonkar;Erin Parker;Alana Turner;Carver Middleton;William O. Carroll;J. Ball;A. Gurbuz;H. Chander;Reuben F. Burch;Brian K. Smith;A. Knight;Charles E. Freeman
摘要可穿戴技术的发展使人们能够在实验室外对人体运动进行运动跟踪分析，从而提高了人们对个人健康和表现的认识。这项研究使用了一个可穿戴的智能袜子原型来跟踪步态运动中脚踝的运动学。训练多变量线性回归和两种深度学习模型，包括长短期记忆(LSTM)和卷积神经网络，以估计通过光学运动捕获系统测量的矢状面和额面的关节角度。为10名健康受试者在跑步机上行走建立了特定于参与者的模型。该原型在不同的行走速度下进行了测试，以评估其跟踪多种速度的运动的能力，并推广用于估计矢状面和额面关节角度的模型。LSTM的表现优于其他模型，它们具有较低的平均绝对误差(MAE)、较低的均方根误差和较高的R平方值。在每个速度下训练模型时，矢状面和额面处的平均MAE得分分别小于1.138°和0.939°，在所有速度下训练和评估时的平均MAE得分分别小于2.15°和1.14°。这些结果表明，可穿戴的智能袜子可以在不同的行走速度下以相对较低的误差概括脚踝运动学，因此可以用于测量步态参数，而不需要实验室受限的运动捕获系统。
Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.