Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network
Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network
复制标题
使用深度卷积神经网络进行地上运动期间基于可穿戴传感器的步长估计
DOI：
10.1109/embc46164.2021.9630060
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Young, Aaron J.
中科院分区：
文献类型：
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作者：
Jin, Heejoo;Kang, Inseung;Choi, Gayeon;Molinaro, Dean D.;Young, Aaron J.
步长是一个关键的步态参数，允许步态不对称的定量评估。步态不对称会导致许多潜在的健康威胁，如关节退化，平衡控制困难和步态效率低下。因此，准确的步长估计是必不可少的，以了解步态不对称，并提供适当的临床干预或步态训练计划。用于步长测量的常规方法依赖于使用脚安装式惯性测量单元（伊穆斯）。然而，由于传感器信号漂移和使用远端传感器的潜在干扰，这可能不适合于现实世界的应用。为了克服这一挑战，我们提出了一种基于深度卷积神经网络的步长估计，仅使用能够推广到各种步行速度的近端可穿戴传感器（臀部测角仪、躯干IMU和大腿IMU）。为了评估这种方法，我们利用从16个健全的受试者在不同的步行速度收集的跑步机数据。我们在地上行走数据上测试了我们的优化模型。我们的CNN模型估计了步长，所有受试者和步行速度的平均绝对误差为2.89 ± 0.89 cm。由于可穿戴传感器和CNN模型易于实时部署，我们的研究结果可以在可穿戴辅助设备和步态训练计划中提供个性化的实时步长监测。
Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs.