George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Ann Biomed Eng. 2024 Aug;52(8):2013-2023. doi: 10.1007/s10439-024-03495-z. Epub 2024 Apr 1.
Center of mass (COM) state, specifically in a local reference frame (i.e., relative to center of pressure), is an important variable for controlling and quantifying bipedal locomotion. However, this metric is not easily attainable in real time during human locomotion experiments. This information could be valuable when controlling wearable robotic exoskeletons, specifically for stability augmentation where knowledge of COM state could enable step placement planners similar to bipedal robots. Here, we explored the ability of simulated wearable sensor-driven models to rapidly estimate COM state during steady state and perturbed walking, spanning delayed estimates (i.e., estimating past state) to anticipated estimates (i.e., estimating future state). We used various simulated inertial measurement unit (IMU) sensor configurations typically found on lower limb exoskeletons and a temporal convolutional network (TCN) model throughout this analysis. We found comparable COM estimation capabilities across hip, knee, and ankle exoskeleton sensor configurations, where device type did not significantly influence error. We also found that anticipating COM state during perturbations induced a significant increase in error proportional to anticipation time. Delaying COM state estimates significantly increased accuracy for velocity estimates but not position estimates. All tested conditions resulted in models with R > 0.85, with a majority resulting in R > 0.95, emphasizing the viability of this approach. Broadly, this preliminary work using simulated IMUs supports the efficacy of wearable sensor-driven deep learning approaches to provide real-time COM state estimates for lower limb exoskeleton control or other wearable sensor-based applications, such as mobile data collection or use in real-time biofeedback.
质心（COM）状态，特别是在局部参考系中（即相对于压力中心），是控制和量化双足运动的重要变量。然而，在人类运动实验中，很难实时获得此度量值。当控制可穿戴机器人外骨骼时，此信息可能非常有价值，特别是在稳定性增强方面，因为 COM 状态的知识可以使类似双足机器人的步长规划器得以实现。在这里，我们探索了模拟可穿戴传感器驱动模型在稳态和受扰行走期间快速估计 COM 状态的能力，涵盖了延迟估计（即，估计过去的状态）到预期估计（即，估计未来的状态）。在整个分析过程中，我们使用了各种常见于下肢外骨骼的模拟惯性测量单元（IMU）传感器配置和时间卷积网络（TCN）模型。我们发现，在髋部，膝部和踝部外骨骼传感器配置下，COM 估计能力相当，而设备类型对误差的影响并不明显。我们还发现，在受扰期间预期 COM 状态会导致误差成比例地增加，与预期时间成正比。延迟 COM 状态估计会显著提高速度估计的准确性，但不会提高位置估计的准确性。所有测试条件下的模型均具有 R > 0.85，其中大多数模型的 R > 0.95，这强调了这种方法的可行性。总体而言，这项使用模拟 IMU 的初步工作支持了可穿戴传感器驱动的深度学习方法在下肢外骨骼控制或其他基于可穿戴传感器的应用（例如移动数据收集或实时生物反馈）中提供实时 COM 状态估计的有效性。
