Abstract
Computer-vision (CV) has been used for environmental classification during gait and is often used to inform control in assistive systems; however, the ability to predict how the foot will contact a changing environment is underexplored. We evaluated the feasibility of forecasting the anterior-posterior (AP) foot center-of-pressure (COP) and time-of-impact (TOI) prior to foot-strike on a level-ground to stair-ascent transition. Eight subjects wore an RGB-D camera on their right shank and instrumented insoles while performing the task of stepping onto the stairs. We trained a CNN-RNN to forecast the COP and TOI continuously within a 250ms window prior to foot-strike, termed the forecast horizon (FH). The COP mean-absolute-error (MAE) at 150, 100, and 50ms FH was 29.42mm, 26.82, and 23.72mm respectively. The TOI MAE was 21.14, 20.08, and 17.73ms for 150, 100, and 50ms respectively. While torso velocity had no effect on the error in either task, faster toe-swing speeds prior to foot-strike were found to improve the prediction accuracy in the COP case, however, was insignificant in the TOI case. Further, more anterior foot-strikes were found to reduce COP prediction accuracy but did not affect the TOI prediction accuracy. We also found that our lightweight model was capable at running at 60 FPS on either a consumer grade laptop or an edge computing device. This study demonstrates that forecasting COP and TOI from visual data was feasible using a lightweight model, which may have important implications for anticipatory control in assistive systems.
Abstract (translated)
计算机视觉（CV）已被用于步态过程中的环境分类，并常常被用来为辅助系统提供控制信息；然而，预测脚如何与变化的环境接触的能力则鲜少被探索。我们在本研究中评估了在平地过渡到上楼梯时，在足部着地之前预测前后方向（AP）的足底压力中心（COP）和触地时刻（TOI）的可能性。八个受试者在其右小腿佩戴RGB-D相机，并在执行上楼任务的同时穿有仪器化的鞋垫内衬。我们训练了一个卷积神经网络-递归神经网络（CNN-RNN），以在步态足部着地前的250毫秒窗口内连续预测COP和TOI，我们将这一时间范围称为预测视界（FH）。150、100和50毫秒FH下的COP平均绝对误差分别为29.42毫米、26.82毫米和23.72毫米。TOI的平均绝对误差在150、100和50毫秒时分别记录为21.14毫秒、20.08毫秒和17.73毫秒。虽然躯干速度对任一任务中的错误没有影响，但在足部着地前较高的趾摆动速度被发现可以提高COP预测的准确性，但对TOI案例的影响不显著。此外，我们还发现了更靠前的足着地点会降低COP的预测准确度，但这不会影响TOI的预测准确度。我们还发现我们的轻量级模型能够在消费级笔记本电脑或边缘计算设备上以每秒60帧的速度运行。这项研究表明，使用轻量级模型从视觉数据中预测COP和TOI是可行的，这可能对辅助系统的预见性控制有重要影响。
URL
https://arxiv.org/abs/2602.09209
