State Key Laboratory of Mechanism System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2020 Jun 30;20(13):3685. doi: 10.3390/s20133685.
Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP values were higher than those of GP.
估计人体步态中的下肢关节扭矩是一项极具挑战性的任务，对于开发高性能下肢外骨骼的高级控制器具有重要意义。本文提出了一种基于依赖高斯过程（DGP）的学习算法，用于从可穿戴智能鞋中获取测量值进行关节扭矩估计。DGP 用于执行数据融合，并作为探索关节运动学与关节扭矩之间内在相关性的数学基础，这些相关性深深嵌入在数据中。由于关节运动学用于训练阶段，而不是预测过程，因此 DGP 模型仅使用智能鞋即可在户外活动中实现准确预测，这种智能鞋具有成本低、对人体步态无干扰和穿着舒适等优点。根据测量信号的先验知识，提出了动态特定核函数的设计方法。设计的复合核函数可用于对不同尺度的多个特征进行建模，并适应人体步态的时间演变。所提出的 DGP 模型和复合核函数的统计性质为随时间变化的步态模式学习提供了卓越的灵活性，并实现了准确的关节扭矩估计。对五名受试者进行了实验，结果表明可以在不同训练和未训练速度水平下估计关节扭矩。比较了所提出的 DGP 和高斯过程（GP）模型。当所有 DGP 值都高于 GP 值时，都能取得明显的改进。
