Contents I. Introduction
With the development of technology, wearable lower limb exoskeletons are increasingly used in rehabilitation training and light mobility [1], [2], [3]. The higher the level of customization of the exoskeleton's assistance, the better its ability to adapt to changes in the participants' physiology [4], [5], [6]. The HILO is influenced by factors such as force loading accuracy, assist profile, optimization method, and objective function [7], [8]. Research on knee exoskeletons focuses primarily on control methods to ensure force loading accuracy [9], [10]. In terms of assist profile, the assist profiles to be optimized for the hip and ankle joints are similar to the statistical profiles [11], [12]. Due to frequent power conversion in the knee joint, the energy storage and release mechanism was initially used to construct the flexion-assisting profile [13]. Optimization strategies are widely used for HILO [7], [14]. Gradient descent mainly depends on the signal-to-noise ratio of the measured metabolic value [15]. Compared to the gradient descent, the convergence time of Bayesian optimization for a hip exoskeleton was reduced by more than half [11]. On the other hand, the covariance matrix adaptive evolution strategy was used to calculate the next generation of control law for the knee exoskeleton [16]. Metabolic consumption in the above studies is measured using indirect calorimetry, which still has limitations in speed, comfort, and generalization [17].
