Cadence-Insensitive Soft Exoskeleton Design With Adaptive Gait State Detection and Iterative Force Control

摘要

Soft exoskeletons have demonstrated the potential to save energy, but their efficiency is sensitive to variations in human gait cadence. This work aims to develop adaptive gait state detection and iterative force control methods for a soft exoskeleton to reduce human walking metabolic cost consistently, while the user may change walking cadence. The proposed approach is motivated by the rhythmicity of gait and applies an iterative learning concept to enhance the exoskeleton’s adaptability to varying walking conditions. The gait state detection method proposed for the designed exoskeleton combines two feature extraction algorithms, which can learn from the present and past body kinematic data, to provide accurate user gait state detection. Based on the state, the proposed force control method iteratively adjusts the commands to keep track of the desired profile. Experiments have been conducted on healthy subjects walking with varying cadence using the soft exoskeleton. Promising results were presented in separate validation tests. Moreover, metabolic costs of subjects walking under one unpowered and two powered conditions, where the assistance profiles were produced by classical methods and the proposed methods, showed that the proposed methods can effectively improve the exoskeleton’s ability to save human energy of walking with varying cadence. Note to Practitioners—Lower limb exoskeletons have demonstrated the potential to save human energy in medical and industrial applications. The main purpose of this work is to solve the exoskeleton assistance efficiency loss problem for users walking with changing cadence. Constant cadence is unlikely maintained during natural human walking. Few existing exoskeletons could retain high efficiency under user cadence changes, limited by their control system capability. This work presents a new cable-driven cadence-insensitive soft exoskeleton, which is purposely designed with two adaptive methods to enable the device to offer consistent benefit to users walking with varying cadence. The proposed methods are inspired by the rhythmicity of human gait and can be iteratively reconfigured to perform accurate human gait state detection and assistive force tracking. The proposed methods have the potential to be integrated into other human-oriented robots to improve their adaptability. This work can greatly enhance the possibility of using the walking assist robotic devices in more practical applications.