Gait Prediction and Assist Control of Lower Limb Exoskeleton Based on Inertia Measurement Unit

The lower limb exoskeleton is a wearable robot with wide application, such as helping the elderly and the disabled, military, etc. This paper focuses on the design and control of the lower exoskeleton based on Inertia Measurement Unit (IMU), which can realize trajectory control, active and passive follow-up control and predictive control, thus realizing the assistance to the wearer and reducing the wearer's sense of load. The kinematic mechanism of human lower limbs is analyzed and the kinematic model is established by using standard D-H coordinate transformation method. The mechanical structure of exoskeleton is designed and its hardware system is built. LabVIEW software is selected as upper computer. The physical model of knee joint motor is established and its transfer function is derived. PID control strategy is selected and target trajectory for closed-loop control of external skeleton is set to achieve the specified trajectory. Gait movements of three participants were collected and analyzed using inertial sensors. Machine learning method is used to predict gait data through ARIMA model.