Investigation of effect of human robot interaction with lower limb exoskeletons

Abstract

Continuous development of exoskeletons (wearable robots) is essential to enhance the user experiences and performances of the wearable device. Therefore, it is necessary to determine human ergonomics and the comfort levels of wearable robots. These aspects can be analyzed by determining human-robot interaction (HRI). HRI is classified in cognitive- HRI (cHRI) and physical-HRI (pHRI) in the literature. cHRI involves the identification of complex human expression and physiological aspects. These pieces of information can be observed using a human-robot cognitive interface. Electroencephalogram (EEG) and electromyography (EMG) are mainly used sensing methods in cHRI. EEG is used to identify electrical activities of brain, while EMG is used to identify electrical activities of muscles. Furthermore, pHRI involves evaluating physical quantities such as position, force, and pressure between humans and robots. In order to identify pHRI with wearable robotic interfaces, a novel surface muscle pressure (SMP) sensory system was developed. The SMP sensor was calibrated and evaluated using surface electromyography (sEMG ) data for two separates experimental scenarios. Hence the system was proposed to determine the pHRI of wearable robotics. In order to determine HRI, a dummy lower limb exoskeleton was designed and manufactured in compliance with human ergonomics and biomechanics. The exoskeleton consists of 8 degrees of freedom (DoF) motions with variable limbs and weight attachment locations. Furthermore, sEMG, motion analysis, and SMP sensory systems were used to carry out the experiments. Moreover, a human lower limb model simulation with ground force reaction prediction was developed to determine the inverse dynamics. The experiments were carried out without exoskeleton, with the exoskeleton, and with exoskeleton weight attachments with six healthy subjects for the walking motion. A qualitative, comfortable level analysis was carried out simultaneously for each experiment. Captured SMP, sEMG, inverse dynamics and qualitative results were processed and feature extracted to evaluate HRI for different weight distributions and attachment locations. The relationship between exoskeleton attachments and locations was observed. The experiment results have provided an improved understanding of HRI for developing practical and ergonomically comfortable lower limb exoskeleton devices.