Admittance Control of a Robotic Knee OrthosisBased on Motion Intention Through sEMG ofTrunk Muscles

The population that requires devices for motion improvement has increased considerably, due to aging and neurological impairments. Robotic devices, such as robotic orthosis, have greatly advanced with the objective of improving both the mobility and quality of life of people. Clinical researches remark that these devices, working in constant interaction with the neuromuscular and skeletal human system, improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For this purpose, control approaches based on motion intention have been presented as a novel control framework for robotic devices. This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance modulation to assist people with reduced mobility and improve their locomotion. For recognition of the lower-limb motion intention, sEMG signals from trunk are used, which implies a new approach to control robotic assistive devices. The control system developed here includes a stage for human-motion intention recognition (HMIR) system, which is based on techniques to classify motion classes related to knee joint. The motion classes that are taken into account are: stand-up, sit-down, knee flexion-extension, walking, rest in stand-up position and rest sit-down position. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system includes a finite state machine, in addition to admittance, velocity and trajectory controllers, which has also the function of stopping the movement according to the users intention. This work also proposes a method for on-line knee impedance modulation based on gait phases recognition using an instrumented insole. This method generates variable gains through the gait cycle for stance control during gait. The proposed HMIR system showed, in off-line analysis, an accuracy between 76% to 83% to recognize motion intention of lower-limb muscles, and 71% to 77% for trunk. Experimental on-line results of the controller with healthy and post-stroke patients showed that the admittance controller proposed here offers knee support in 50% of the gait cycle, and assists correctly the motion classes. A positive effect of the controller on post-stroke patients as users regarding safety during gait was also found, with a score of 4.64 in a scale of 5. Thus the robotic knee exoskeleton introduced here is an alternative method to empower knee movements using motion intention based on sEMG signals from lower limb and trunk muscles.