Admittance control of a robotic knee orthosis based on motion intention through sEMG of trunk muscles
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Data
2017-12-04
Autores
Parra, Ana Cecilia Villa
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Universidade Federal do Espírito Santo
Resumo
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 flexionextension, walking, rest in stand-up position and rest sit-down position. For translation of the users 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, which 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 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 users regarding safety during gait was also found, with a score of 4 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.
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Admittance control , Stance control