Wearable robots (WR) are person-oriented devices, usually in the form of exoskeletons. These devices are worn by human operators to enhance or support a daily function, such as walking. WRs find applications in the enhancement of intact operators or in clinical environments, e.g. rehabilitation of gait function in neurologically injured patients. Most advanced WRs for human locomotion still fail to provide the real-time adaptability and flexibility presented by humans when confronted with natural perturbations, due to voluntary control or environmental constraints. Current WRs are extra body structures inducing fixed motion patterns on its user.
The aim of the BioMot project is to improve the efficiency in the management of human-robot interaction in overground gait exoskeletons by means of mixture of bioinspired control, actuation and learning approaches. Our aim is to show how the embodiment of bioinspired and architectural mechanisms can allow a user to conveniently alter the behaviour of WRs for walking.
The final goal of the project is to deliver novel ambulatory wearable exoskeleton technology that exploits neuronal control and learning mechanisms and provides a) more energy efficient cooperative (human-robot) performance, and b) adaptive assistance based on the user’s residual and voluntary action.
BioMot’s exoskeletons apply adaptive assistance as a function of real-time estimation of human effort provided by a detailed neuromusculoskeletal model that computes neuromuscular activity (surface electromyography, EMG) to predict joint moments and hence prescribe the exoskeleton function. Gait detection algorithms based on human performance (brain signals, EEG) and embedded sensors (kinematic and kinetic) are developed for decision making, handling transitions or volitional changes in the task (such as gait speed). Local reflex-based joint controllers are designed to allow for automatic adaptation when confronting changes in the interaction. At the physical level, intrinsically compliant actuators are developed to exploit natural dynamics of movement, orchestrated by the control system for economy and stability. A global learning scheme modules joint compliance as a function of gait efficiency and semantic signals infered from user demand.
A cognitive system for a wearable gait exoskeleton that assists overgound human walking. The cognitive system processes biomechanical and electrophysiological signals to adaptively assist the human movement based on the user’s contribution and performance.
The wearable 6 DoF exoskeleton assists hip, knee and ankle movements with compliant actuators that can be transparent to the user and store and release energy.
BioMot’s assistive exoskeleton flexible built-in intelligence in its artificial brain and muscles allows to exploit the natural dynamics of overground walking.
As a wearable robotic trainer for gait disorders, BioMot uniquely provides overground gait training, promoting the patient effort to induce recovery, and assists as needed the patient during performance of the exercises. The training sessions enabled by BioMot trainer will go beyond common available protocols, including variations of speed, turning and improved negotiation with transitions.