Neuromechanical Model-Based Adaptive Control of Bilateral Ankle Exoskeletons:Biological Joint Torque and Electromyogram Reduction Across Walking Conditions

To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of 'unseen' walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in real-time from measured electromyograms (EMGS) and joint angles. We call this 'neuromechanical model-based control' (NMBC). NMBC enabled six individuals to voluntarily control a bilateral ankle exoskeleton across six walking conditions, including all intermediate transitions, i.e., two walking speeds, each performed at three ground elevations. A single subject case-study was carried out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled reducing biological ankle torques, as well as eight ankle muscle EMGs both within (22% torque;12% EMG) and between walking conditions (24% torque; 14% EMG) when compared to non-assisted conditions. Torque and EMG reductions in novel walking conditions indicated that the exoskeleton operated symbiotically, as an exomuscle controlled by the operator.s neuromuscular system. This opens new avenues for the systematic adoption of wearable robots as part of out-of-the-lab medical and occupational settings

Model-based myoelectric control of robots for assistance and rehabilitation

The first anthropomorphic robots and exoskeletons were developed with the idea of combining man and machine into an intimate symbiotic unit that can perform as one joint system. A human-robot interface consists of processes of two different nature: (1) the physical interaction (pHRI) between the device and its user and (2) the exchange of cognitive information (cHRI) between the human and the robot. To achieve the symbiosis between the two actors, both need to be optimized. The evolution of mechanical design and the introduction of new materials pushed pHRI to new frontiers on ergonomics and assistance performance. However, cHRI still lacks on this direction because is more complicated: it requires communication from the cognitive processes occuring in the human agent to the robot, e.g. intention detection; but also from the robot to the human agent, e.g. feedback modalities such as haptic cues. A possible innovation is the inclusion of the electromyographic signal, the command signal from our brain to the musculoskeletal system for the movement, in the robot control loop. The aim of this thesis was to develop a real-time control framework for an assistive device that can generate the same force produced by the muscles. To do this, I incorporated in the robot control loop a detailed musculoskeletal model that estimates the net torque at the joint level by taking as inputs the electromyography signals and kinematic data. This module is called myoprocessor. Here I present two applications of this control approach: the first was implemented on a soft wearable arm exosuit in order to evaluate the adaptation of the controller on different motion and loads. The second one, was a generation of myoprocessor-driven force field on a planar robot manipulandum in order to study the modularity changes of the musculoskeletal system. Both applications showed that the device controlled by myoprocessor works symbiotically with the user, by reducing the muscular activity and preserving the motor performance. The ability of seamlessly combining musculoskeletal force estimators with assistive devices opens new avenues for assisting human movement both in healthy and impaired individuals

Integral admittance shaping: A unified framework for active exoskeleton control

© 2015 Elsevier B.V. Current strategies for lower-limb exoskeleton control include motion intent estimation, which is subject to inaccuracies in muscle torque estimation as well as modeling error. Approaches that rely on the phases of a uniform gait cycle have proven effective, but lack flexibility to aid other kinds of movement. This research aims at developing a more versatile control that can assist the lower limbs independently of the movement attempted. Our control strategy is based on modifying the dynamic response of the human limbs, specifically their mechanical admittance. Increasing the admittance makes the lower limbs more responsive to any muscle torque generated by the human user. We present Integral Admittance Shaping, a unified mathematical framework for: (a) determining the desired dynamic response of the coupled system formed by the human limb and the exoskeleton, and (b) synthesizing an exoskeleton controller capable of achieving said response. The present control formulation focuses on single degree-of-freedom exoskeleton devices providing performance augmentation. The algorithm generates a desired shape for the frequency response magnitude of the integral admittance (torque-to-angle relationship) of the coupled system. Simultaneously, it generates an optimal feedback controller capable of achieving the desired response while guaranteeing coupled stability and passivity. The potential effects of the exoskeleton's assistance are motion amplification for the same joint torque, and torque reduction for the same joint motion. The robustness of the derived exoskeleton controllers to parameter uncertainties is analyzed and discussed. Results from initial trials using the controller on an experimental exoskeleton are presented as well