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

Abstract

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 realtime 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.

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

“…Dambreville et al [ 55 ] developed an electrohydraulic robotized ankle–foot orthosis which was equipped with an optical encoder to determine ankle joint angles in the sagittal plane. Also, Durandau et al [ 54 ] used the ankle modules of the Symbitron to determine the ankle angle of the user by just reading the encoders of the exoskeleton. Furthermore, Aíin et al [ 53 ] used a “motorized ankle foot orthosis” (MAFO) which is capable of measuring ankle joint angles, but they did not provide any information as to which sensors are used by the device, and whether the procedure is validated or not.…”

Analysis of Exoskeleton Suit in Clinical and Home Use

“…HMI is also an advancement in UI that bridges intuitive communications and control. It imitates the human neuromusculoskeletal system by using a neuromechanical model human hand or leg, extracting electromyography (EMG) signals and converting them to joint torque [14]. However, recent HMI cannot make users control the exoskeleton completely voluntarily.…”

Consistent control information driven musculoskeletal model for multiday myoelectric control

“…Moreover, we checked the contribution differences of each muscle for different synergies by the percentages of weightings (P). P was calculated as follows: (1) the elements in each row of H were normalized to the highest value of the corresponding row, and the normalized row vectors of H were denoted as H + i (t) and H − i (t); (2) W was transformed into V (see (3)) to ensure the equality between…”

“…Human-machine interfaces (HMIs) based on surface electromyography (EMG) have built an intuitive way for intelligent interactions between humans and machines. Typical applications include dexterous myoelectric prostheses [1], robot control [2], and exoskeletal systems [3]. To achieve more intuitive and natural control, decoding simultaneous and proportional movements is desirable [4].…”