Myoelectric Control of Robotic Leg Prostheses and Exoskeletons: A Review

Abstract

Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and non-rhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

“…One solution to mitigate the vibration in the initial experience is using a lower desired strength for the robot controller in the first moments of using the robot and then increasing it after a while. This approach, i.e., changing the strength from less (initial experience) to high amount (long-term), has been experimentally evaluated in the literature [ 2 , 65 , 66 ], and the results have reported that the method of increasing the strength over time is preferred for human adaptation to robots. Many wearable biomechatronic device users have preferred to initiate with lower controller strength [ 65 , 66 ].…”

“…Researchers have commonly used the proportional gain method [ 2 , 24 , 25 ] ( Figure 2 A). The desired robot torque is calculated as a proportion of the estimated human joint torque (Equation ( 1 )).…”

“…The human subject wearing a robotic assistive device that interacts with the environment forms a closed-loop system with two separate controllers: the human central nervous system (CNS) and the robot control system [ 1 ]. These two control systems simultaneously modify the behavior of the closed-loop system with different approaches of robot’s hierarchical control [ 2 , 3 ] and human’s optimal control [ 4 ]. Desirable performance of wearable devices entails good coordination between these two controllers [ 5 ].…”

“…An inefficient control policy may limit the normal human range of motion or increase discomfort due to unnatural motion [ 17 , 18 ]. Executing natural motions from human intent is challenging, applying volitional natural human adjustment is difficult, and the overall applicability is limited [ 2 ]. Recently, the assist-as-needed (AAN) strategy, which operates based on enhancing motor activity but not replacing it, shows promising results [ 7 , 12 , 19 , 20 , 21 ].…”

“…Previous research has converted the human–robot–environment interaction system to an overly simplified rule. Consequently, adjustments are necessary for any changes in the dynamics of limb, robot, and environment [ 2 , 24 , 27 , 31 ]. To overcome these challenges, or at least mitigate the inefficiencies and limitations, considering the human–robot–environment dynamic interaction is a viable solution.…”