Processing Surface EMG Signals for Exoskeleton Motion Control

Abstract

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

“…Dynamicmodel-based control mainly uses dynamic models adopted from humanoid robotics [118], [119], on which assistive torque calculation was based [120]. Other uncommon method including ambulation speed regulation based on sEMG signal [121], synergy-based control [122], etc.…”

“…For nonbackdrivable motors, especially that with high transmission ratio, usually the corresponding motion controller is in kinematic control mode, i.e., regulating the joint angle or joint velocity of the actuator. The kinematic approach can be in the form of plain trajectory tracking [168], admittance control [54], some of the biological-signal-based control [121], and so on. The controller for executing the kinematic command can be simple PID [41], or with some sophisticated method such as model-based control, which directly regulate torque output by input-output linearization controller in underactuated model [12], or by feedback linearization controller in fully-actuated model [50].…”

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

“…Indeed, sEMG signals acquired on the healthy side are classified by a SVM to decode the subject's motor intention, then executed by the exoskeleton attached to the impaired side. Yin et al extracted gait cycle durations (GCDs) from an eight-channel sEMG signal using the autocorrelation and the Bayesian fusion algorithm for controlling the motion speed of an exoskeleton-treadmill gait rehabilitation system [122]. In [123], an upper limb EMG-controlled power-assist exoskeleton intended for elbow rehabilitation was developed.…”

AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

“…Brain Computer Interface (BCI) controlled assistive technology is another paradigm that provides assistance and rehabilitation for the paralyzed [16]. To control the exoskeleton movement, Electromyography (EMG) sensors are employed that help in returning the information related to the human muscle acitivity [17]. But EMG signals are restricted to muscles and also have much limitations.…”