Biofeedback Signals for Robotic Rehabilitation

TY - JOUR

T1 - Biofeedback Signals for Robotic Rehabilitation

T2 - Assessment of Wrist Muscle Activation Patterns in Healthy Humans

AU - Semprini, Marianna

AU - Cuppone, Anna Vera

AU - Delis, Ioannis

AU - Squeri, Valentina

AU - Panzeri, Stefano

AU - Konczak, Jurgen

PY - 2017/7

Y1 - 2017/7

N2 - Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of seven major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested six movement directions and four force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

AB - Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of seven major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested six movement directions and four force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

KW - Biofeedback, Psychology/methods

KW - Electromyography/methods

KW - Female

KW - Humans

KW - Male

KW - Muscle Contraction/physiology

KW - Muscle, Skeletal/physiology

KW - Neurological Rehabilitation/methods

KW - Reference Values

KW - Robotics/methods

KW - Wrist/physiology

KW - Young Adult

U2 - 10.1109/TNSRE.2016.2636122

DO - 10.1109/TNSRE.2016.2636122

M3 - SCORING: Journal article

C2 - 28114024

VL - 25

SP - 883

EP - 892

JO - IEEE T NEUR SYS REH

JF - IEEE T NEUR SYS REH

SN - 1534-4320

IS - 7

ER -