Biofeedback Signals for Robotic Rehabilitation
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Biofeedback Signals for Robotic Rehabilitation : Assessment of Wrist Muscle Activation Patterns in Healthy Humans. / Semprini, Marianna; Cuppone, Anna Vera; Delis, Ioannis; Squeri, Valentina; Panzeri, Stefano; Konczak, Jurgen.
In: IEEE T NEUR SYS REH, Vol. 25, No. 7, 07.2017, p. 883-892.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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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 -