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Body-sensor-network-based spasticity detection

Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new...

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Bibliographic Details
Main Authors: Misgeld, Berno Johannes Engelbert (Author) , Heitzmann, Daniel (Author) , Wolf, Sebastian Immanuel (Author)
Format: Article (Journal)
Language:English
Published: May 9, 2016
In: IEEE journal of biomedical and health informatics
Year: 2016, Volume: 20, Issue: 3, Pages: 748-755
ISSN:2168-2208
DOI:10.1109/JBHI.2015.2477245
Online Access:Verlag, Volltext: https://doi.org/10.1109/JBHI.2015.2477245
Verlag, Volltext: https://ieeexplore.ieee.org/document/7244344
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Author Notes:B.J.E. Misgeld, Member, IEEE, M. Lüken, D. Heitzmann, S.I. Wolf, and S. Leonhardt, Senior Member, IEEE

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520 |a Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity. 
650 4 |a Adolescent 
650 4 |a Adult 
650 4 |a Algorithms 
650 4 |a antagonistic muscle group coactivation 
650 4 |a Blackman window 
650 4 |a body sensor networks 
650 4 |a body-sensor-network-based spasticity detection 
650 4 |a body-worn sensors 
650 4 |a Cerebral Palsy 
650 4 |a clinical gait data 
650 4 |a coactivation index 
650 4 |a cross-correlation function 
650 4 |a electromyography 
650 4 |a Electromyography 
650 4 |a Electromyography (EMG) 
650 4 |a electromyography sensor node 
650 4 |a EMG 
650 4 |a EMG signals 
650 4 |a exaggerated stretch reflex 
650 4 |a Female 
650 4 |a Gait 
650 4 |a gait analysis 
650 4 |a human locomotion 
650 4 |a Humans 
650 4 |a Informatics 
650 4 |a Integrated Posture and Activity Network by Medit Aachen body sensor network 
650 4 |a joint rigidity 
650 4 |a Legged locomotion 
650 4 |a Male 
650 4 |a medical disorders 
650 4 |a medical signal processing 
650 4 |a Medical treatment 
650 4 |a Middle Aged 
650 4 |a Muscle Spasticity 
650 4 |a Muscles 
650 4 |a neurological diagnostics 
650 4 |a rehabilitative motor training 
650 4 |a rehabilitative therapy 
650 4 |a Robot sensing systems 
650 4 |a robotic sensorimotor therapy 
650 4 |a signal equivalent energy 
650 4 |a signal processing 
650 4 |a Signal Processing, Computer-Assisted 
650 4 |a skeletal muscle 
650 4 |a spasticity 
650 4 |a Telemetry 
650 4 |a unilateral cerebral palsy 
650 4 |a Young Adult 
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