Parameters that integrated electrophysiological signals with joint movement characteristics were best able to quantify spasticity .
There were conflicting results regarding biomechanical-based parameters that quantify the resistance to passive stretch .
AIM The first aim of this study was to systematic ally review and critically assess manually controlled instrumented spasticity assessment methods that combine multidimensional signals .
The second aim was to extract a set of quantified parameters that are psychometrically sound to assess spasticity in a clinical setting .
Objective motor response onset detection in surface myoelectric signals.
Precise detection of discrete motor events like the onsets of voluntary muscle contractions is a prerequisite for various psychophysiological approaches in sensorimotor system analysis . In biomedical research and clinical diagnosis , motor events frequently are determined from surface electromyographic ( SEMG ) signals by some computerized detection algorithm . However , little is known about the reliability and accuracy of these methods , which frequently rely on intuitive and heuristic criteria . Therefore , the systematic approach to computerized detection of discrete motor events from SEMG signals presented by this paper fills a basic gap in EMG signal processing . Based upon a dynamic process model for the SEMG signal , a formal detection scheme is established which incorporates the essential processing modules common to the majority of algorithms . In addition , using concepts of statistically optimal change detection in r and om processes , a new model-based algorithm is presented which serves as a reference for optimum performance . The validity of this concept is demonstrated for the specific example of accurate detection of muscle activation onsets in rapid voluntary contractions ; the estimation error ( i.e. , the deviation between estimated and " true " onset time ) was evaluated by statistical simulations for three representative methods . Results show a substantial decrease of performance of traditional methods in the case of highly variable dynamic muscle activation profiles and /or superimposed activation patterns ( e.g. , due to a secondary motor task simultaneously executed by the same muscle ) . The model-based approach provided significantly more accurate results , even when the exact model parameters were unknown but estimated from the SEMG signal actually measured . It is concluded that the detection algorithm has to be critically taken into consideration during interpretation of motor events resolved from SEMG signals . The process model together with the corresponding statistically optimal detector represents an efficient tool for selecting appropriate detection algorithms for a particular experimental condition , and it allows a quantitative assessment of their performance
Parameters that integrated electrophysiological signals with joint movement characteristics were best able to quantify spasticity .
There were conflicting results regarding biomechanical-based parameters that quantify the resistance to passive stretch .
AIM The first aim of this study was to systematic ally review and critically assess manually controlled instrumented spasticity assessment methods that combine multidimensional signals .
The second aim was to extract a set of quantified parameters that are psychometrically sound to assess spasticity in a clinical setting .