Robotic pilot study for analysing spasticity: clinical data versus healthy controls.

Nitin Seth,Graham W Taylor,Denise Johnson,Hussein A Abdullah,O Brian Allen

doi:10.1186/s12984-015-0103-8

Nitin Seth, Graham W Taylor + Show 3 more

Open Access

https://doi.org/10.1186/s12984-015-0103-8

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Abstract

BackgroundSpasticity is a motor disorder that causes significant disability and impairs function. There are no definitive parameters that assess spasticity and there is no universally accepted definition. Spasticity evaluation is important in determining stages of recovery. It can determine treatment effectiveness as well as how treatment should proceed. This paper presents a novel cross sectional robotic pilot study for the primary purpose of assessment. The system collects force and position data to quantify spasticity through similar motions of the Modified Ashworth Scale (MAS) assessment in the Sagittal plane. Validity of the system is determined based on its ability to measure velocity dependent resistance.MethodsForty individuals with Acquired Brain Injury (ABI) and 45 healthy individuals participated in a robotic pilot study. A linear regression model was applied to determine the effect an ABI has on force data obtained through the robotic system in an effort to validate it. Parameters from the model were compared for both groups. Two techniques were performed in an attempt to classify between healthy and patients. Dynamic Time Warping (DTW) with k-nearest neighbour (KNN) classification is compared to a time-series algorithm using position and force data in a linear discriminant analysis (LDA).ResultsThe system is capable of detecting a velocity dependent resistance (p<0.05). Differences were found between healthy individuals and those with MAS 0 who are considered to be healthy. DTW with KNN is shown to improve classification between healthy and patients by approximately 20 % compared to that of an LDA.ConclusionsQuantitative methods of spasticity evaluation demonstrate that differences can be observed between healthy individuals and those with MAS of 0 who are often clinically considered to be healthy. Exploiting the time-series nature of the collected data demonstrates that position and force together are an accurate predictor of patient health.

Highlights

Upper motor neural (UMN) syndrome is common in those with multiple sclerosis, spinal cord injury, stroke, or other forms of acquired brain injury (ABI)
We present a robotic system customized by physiotherapists (PTs) to quantify upper limb (UL) spasticity for elbow flexion/extension in the sagittal plane
Results of this study provide evidence that the system is capable of distinguishing between healthy individuals and individuals with spasticity using this multidimensional data

Summary

Introduction

Upper motor neural (UMN) syndrome is common in those with multiple sclerosis, spinal cord injury, stroke, or other forms of acquired brain injury (ABI). It can manifest itself in the form of negative features, such as flaccidity and weakness, or positive features such as exaggerated tendon reflexes or spasticity [1]. Spasticity has inconsistent definitions in literature [2], it can be described as a “sustained involuntary activation of muscles” and is commonly attributed to an exaggerated stretch reflex during passive stretch [3] This abnormality of muscle tone becomes clinically apparent as spinal shock or ABI resolves and the individual begins to regain volitional control [4]. Validity of the system is determined based on its ability to measure velocity dependent resistance

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