Robotic Assessment System for Spasticity in Patients with Acquired Brain Injury

Abstract

Spasticity is a symptom of upper motor neuron (UMN) syndrome that commonly affects individuals suffering the effects of stroke, multiple-sclerosis, spinal cord injury, or acquired brain injury. Current clinical standards and methods available for assessing and quantifying the effects of the UMN syndrome are considered to lack sensitivity and do not properly reflect the condition of the patient. This work discusses the analysis and quantification of spasticity in patients with acquired brain injury. A sensor integrated robotic system was developed in close consultations with physiotherapists to assist in the spasticity assessment and monitoring of individuals receiving care. Resistive force measurements have been obtained from individuals undergoing flexion and extension of the elbow joint in the sagittal plane. Repetitions were performed at progressively increasing speeds in an effort to capture the velocity dependent resistance to passive motion that is commonly attributable to spasticity. The goal of this research was to collect multidimensional healthy control and clinical data to provide insight into patients' condition and their evaluations. An analysis was performed on the force, position, and time data along with other metrics developed to assist in assessment. These variables and metrics were compared against the traditional spasticity evaluation scale, the Modified Ashworth Scale (MAS), to demonstrate that similar effects are being captured while providing more information regarding the individual. The healthy control data are presented as a reference for clinicians to observe what quantified baseline data "looks like", including quantiles that may be used for patient tracking or monitoring. Results demonstrate that the system is capable of detecting the effects of spasticity while relating it to the MAS. This study helps uncover the nature of the MAS scale and illustrates that individuals who scored 0 on the MAS scale are closely related to healthy individuals but still distinct. The multidimensional aspect of the data is leveraged to differentiate different levels of the MAS. Although MAS 0's and healthy individuals present similar data, the multidimensional nature allows intensive comparison techniques such as dynamic time warping, to distinguish between the two accurately.