Relationship Between Clinical Assessments of Function and Measurements From an Upper-Limb Robotic Rehabilitation Device in Cervical Spinal Cord Injury

Abstract

Upper limb robotic rehabilitation devices can collect quantitative data about the user's movements. Identifying relationships between robotic sensor data and manual clinical assessment scores would enable more precise tracking of the time course of recovery after injury and reduce the need for time-consuming manual assessments by skilled personnel. This study used measurements from robotic rehabilitation sessions to predict clinical scores in a traumatic cervical spinal cord injury (SCI) population. A retrospective analysis was conducted on data collected from subjects using the Armeo Spring (Hocoma, AG) in three rehabilitation centers. Fourteen predictive variables were explored, relating to range-of-motion, movement smoothness, and grip ability. Regression models using up to four predictors were developed to describe the following clinical scores: the GRASSP (consisting of four sub-scores), the ARAT, and the SCIM. The resulting adjusted R(2) value was highest for the GRASSP "Quantitative Prehension" component (0.78), and lowest for the GRASSP "Sensibility" component (0.54). In contrast to comparable studies in stroke survivors, movement smoothness was least beneficial for predicting clinical scores in SCI. Prediction of upper-limb clinical scores in SCI is feasible using measurements from a robotic rehabilitation device, without the need for dedicated assessment procedures.

Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: A review

“…O Sensitivity to the change: Metrics must have the ability of detecting changes if any clinical change (positive or negative) has occurred in relation to the functional ability of a subject (Murphy etal.,2013). -Predictive measures: Metrics are used for predicting the outcomes in clinical scales by the application of regression models (Zariffa et al, 2012).…”

“…-Reaching range of motion Related to the hand displacement during a task, it was computed in Cartesian coordinates x,y,z during reaching movements (Zariffa et al, 2012). Balasubramanian et al defined the reachable workspace as the region that can be reached by the subject voluntarily (Balasubramanian et al, 2012a) (Fig.…”

“…The most frequently used parameter was the maximum hand velocity ( , 2007, 2008). Other studies analyzed only the mean hand velocity during the movement (Bosecker et al, 2010;Celik et al, 2010;Colombo et al, 2005Colombo et al, , 2012Rohrer et al, 2002;Zariffa et al, 2012). People with movement disorders after neurological injury perform the movements with several peaks in the hand velocity profile.…”

“…In a healthy subject this value should be close to one, but in the presence of movement disorders this metric could detect alterations during the movement pattern related to various acceleration and deceleration periods or the presence of brisk movements (Merlo et al, 2013). Several studies proposed this metric during reaching movements (Bosecker et al, 2010;Merlo et al, 2013;Rohrer et al, 2002;Zariffa et al, 2012) and path drawing (Bosecker et al, 2010). …”

“…On the other hand, devices for robot-aided neurorehabilitation are increasingly being incorporated into patients' care programs after stroke (Bosecker et al, 2010;Colombo et al, 2008Colombo et al, , 2012Ellis et al, 2008;Zoilo et al, 2011) or spinal cord injury (Zariffa et al, 2012), and these devices can serve as precise and reliable measurement tools that record simultaneously kinematic and kinetic data. Recently, Bosecker et al (2010) and Colombo et al (2008Colombo et al ( , 2012 have provided the first experiences of evaluation metrics based on robot kinematic data for quantifying motor recovery of stroke patients during robot-aided rehabilitation.…”