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

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2012-05, Vol.20 (3), p.341-350
• Main Authors: Zariffa, José, Kapadia, Naaz, Kramer, John L. K., Taylor, Philippa, Alizadeh-Meghrazi, Milad, Zivanovic, Vera, Albisser, Urs, Willms, Rhonda, Townson, Andrea, Curt, Armin, Popovic, Milos R., Steeves, John D.
• Format: Article
• Language: English
• Subjects: Action Research Arm Test (ARAT); Adult; Biomechanical Phenomena; Cervical Vertebrae - injuries; Data Collection; Data models; Female; Graded and Redefined Assessment of Strength; Hand - physiology; Hand Strength - physiology; Humans; Joints; Linear Models; Male; Middle Aged; Models, Statistical; Movement - physiology; Predictive models; Range of Motion, Articular; regression analysis; Reproducibility of Results; Retrospective Studies; Robot sensing systems; robotic rehabilitation; Robotics; Robotics - instrumentation; Robotics - methods; Sensibility and Prehension (GRASSP); Spinal Cord Independence Measure (SCIM); Spinal Cord Injuries - diagnosis; Spinal Cord Injuries - physiopathology; Spinal Cord Injuries - rehabilitation; Spinal cord injury; Studies; Training; Treatment Outcome; upper extremity; Upper Extremity - physiology
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2011.2181537

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.