Inter-session Reliability of Robot-Measured Parameters for the Evaluation of Upper Limb Recovery

Rehabilitation robots have built-in technology and sensors that allow accurate measurement of movement kinematics and kinetics, which can be used to derive measures related to upper limb performance and highlight changes in motor behavior due to rehabilitation. This study aimed to assess the test-retest reliability of some robot-measured parameters by analyzing their inter-session (day-by-day) variability. The study was carried out using the MEMOS device, in a group of 14 patients after stroke who practiced two different motor tasks consisting of point-to-point reaching movements in the shape of two geometrical figures specifically selected for the assessment of global and directional (8 directions of the workspace) test-retest reliability. The reliability of 4 parameters measuring movement velocity, accuracy, efficiency and smoothness was assessed by the Intra-class Correlation Coefficient, Standard Error of Measurements and Coefficient of Variation. Patients enrolled in the study had high reproducibility values. Directional analysis showed that in some parameters the reliability was generally high but not homogeneous in all directions. In addition, some directions showed systematic error. This study demonstrates that robot-measured parameters are reliable and can be considered ideal candidates for use in combination with clinical scales assessing functional impairment to evaluate motor improvement during robot-assisted neurorehabilitation.

[1]  V. Sanguineti,et al.  Robotic Assessment of Upper Limb Motor Function After Stroke , 2012, American journal of physical medicine & rehabilitation.

[2]  R. Colombo,et al.  Measuring Changes of Movement Dynamics During Robot-Aided Neurorehabilitation of Stroke Patients , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Janice I. Glasgow,et al.  Assessment of Upper-Limb Sensorimotor Function of Subacute Stroke Patients Using Visually Guided Reaching , 2010, Neurorehabilitation and neural repair.

[4]  Etienne Burdet,et al.  A Robust and Sensitive Metric for Quantifying Movement Smoothness , 2012, IEEE Transactions on Biomedical Engineering.

[5]  Ronney B Panerai,et al.  Reliability of dynamic cerebral autoregulation measurement using spontaneous fluctuations in blood pressure. , 2009, Clinical science.

[6]  Gert Kwakkel,et al.  Impact of Time on Improvement of Outcome After Stroke , 2006, Stroke.

[7]  J. Weir Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. , 2005, Journal of strength and conditioning research.

[8]  S. Micera,et al.  Robotic techniques for upper limb evaluation and rehabilitation of stroke patients , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  P. Dario,et al.  Assessing Mechanisms of Recovery During Robot-Aided Neurorehabilitation of the Upper Limb , 2008, Neurorehabilitation and neural repair.

[10]  Silvestro Micera,et al.  Tracking Motor Improvement at the Subtask Level During Robot-Aided Neurorehabilitation of Stroke Patients , 2012, Neurorehabilitation and neural repair.

[11]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .