Robot-Assisted Arm Assessments in Spinal Cord Injured Patients: A Consideration of Concept Study

May Robot-Assisted Arm Assessments in Spinal Cord Injured Patients: A Consideration of Concept Study Urs Keller 0 1 Sabine Schlch 0 1 Urs Albisser 0 1 Claudia Rudhe 0 1 Armin Curt 0 1 Robert Riener 0 1 Verena Klamroth-Marganska 0 1 0 1 Sensory-Motor Systems Lab, Department of Health Sciences and Technology ETH Zurich , Zurich , Switzerland , 2 Balgrist University Hospital, University of Zurich , Zurich , Switzerland 1 Academic Editor: Hatem E Sabaawy, Rutgers- Robert wood Johnson Medical School , UNITED STATES Robotic assistance is increasingly used in neurological rehabilitation for enhanced training. Furthermore, therapy robots have the potential for accurate assessment of motor function in order to diagnose the patient status, to measure therapy progress or to feedback the movement performance to the patient and therapist in real time. We investigated whether a set of robot-based assessments that encompasses kinematic, kinetic and timing metrics is applicable, safe, reliable and comparable to clinical metrics for measurement of arm motor function. Twenty-four healthy subjects and five patients after spinal cord injury underwent robotbased assessments using the exoskeleton robot ARMin. Five different tasks were performed with aid of a visual display. Ten kinematic, kinetic and timing assessment parameters were extracted on joint- and end-effector level (active and passive range of motion, cubic reaching volume, movement time, distance-path ratio, precision, smoothness, reaction time, joint torques and joint stiffness). For cubic volume, joint torques and the range of motion for most joints, good inter- and intra-rater reliability were found whereas precision, movement time, distance-path ratio and smoothness showed weak to moderate reliability. A comparison with clinical scores revealed good correlations between robot-based joint torques and the Manual Muscle Test. Reaction time and distance-path ratio showed good correlation with the Graded and Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) and the Van Lieshout Test (VLT) for movements towards a predefined position in the center of the frontal plane. In conclusion, the therapy robot ARMin provides a comprehensive set of assessments that are applicable and safe. The first results with spinal cord injured patients and healthy subjects suggest that the measurements are widely reliable and comparable to clinical scales for arm motor function. The methods applied and results can serve as a basis for the future development of end-effector and exoskeleton-based robotic assessments. - Funding: This study was funded by the Swiss National Science Foundation (SNF) in the framework of the National Centre of Competence in Research (NCCR, 51NF40-111381) transfer project RANA (Robot-Assisted Neurorehabilitation of the Arm). The funders had no role in study design, data collection Competing Interests: The authors have declared that no competing interests exist. Patients who suffer from a neurological disorder such as spinal cord injury (SCI) or stroke often face deficits in motor function. The global-incident rate for traumatic SCI is estimated to be 23 cases per million people (180000 per year) [1]. Stroke has a prevalence of approximately 795000 people in the US (Center of Disease Control and Prevention, 2010). These impairments due to stroke or SCI lead to a restriction of both independence and participation in daily life [2, 3]. An intensive rehabilitative intervention can help to improve motor function in stroke [4] and SCI patients [5] and, eventually, the patients quality of life. Plenty of clinical scores and assessments are available for different diseases, ages, movements and body parts to measure patients motor functions. The assessments are often categorized using the international classification of functioning, disability and health (ICF) [6] to standardize the description of the health status. With this classification the scores can be grouped according to the disability they address, i.e., body functions and structure, activities and participation. Assessments covering these groups can be used for diagnosis of the patients status, as measurement of therapy progress or as feedback about patients performance. However, clinical assessments often show deficits in terms of reliability, validity, sensitivity and duration of execution [7]. Rehabilitation robots have the potential to provide an interface for objective, sensitive and reliable measurements. The prevalent use of robots for therapy and the positive findings of robotassisted therapy contributed to an increased development of robot-assisted assessments in the last five years. Generally two fundamental approaches can be used to evaluate sensorimotor impairment using robot-assisted assessments: Using raw sensor data or feature extraction [7]. The first approach uses raw sensor data to directly extract information from sensors about body functions. Depending on the used sensors and parameters, different robot-assisted assessments have already been described. Several approaches focus on assessments of the upper extremity. In time-based assessments the duration is usually measured that is needed to finish a given point-to-point movement or position adjustment of the hand or a joint (e.g. using the MIT Manus [8], the Delta robot [9], the REAplan [10] or the HapticKnob [11]) or by measuring the time needed for a given task (e.g. using the PHANTOM [12] or the MIT Manus [8]). With sensors that measure kinematic or kinetic information, assessments can be performed such as measuring the joint range of motion (ROM) or the workspace (work area) of the hand that can be reached (e.g. using the Lokomat [13], the ACT3D [14], the ArmeoPower [15] or the Microsoft Kinect [16]) or the mean/peak/tangential speed (e.g. using the MIT Manus [8], the MEMOS [17], the IE2000 haptic joystick [18] or the REAplan [10]). An assessment device which can record forces or torques can be used to measure the active joint strength. This can be done recording the maximum voluntary isometric forces or torques (e.g. using the ARMin [19], its commercial version the ArmeoPower [15] or the Lokomat [20]) or isokinetic forces and torques (e.g. using the Kin-Com Dynamometer [21]). In the feature extraction approach the sensor data is further processed, conditioned and characteristic properties are extracted. Using the time and position information during a movement, the quality of the corresponding joint or hand trajectory can be analyzed. Smoothness is a prominent metric to estimate the quality of a movement. Different metrics were used to calculate smoothness such as the ratio between mean speed and peak speed [10, 18, 22, 23], different jerk metrics [10, 11, 17, 22, 23, 24, 25], tent metric [23], mean arrest period [23], peak metric [17, 23, 25], number of submovements [26], comparison with an idealized normal speed profile [27], number of directional changes [22] or the spe (...truncated)