Background. High-intensity repetitive training is challenging to provide poststroke. Robotic approaches can facilitate such training by unweighting the limb and/or by improving trajectory control, but the extent to which these types of assistance are necessary is not known.
Objective. The purpose of this study was to examine the extent to which robotic path assistance and/or weight support facilitate repetitive 3D movements in high functioning and low functioning subjects with poststroke arm motor impairment relative to healthy controls.
Methods. Seven healthy controls and 18 subjects with chronic poststroke right-sided hemiparesis performed 300 repetitions of a 3D circle-drawing task using a 3D Cable-driven Arm Exoskeleton (CAREX) robot. Subjects performed 100 repetitions each with path assistance alone, weight support alone, and path assistance plus weight support in a random order over a single session. Kinematic data from the task were used to compute the normalized error and speed as well as the speed-error relationship.
Results. Low functioning stroke subjects (Fugl-Meyer Scale score = 16.6 ± 6.5) showed the lowest error with path assistance plus weight support, whereas high functioning stroke subjects (Fugl-Meyer Scale score = 59.6 ± 6.8) moved faster with path assistance alone. When both speed and error were considered together, low functioning subjects significantly reduced their error and increased their speed but showed no difference across the robotic conditions.
Conclusions. Robotic assistance can facilitate repetitive task performance in individuals with severe arm motor impairment, but path assistance provides little advantage over weight support alone. Future studies focusing on antigravity arm movement control are warranted poststroke.
via The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis – Preeti Raghavan, Seda Bilaloglu, Syed Zain Ali, Xin Jin, Viswanath Aluru, Megan C. Buckley, Alvin Tang, Arash Yousefi, Jennifer Stone, Sunil K. Agrawal, Ying Lu, 2020