Robotic rehabilitation for stroke patients has been an active field of research since the 1990s. There has been many studies focusing on mechanical design of robotic devices, design of software and interfaces for the patients and therapists, identifying quantitative and objective measures for motor improvement, and developing different operating modes/scenarios for the devices. However, a unified set of robotic (based on data captured by the robotic device) motor function improvement measures still does not exist. If identified, such measures would enable effective comparison of the outcomes obtained by using different robotic rehabilitation devices and platforms. Typically, the motor improvement outcomes are reported using clinical stroke measures (such as Fugl-Meyer) that are reliable and widely used, albeit only in a pre-treatment and post-treatment fashion, which severely decreases the resolution of analyzing the actual recovery process.

We aimed to identify task and hardware independent robotic motor improvement measures that correlate well with clinical measures. Defining such measures can make it possible to estimate clinical measures from a data file captured by the robotic device. Functional gain comparisons for different robotic rehabilitation protocols or devices will be more reliable and accurate when based on a unified set of robotic measures than when based on heterogeneous robotic measures or on pre- and post-treatment evaluations.

In this project in collaboration with The Institute for Rehabilitation and Research (TIRR), nine stroke patients underwent a hybrid robotic and traditional constraint-induced movement therapy (CIMT) for a month. During the robotic component of the rehabilitation protocol, patients completed a simple target-hitting task using a haptic joystick, which was capable of providing assistive/resistive forces or virtual fixture guides. Correlational analyses using several functional and impairment based clinical measures (Fugl-Meyer upper limb component, action research arm test, motor activity log, Jebsen-Taylor hand function test) and robotic measures (smoothness of movement, trajectory error, average number of target hits per minute and mean tangential speed) revealed strong and significant correlations between clinical motor impairment measures and robotic movement quality measures.