(Georgia Institute of Technology, 2013-06)
Howard, Ayanna M.; Brooks, Douglas Antwonne; Brown, Edward; Gebregiorgis, Adey; Chen, Yu-ping
In recent years, robot-assisted rehabilitation has gained momentum as a viable means for improving outcomes for therapeutic interventions. Such therapy experiences allow controlled and repeatable trials and quantitative evaluation of mobility metrics. Typically though these robotic devices have been focused on rehabilitation within a clinical setting. In these traditional robot-assisted rehabilitation studies, participants are required to perform goal-directed movements with the robot during a therapy session. This requires physical contact between the participant and the robot to enable precise control of the task, as well as a means to collect relevant performance data. On the other hand, non-contact means of robot interaction can provide a safe methodology for extracting the control data needed for in-home rehabilitation. As such, in this paper we discuss a contact and non-contact based method for upper-arm rehabilitation exercises that enables quantification of upper-arm movements. We evaluate our methodology on upper-arm abduction/adduction movements and discuss the advantages and limitations of each approach as applied to an in-home rehabilitation scenario.
(Georgia Institute of Technology, 2013)
Nixon, Mason; Chen, Yu-ping; Howard, Ayanna M.
Low cost depth sensors could potentially allow for home-based care and rehabilitation using virtual systems. Currently, no publicly available and peer-reviewed assessment has been made on the accuracy of joint position data determined by the Microsoft KinectTM for assessment of upper extremity movements. We devised and validated clinically-based angle classifications for random arm movements in 3D-space, specifically, the shoulder joint flexion/extension angle, shoulder joint abduction/adduction angle, and 3-dimensional shoulder joint angle of 19 subjects at a distance of 2.0m using an eight camera Vicon Motion Capture system. Results show an average absolute error of these angle measurements not exceeding 10.0%.
(Georgia Institute of Technology, 2012-06)
Brooks, Douglas Antwonne; Chen, Yu-ping; Howard, Ayanna M.
This research investigates proper movement correlation as well as the overall perception of human subjects’ interaction with a simulated agent and an embodied agent in a physical therapeutic scenario. Using computer vision techniques coupled with the Microsoft Kinect to quantify reaching kinematics, correlation was assessed by aliging movements with a Vicon Motion Capture System as well as determining how well the specific exercises were mimicked. The results indicate that this approach is a viable alternative to Motion Capturing Systems for assessing certain movements during therapy. The results also indicate that there is some dependence on the use of an embodied agent as opposed to a simulated agent when assessing adherence.