Semiautonomous Robotassisted Cooperative Therapy Exercises for a Therapistxe2x80x99s Interaction with a Patient Recent increases in demand for poststroke motor rehabilitation services together with limited time of therapist and accessibility issues, in particular for patients living in remote areas, have created a significant burden on healthcare systems worldwide. Semiautonomous techniques that allow for sharing the time of a therapist between multiple patients have attracted great interest. Among them Learning from Demonstration (LfD) based robots have been studied as solutions to address this growing demand. In this work, a Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) based LfD approach are proposed to generate a versatile framework to deliver rehabilitation in the absence of the therapist. To collect data for training the models, a bilateral telerehabilitation system is used to enable patienttherapist collaborative task performance is one Degree of Freedom (DOF). The performance and generalizability of the trained model are demonstrated for a variety of patient actions.

Investigating Human Factors in the HandHeld Gaming Interface of a Telerehabilitation Robotic System. Robotic devices can be used as effective tools to provide rehabilitation supports to stroke patients. Such robotic rehabilitation practices can replace therapists providing rehabilitation therapy to patients. Patients can independently practice rehabilitation with robotic rehabilitation devices for longer hours. These advantages are now motivating patients to use rehabilitation robots. However, contemporary rehabilitation practices are expensive, and patients need to travel to rehabilitation centers for rehabilitation, which is time consuming and burdensome. This is why, the concepts of telerehabilitation are becoming more and more popular where the patients can practice rehabilitation at homes, and the therapists can remotely monitor the rehabilitation practices and communicate with the patients if necessary. However, such rehabilitation practices, realtime monitoring of patients by therapists from distant places and communication of patients with therapists may be more intuitive and humanfriendly if the patients can operate the rehabilitation system and communicate with the therapists using a handheld interface. Again, the handheld interface may be more intuitive and engaging if the rehabilitation performance can be expressed through some gamelike activities. To do so, a clear understanding of human factors involved in the gaming interface is necessary. However, such knowledge is not available in the literature. To address this knowledge gap, in this paper, the human factors associated with the operation of a handheld gaming interface for robotassisted fullbody smart telerehabilitation of stroke patients are investigated. At first, potential human factors associated with the operation are identified through surveys conducted with healthcare professionals, researchers and patients. The identified human factors are then analyzed and divided into different categories, e.g. physical and cognitive human factors. The role of each human factor in the interface operation is explained. The findings can be utilized to design and develop handheld gaming interfaces for robotassisted telerehabilitation that may be more humanfriendly and intuitive.