Stroke is a leading cause of long-term disabilities in the United States, affecting about 6.5 million Americans. With a decreasing stroke mortality and increase in the aging population, the number of people requiring rehabilitation after a stroke is projected to increase, creating a critical need to improve the effectiveness of stroke rehabilitation services. To address this challenge, this Faculty Early Career Development Program (CAREER) project introduces a new robot-aided rehabilitation framework, namely Transparent Robot-Aided Rehabilitation (TRAIN). This framework builds upon "enhanced transparency" in two distinct aspects: (1) transparency in terms of understanding altered biomechanics following stroke and (2) transparency in physical human-robot interaction, i.e., the concept of a robot being physically imperceptible to a human during motor tasks. The project focuses on developing the TRAIN framework for shoulder rehabilitation and testing the effectiveness for stroke survivors. Shoulder dysfunction is one of the most common complications following stroke, but robot-aided shoulder rehabilitation has not yet been fully explored. Successful application of the TRAIN framework to robotic shoulder exercise therapy will directly benefit the overall motor function of the upper extremity including improved range of motion, strength, and stability. In addition, successful application will lead to secondary benefits of improving the quality of life, such as reduced fatigue during motor tasks and improved independence in daily activities. Research activities and outcomes of this project will be seamlessly integrated into various education and outreach programs in order to excite and attract a diverse group of students, inspire them to pursue careers in STEM, and train next-generation scientists and engineers in robotics and human movement science. A unique "Outreach on Demand" program will promote outreach opportunities for underrepresented minority students.
The investigator's long-term research goal is to advance robot-aided rehabilitation through integrated innovations in robot design, controller design, and refined quantification (system identification) of the neuromuscular system. Toward this goal, this project will produce a transformative framework for shoulder rehabilitation using integrated innovations in robot design (a lightweight, parallel-actuated shoulder exoskeleton robot), system identification (refined quantification of 3D shoulder impedance), and controller design (a biomechanics-based active impedance controller). Integration of a novel lightweight, parallel-actuated shoulder exoskeleton robot that minimally impacts natural arm dynamics, in combination with a fast and robust system identification algorithm, will advance understanding of how brain injury due to stroke alters 3D shoulder impedance. The Research Plan is organized under 4 thrusts. The FIRST THRUST is to develop a 5-DOF lightweight, parallel, actuated shoulder exoskeleton Robot. The robot will consist of a fully-actuated/motor driven 3-DOF spherical parallel manipulator (SPM) and a 2-DOF passive slip interface. The SPM consists of three parallel actuators connected to a shoulder piece coupled to the user; the slip interface is a cuff placed on the user's upper arm and is also coupled to the shoulder piece. The actuators are coupled to provide access to a spherical workspace. The optimal configuration of the robot will be determined and its ability to apply precise perturbations and simulate a wide range of impedances at the shoulder joint will be evaluated. The SECOND THRUST is to quantify 3D shoulder impedance during dynamic motor Tasks. Using the optimized robot, a robust system identification algorithm well be developed to quantify 3D shoulder impedances (stiffness, damping and inertia) in the direction of arm movements in young subjects with no history of neuromuscular disorders. The robot and algorithms developed will then be used to determine how shoulder impedance during normal shoulder functions is altered in stroke patients with chronic hemiparesis. The THIRD THRUST is to develop a biomechanics-based active impedance controller. An active impedance controller will further enhance transparency by altering the damping resistance from the robot in response to the user's intent of motion, e. g., to lower damping to reduce the experience of undesired resistance when the user intends to move in a direction. The effectiveness of the controller, i.e. its ability to increase transparency by lowering muscle activity/effort, will then be assessed in the Thrust 2 stroke patients. The FOURTH THRUST is to develop and evaluate patient-specific, adaptive exercise therapy based on the TRAIN Framework. Based on quantification of altered shoulder impedance in each stroke patient participating in earlier Thrusts, robotic exercise therapy that aims to correct altered shoulder impedance towards the unimpaired baseline (as determined from age-matched unaffected controls) will be developed. A 6-week (12 sessions) patient specific robotic training program that provides a unique set of strengthening and stretching exercises will be designed to adjust robotic impedance based on assessment of the patient's motor performance. Finally, the exercise program will be evaluated in the patients for whom they were designed and the effects on improvement of shoulder motor function will be assessed post training and at a 3-month follow-up.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
