A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation

This paper proposes a new robotic rehabilitation training platform that is motivated by the requirement for adjusting the training strategy and intensity in a patient-specific manner. The platform is implemented for tele-rehabilitation and is comprised of a haptic device operated by therapists, a lightweight exoskeleton worn by patients and a visually shared model. Through the visually shared model, the motion of the therapist and patient are measured and mapped to the motion of the corresponding object. Thus, the force generated by the therapist can be transferred to the patient for delivering training, while real-time force feedback with high transparency can be provided to the therapist so they know the amount of force being applied to patients in real time. In particular, both assistive therapy in the early stages and resistive therapy in the later stages of stroke can be performed. The home-use exoskeleton device is specifically designed to be light-weight and compliant for safety. The patient-exoskeleton and therapist-haptic interaction performance is evaluated by observing the muscle activities and interaction force. Two volunteers were requested to imitate the process of the therapist-in-the-loop training to evaluate the proposed platform.

[1]  Xiong Luo,et al.  Rule-Based Human Motion Tracking for Rehabilitation Exercises: Realtime Assessment, Feedback, and Guidance , 2017, IEEE Access.

[2]  Zhicong Huang,et al.  Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals , 2017, IEEE Transactions on Industrial Electronics.

[3]  R. Teasell,et al.  What’s New in Stroke Rehabilitation , 2004, Stroke.

[4]  Yanhe Zhu,et al.  Design and evaluation of a 7-DOF cable-driven upper limb exoskeleton , 2018 .

[5]  B. Johansson.  Brain plasticity and stroke rehabilitation. The Willis lecture. , 2000, Stroke.

[6]  Shuxiang Guo,et al.  Design of a Novel Telerehabilitation System with a Force-Sensing Mechanism , 2015, Sensors.

[7]  Arantza Illarramendi,et al.  A Telerehabilitation System for the Selection, Evaluation and Remote Management of Therapies , 2018, Sensors.

[8]  Sunil K. Agrawal,et al.  Human Movement Training With a Cable Driven ARm EXoskeleton (CAREX) , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Mahdi Tavakoli,et al.  A Passivity-Based Approach for Stable Patient–Robot Interaction in Haptics-Enabled Rehabilitation Systems: Modulated Time-Domain Passivity Control , 2017, IEEE Transactions on Control Systems Technology.

[10]  Veronica Teichrieb,et al.  Guidance and Movement Correction Based on Therapeutics Movements for Motor Rehabilitation Support Systems , 2012, 2012 14th Symposium on Virtual and Augmented Reality.

[11]  Yili Fu,et al.  Integrating Compliant Actuator and Torque Limiter Mechanism for Safe Home-Based Upper-Limb Rehabilitation Device Design , 2017 .

[12]  Hidenori Ishihara,et al.  Muscle Strength Assessment System Using sEMG-Based Force Prediction Method for Wrist Joint , 2016 .

[13]  Hyung-Soon Park,et al.  Two-Channel Transparency-Optimized Control Architectures in Bilateral Teleoperation With Time Delay , 2013, IEEE Transactions on Control Systems Technology.

[14]  Shuxiang Guo,et al.  Implementation of Resistance Training Using an Upper-Limb Exoskeleton Rehabilitation Device for Elbow Joint , 2014 .

[15]  Yves Rybarczyk,et al.  Implementation and Assessment of an Intelligent Motor Tele-Rehabilitation Platform , 2019, Electronics.

[16]  Albert Wang,et al.  Directional efficiency in geared transmissions: Characterization of backdrivability towards improved proprioceptive control , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Nicola Vitiello,et al.  NEUROExos: A Powered Elbow Exoskeleton for Physical Rehabilitation , 2013, IEEE Transactions on Robotics.

[18]  A. Eagger.  Rehabilitation , 1960 .

[19]  Shuxiang Guo,et al.  Coordinative Motion-Based Bilateral Rehabilitation Training System with Exoskeleton and Haptic Devices for Biomedical Application , 2018, Micromachines.

[20]  C.-C.K. Lin,et al.  A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Bram Vanderborght,et al.  Online Reconfiguration of a Variable-Stiffness Actuator , 2018, IEEE/ASME Transactions on Mechatronics.

[22]  J. Hidler,et al.  Multicenter Randomized Clinical Trial Evaluating the Effectiveness of the Lokomat in Subacute Stroke , 2009, Neurorehabilitation and neural repair.

[23]  Francisco Javier Díaz-Pernas,et al.  Rehabilitation Using Kinect-based Games and Virtual Reality☆ , 2015 .

[24]  Jesse Hoey,et al.  A decision-theoretic approach in the design of an adaptive upper-limb stroke rehabilitation robot , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[25]  George C. Verghese,et al.  Design issues in 2-port network models of bilateral remote manipulation , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[26]  Shuxiang Guo,et al.  Safety Operation Consciousness Realization of a MR Fluids-Based Novel Haptic Interface for Teleoperated Catheter Minimally Invasive Neurosurgery , 2016, IEEE/ASME Transactions on Mechatronics.

[27]  Yves Rybarczyk,et al.  Smart Web-Based Platform to Support Physical Rehabilitation , 2018, Sensors.

[28]  Marcia Kilchenman O'Malley,et al.  Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation , 2016, IEEE Transactions on Robotics.

[29]  Shuxiang Guo,et al.  Prediction of interaction force using EMG for characteristic evaluation of touch and push motions , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[30]  Rory A. Cooper,et al.  Using the kinect to limit abnormal kinematics and compensation strategies during therapy with end effector robots , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[31]  Danwei Wang,et al.  Learning impedance control for robotic manipulators , 1998, IEEE Trans. Robotics Autom..

[32]  R. Riener,et al.  Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review , 2012, Psychonomic Bulletin & Review.

[33]  Mahdi Tavakoli,et al.  A Computational-Model-Based Study of Supervised Haptics-Enabled Therapist-in-the-Loop Training for Upper-Limb Poststroke Robotic Rehabilitation , 2018, IEEE/ASME Transactions on Mechatronics.

[34]  M. Roizen.  Forecasting the Future of Cardiovascular Disease in the United States: A Policy Statement From the American Heart Association , 2012 .

[35]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part II—Implementation , 1985 .

[36]  Shuxiang Guo,et al.  Development of an upper extremity motor function rehabilitation system and an assessment system , 2011, Int. J. Mechatronics Autom..

[37]  Shuxiang Guo,et al.  Training Model-based Master-slave Rehabilitation Training Strategy using the Phantom Premium and an Exoskeleton Device , 2014 .

[38]  Shuxiang Guo,et al.  Design Process of Exoskeleton Rehabilitation Device and Implementation of Bilateral Upper Limb Motor Movement , 2011 .

[39]  Sunil Kumar Agrawal,et al.  Transition from mechanical arm to human arm with CAREX: A cable driven ARm EXoskeleton (CAREX) for neural rehabilitation , 2012, 2012 IEEE International Conference on Robotics and Automation.