Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32586
MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation

Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon


This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: Human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1206


[1] Sivan, Manoj, et al. ”Home-based Computer Assisted Arm Rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting.” Journal of neuroengineering and rehabilitation 11.1 (2014): 1.
[2] Mukhopadhyay, Subhas Chandra. ”Wearable sensors for human activity monitoring: A review.” IEEE Sensors Journal 15.3 (2015): 1321-1330.
[3] Theofanidis Michail, Lioulemes Alexandros, and Makedon Fillia. ”A Motion and Force Analysis System for Human Upper-limb Exercises.” International Conference on PErvasive Technologies Related to Assistive Environments,(PETRA), Corfu Island Greece. 2016.
[4] Delsys, Inc., Accessed on 03/22/2017.
[5] H. S. Lo, S. Q. Xie, ”Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects, Medical Engineering & Physics”, Volume 34, Issue 3, Pages 261-268, April 2012,.
[6] G. Maxime, et al. ”A robotic device as a sensitive quantitative tool to assess upper limb impairments in stroke patients: a preliminary prospective cohort study.”Journal of rehabilitation medicine44.3 (2012): 210-217.
[7] Ba. Laurent, et al. ”Joint torque variability and repeatability during cyclic flexion-extension of the elbow.” BMC sports science, medicine and rehabilitation 8.1 (2016): 1.
[8] Cuthbert, Scott C., and George J. Goodheart. ”On the reliability and validity of manual muscle testing: a literature review.” Chiropractic & osteopathy 15.1 (2007)
[9] Jepsen, Jrgen, et al. ”Manual strength testing in 14 upper limb muscles A study of inter-rater reliability.” Acta Orthopaedica Scandinavica 75.4 (2004): 442-448.
[10] Toemen, Angela, Sarah Dalton, and Fiona Sandford. ”The intra-and inter-rater reliability of manual muscle testing and a hand-held dynamometer for measuring wrist strength in symptomatic and asymptomatic subjects.” Hand Therapy 16.3 (2011): 67-74.
[11] Osu, Rieko, and Hiroaki Gomi. ”Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals.” Journal of neurophysiology 81.4 (1999): 1458-1468.
[12] Banala, Sai K., Suni K. Agrawal, and John P. Scholz. ”Active Leg Exoskeleton for gait rehabilitation of motor-impaired patients.” In 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 401-407. IEEE, 2007.
[13] Abujelala, Maher, Alexandros Lioulemes, Paul Sassaman, and Fillia Makedon. ”Robot-aided rehabilitation using force analysis.” In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 97. ACM, 2015.
[14] Phan, Scott, Alexandros Lioulemes, Cyril Lutterodt, Fillia Makedon, and Vangelis Metsis. ”Guided physical therapy through the use of the barrett wam robotic arm.” In Haptic, Audio and Visual Environments and Games (HAVE), 2014 IEEE International Symposium on, pp. 24-28. IEEE, 2014.
[15] Saraee,Elham, Margrit Betke. ”Dynamic Adjustment of Physical Exercises Based on Performance Using the Proficio Robotic Arm.” In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, 2016.
[16] Liu, J., J. L. Emken, S. C. Cramer, and D. J. Reinkensmeyer. ”Learning to perform a novel movement pattern using haptic guidance: slow learning, rapid forgetting, and attractor paths.” In 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., pp. 37-40. IEEE, 2005.
[17] Feygin, David, Madeleine Keehner, and R. Tendick. ”Haptic guidance: Experimental evaluation of a haptic training method for a perceptual motor skill.” In Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2002. HAPTICS 2002. Proceedings. 10th Symposium on, pp. 40-47. IEEE, 2002.
[18] Huq, Rajibul, et al. ”Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.” Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on. IEEE, 2013.
[19] Badesa, Francisco Javier, et al. ”Dynamic Adaptive System for Robot-Assisted Motion Rehabilitation.” IEEE Systems Journal 10.3 (2016): 984-991.
[20] Barrett Technology, LLC., Accessed on 03/22/2017.
[21] Microsoft Kinect., Accessed on 03/22/2017.
[22] L. Ferrajoli and A. De Luca. A modified newton-euler method for dynamic computations in robot fault detection and control. Proceedings - IEEE International Conference on Robotics and Automation, pages 33593364, 2009.
[23] Lioulemes Alexandros, Michail Theofanidis, and Fillia Makedon. ”Quantitative analysis of the human upper-limb kinematic model for robot-based rehabilitation applications.” IEEE Conference on Automation Science and Engineering (CASE), Fort Worth TX. 2016.
[24] Craig, John J. Introduction to robotics: mechanics and control. Vol. 3. Upper Saddle River: Pearson Prentice Hall, 2005.
[25] Gattupalli, S., Lioulemes, A., Gieser, S., N., Sassaman, P., Athitsos, V., Makedon F., ”MAGNI: A Real-Time Robot-Aided Game-Based Tele-Rehabilitation System”., Universal Access in Human-Computer Interaction. 10th International Conference, UAHCI 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016.