MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation
References:
[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. http://www.delsys.com/, 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. http://www.barrett.com/products-arm.htm,
Accessed on 03/22/2017.
[21] Microsoft Kinect. https://developer.microsoft.com/en-us/windows/kinect/develop,
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.