Wearable Rehabilitation Device for the Training of Movements Based on the Mapping of the Real Time Angle Values

Abstract

Most of The movement disabilities occur either because of some kind of injury or because of some kind of disorders in the body. Disabilities related to hand movements are more common. These kinds of disabilities require specific training that is to be done in order to regain the normal movements of the specific part. The movements of arms are monitored by measuring the 3D angles made using IMU gyro sensors interfaced with the microcontroller. Mapping of these values is used to generate a specific output which varies with the varying angular values and is used to specify the range of the movements which are required for the training. The magnitude of actuation varies with the varying values of the measured angle which indicate the improvement in the movements. The device is able to set the range of the motion for the movement. The intensity of the buzzer decreases with the increasing value of angle and totally disappears as the pre-set value of the angle is achieved. The basic principle behind the device is the decrease of analog output of the microcontroller calibrated with the value of gyro sensor such that the output decreases with increase in the value of the gyro sensor. This device can be used to monitor the improvement in the movement and also can be used to monitor the accomplishment of the task suggested for movement rehabilitation. As the output of the device is very easy to interpret so can be used by any person. The patient can monitor their movements by just observing changes in the output.

Country : India

1 Pranmya Ratnaparkhi

  1. Student, VIT University Bhopal, India

IRJIET, Volume 5, Issue 6, June 2021 pp. 35-41

doi.org/10.47001/IRJIET/2021.506008

References

  1. NASAM – National Stroke Association of Malaysia. http://www.nasam.org/english/prevention-what_is_a_stroke.php, 2013.
  2. Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S. A survey on robotic device for upper limb. Journal of Neuro Engineering, 2014.
  3. Chang, W.H., Kim, Y. Robot-assisted therapy in stroke rehabilitation. Journal of Stroke. 2013. p. 174-181.
  4. Krebs, H.I., Ferraro, M., Buerger, S.P., Newbery, M.J., Makiyama, A., Sandmann, M., Hogan, N. Rehabilitation robotics: Pilot trial of a spatial extension for MIT-Manus. Journal of Neuro Engineering.
  5. Lu. C.E. Development of an Upper Limb Robotic Device for Stroke Rehabilitation.
  6. Masiero, S., Armani, M. Rosati, G. Upper-limb robot-assisted therapy in rehabilitation of acute stroke patients: Focused review and results of new randomized controlled trial. Journal of Rehabilitation Research and Development. 2011. p. 355-366.
  7. Kahn, L.E., Zygman, M.L., Rymer, W.Z., Reinkensmeyer, D.J. Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: A randomized controlled pilot study. 2006. Journal of NeuroEngineering and Rehabilitation.
  8. Rahman, T., Sample, W., Jayakumar, S., King, M.M., Wee, J.Y., Seliktar, R., Alexander, M., Scavina, M., Clark, A. Passive  exoskeletons for assisting limb movement. Journal of Rehabilitation Research & Development. 2006. p. 583-590.
  9. Mihelj, M., Nef, T., Riener, R. ARMin – Towards a six DoF upper limb rehabilitation robot. 2006. Biomedical Robotics and Biomechatronics. 2006. p. 1154-1159.
  10. Annisa, J., Mohamaddan, S., Jamaludin, M.S., Noor Aliah Abd., M., Omar, A., Helmy, H., Norafizah, A. Development of Upper Limb Rehabilitation Robot Prototype for Home Setting.
  11. R. Ambar, M. S. Ahmad, and M. M. Abdul Jamil, “Design and Development of Arm Rehabilitation Monitoring Device”, IFMBE proceedings: vol. 35, pp. 781-784, 5th Kuala Lumpur International Conference on Biomedical Engineering (Biomed), Kuala Lumpur, Malaysia, in conjunction with the 8th asian Pacific Conference on Medical and Biological Engineering (APCMBE 2011) 20-23 June 2011, Springer-Verlag Berlin.
  12. Georg Ogris, Matthias Kreil, Paul Lukowicz. “Using Fsr based muscule activity monitoring to recognize manipulative arm gestures”, Wearable Computers, 2007. 11th IEEE International Symposium on Wearable Computers, 2007. pp 45-48.
  13. John Sarik, Ionannis Kymissis (2010). Lab Kits Using the Arduino Prototyping Platform. 40th ASEE/IEEE Frontiers in Education Conference, 2010.
  14. Ryan Fitzhenry (2009). Design and Develop Virtual Reality Games Utilising the “Anti-gravity” Arm Support for Stroke Rehabilitation Therapy. Degree thesis 2009, Faculty of Engineering and Surveying, University of Southern Queensland, Australia.
  15. Muscular Dystrophy Association (MDA) ALS Division (2010). Everyday Life with ALS: A Practical Guide. MDA Publications Department.
  16. Peggy A. Houglum. (2010). Therapeutic Exercise for Musculoskeletal Injuries. Human Kinetics Publisher, 229.
  17. Taborri, J.; Palermo, E.; Rossi, S.; Cappa, P. Gait Partitioning Methods: A Systematic Review. Sensors 2016, 16, 66.
  18. Chiraka Phaisarn, N. Measurement and Analysis System of the Knee Joint Motion in Gait Evaluation for Rehabilitation Medicine. In Proceedings of the Fourth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP), Bangkok, Thailand, 6–8 May 2014.
  19. Moufawad el Achkar, C.; Lenoble-Hoskovec, C.; Paraschiv-Ionescu, A.; Major, K.; Büla, C.; Aminian, K. Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes. Sensors 2016, 16, 1225. Appl. Sci. 2018, 8, 2032 14 of 14.
  20. Ladha, C.; O’Sullivan, J.; Belshaw, Z.; Asher, L. Gait Keeper: A System for Measuring Canine Gait. Sensors 2017, 17, 309.
  21. Duong, P.D.; Suh, Y.S. Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors. Sensors 2015, 15, 15888–15902.
  22. Boussaad, M.; Sijobert, B.; Mombaur, K.; Azevedo Coste, C. Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data. Sensors 2016, 16, 12.
  23. Zhou, Q.; Zhang, H.; Lari, Z.; Liu, Z.; El-Sheimy, N. Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application. Sensors 2016, 16, 1752.
  24. Mitschke, C.; Heß, T.; Milani, T.L. Which Method Detects Foot Strike in Rearfoot and Forefoot Runners Accurately when Using an Inertial Measurement Unit? Appl. Sci. 2017, 7, 959.
  25. Kok,M.;Hol,J.D.;Schön,T.B.And Optimization-based approach human body motion capture using inertial sensors. IFAC Proc. Vol. 2014, 47, 79–85.