Abstract

Stroke is a severe, frequent, and certain as an international health problem worldwide. Stroke is among most frequent cause of mortality and one of the leading causes of disability in adults. Even with all of the amazing progress made in stroke care, the majority of stroke patients will still require PT intervention if proper care is not provided. This study aims to describe a new, improved gadget that helps stroke patients who are unable to move their hands. The proposed prototype is an accessible smart glove designed to help stroke survivors recover by continuously contracting and relaxing their muscles without the need for physiotherapy. In this project Temperature and Heartbeat sensors are interfaced to microcontroller. Vibration motor will be turned ON to activate hand movement for long time when MEMS sensor remains constant.

Keywords

Physiotherapy Involvement, Stroke Rehabilitation, Smart Glove, MEMS sensor,

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References

  1. G.R.S. Murthy, R.S. Jadon, A review of vision based hand gestures recognition. International Journal of Information Technology and Knowledge Management, 2(2), (2009) 405-410.
  2. P. Garg, N. Aggarwal, S. Sofat. Vision based hand gesture recognition. World academy of science, engineering and technology, 49(1), (2009) 972-977.
  3. F. Karray, M. Alemzadeh, J. Abou Saleh, M.N. Arab, Human-computer interaction: Overview on state of the art. International journal on smart sensing and intelligent systems, 1(1), (2008) 137-159.
  4. M.M. Hasan, P.K. Misra, Brightness Factor Matching For Gesture Recognition System Using Scaled Normalization. International Journal of Computer Science & Information Technology (IJCSIT), 3(2), (2019) 99-167.
  5. X. Li. (2019) Gesture Recognition Based on Fuzzy C- Means Clustering Algorithm, Master's Thesis or Dissertation, Department of Computer Science. The University of Tennessee Knoxville.
  6. S. Mitra, T. Acharya. Gesture Recognition: A Survey. IEEE Transactions on systems, Man and Cybernetics, Part C: Applications and reviews, 37(3), (2017) 311- 324.
  7. S.G. Wysoski, M.V. Lamar, S. Kuroyanagi, A. Iwata, (2019) A Rotation Invariant Approach On Static- Gesture Recognition Using Boundary Histograms And Neural. IEEE Proceedings of the 9th International Conference on Neural Information Processing, Singapura.
  8. Joseph J. LaViola, (1999) A Survey of Hand Posture and Gesture Recognition Techniques and Technology. Brown University, United States.
  9. R.Z. Khan, N.A. Ibraheem, (2012) Survey on gesture recognition for hand image postures. Computer and information science, 5(3), 110.
  10. T.B. Moeslund, E. Granum, A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding, 81, (2019) 231– 268.
  11. N.A. Ibraheem, R.Z. Khan, M.M. Hasan, Comparative study of skin color based segmentation techniques. International Journal of Applied Information Systems (IJAIS), 5(10), (2013) 24-38.
  12. M. Elmezain, A. Al-Hamadi, J. Appenrodt, B. Michaelis, A hidden markov model-based isolated and meaningful hand gesture recognition. International Journal of Electrical, Computer, and Systems Engineering, 3(3), (2009) 156-163.
  13. E. Stergiopoulou, N. Papamarkos, Hand gesture recognition using a neural network shape fitting technique. Engineering Applications of Artificial Intelligence, 22(8), (2009) 1141-1158.
  14. M.M. Hasan, P.K. Mishra, HSV brightness factor matching for gesture recognition system. International Journal of Image Processing (IJIP), 4(5), (2010) 456-467.
  15. Malima, Ozgur, Cetin. (2006). A fast algorithm for vision-based hand gesture recognition for robot control. IEEE 14th Signal Processing and Communications Applications, IEEE, Turkey.
  16. M.M. Hasan, P.K. Mishra, Features fitting using multivariate gaussian distribution for hand gesture recognition. International Journal of Computer Science & Emerging Technologies IJCSET, 3(2), (2012) 73-80.
  17. M.M. Hasan, P.K. Mishra, Robust gesture recognition using gaussian distribution for features fitting. International Journal of Machine Learning and Computing, 2(3), (2012) 266.
  18. W.T. Freeman, M. Roth, Orientation histograms for hand gesture recognition. In International workshop on automatic face and gesture recognition, 12, (1995) 296-301.
  19. B.W. Min, H.S. Yoon, J. Soh, Y.M. Yang, T. Ejima, (1997) Hand gesture recognition using hidden Markov models. In 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, IEEE, USA.