Gesture Recognition System for the Blind

Authors

  • Chandana V Reddy Dept. Electronics and Communication, East West Institute of Technology, Bangalore, India
  • Divya M Ramani Dept. Electronics and Communication, East West Institute of Technology, Bangalore, India
  • Geetha K Dept. Electronics and Communication, East West Institute of Technology, Bangalore, India
  • Harshitha K Dept. Electronics and Communication, East West Institute of Technology, Bangalore, India
  • Srinivas Babu P Electronics and Communication, East West Institute of Technology, Bangalore, India

DOI:

https://doi.org/10.5281/zenodo.5762140

Keywords:

Blind, Wearables, Gesture, ATM

Abstract

Several assistive technologies are booming up now-a-days to help the specially abled – it could be guiding walking sticks for the blind community, optical readers, wheelchairs and many more. However, very less research has been conducted on helping the blind access ATMs. This paper focuses on existing gesture recognition techniques that can be hooked up with ATM machines for accessing them without the keys.

Downloads

Download data is not yet available.

References

S. Huang, C. Mao, J. Tao and Z. Ye, "A Novel Chinese Sign Language Recognition Method Based on Keyframe-Centered Clips," in IEEE Signal Processing Letters, vol. 25, no. 3, pp. 442-446, March 2018.

Caporusso N., Biasi L., Cinquepalmi G., Trotta G.F., Brunetti A., Bevilacqua V. (2018) A Wearable Device Supporting Multiple Touch- and Gesture-Based Languages for the Deaf-Blind. In: Ahram T., Falcão C. (eds) Advances in Human Factors in Wearable Technologies and Game Design. AHFE 2017. Advances in Intelligent Systems and Computing, vol 608. Springer, Cham

B. G. Lee and S. M. Lee, "Smart Wearable Hand Device for Sign Language Interpretation System With Sensors Fusion," in IEEE Sensors Journal, vol. 18, no. 3, pp. 1224-1232, 1 Feb.1, 2018.

B. Lee and W. Chung, "Wearable Glove-Type Driver Stress Detection Using a Motion Sensor," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 7, pp. 1835-1844, July 2017.

S. R. Rupanagudi et al., "A high speed algorithm for identifying hand gestures for an ATM input system for the blind," 2015 IEEE Bombay Section Symposium (IBSS), Mumbai, 2015, pp. 1-6.

T. Starner and A. Pentland, “Visual recognition of American sign language using hidden Markov model,” in Proc. IEEE Int. Conf. Autom. Face Gesture Recognit., 1995, pp. 1–52.

C. Wang, W. Gao, and S. Shan, “An Approach based on phonemes to large vocabulary Chinese sign language recognition,” in Proc. IEEE Int. Conf. Autom. Face Gesture Recognit., 2002, pp. 393–398.

P. Jangyodsuk, C. Conly, and V. Athitsos, “Sign language recognition using dynamic time warping and hand shape distance based on histogram of oriented gradient features,” in Proc. ACM Int. Conf. Pervasive Technologies Related Assistive Environments, 2014, pp. 1–6.

G. Marin, F. Dominio, and P. Zanuttigh, “Hand gesture recognition with leap motion and Kinect devices,” in Proc. IEEE Int. Conf. Image Process., 2014, pp. 1565–1569.

J. Zhang, W. Zhou, X. Chao, J. Pu, and H. Li, “Chinese sign language recognition with adaptive HMM,” in Proc. IEEE Int. Conf. Multimedia Expo., 2016, pp. 1–6.

Akshay Dekate, Anam Kamal, Surekha K.S.-E&TC, AlT, Pune “MAGIC GLOVE- WIRELESS HAND GESTURE HARDWARE CONTROLLER” IEEE Electronics and Communication systems (ICECS)

Downloads

Published

2021-11-05

How to Cite

[1]
C. V. Reddy, D. M. Ramani, G. K, H. K, and S. B. P, “Gesture Recognition System for the Blind”, pices, vol. 5, no. 7, pp. 73-75, Nov. 2021.

URN

Most read articles by the same author(s)