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Review
. 2022 Apr 2;8(4):98.
doi: 10.3390/jimaging8040098.

A Comparative Review on Applications of Different Sensors for Sign Language Recognition

Muhammad Saad Amin  1 , Syed Tahir Hussain Rizvi  2 , Md Murad Hossain  3
Affiliations
Review

A Comparative Review on Applications of Different Sensors for Sign Language Recognition

Muhammad Saad Amin et al. J Imaging. .

Abstract

Sign language recognition is challenging due to the lack of communication between normal and affected people. Many social and physiological impacts are created due to speaking or hearing disability. A lot of different dimensional techniques have been proposed previously to overcome this gap. A sensor-based smart glove for sign language recognition (SLR) proved helpful to generate data based on various hand movements related to specific signs. A detailed comparative review of all types of available techniques and sensors used for sign language recognition was presented in this article. The focus of this paper was to explore emerging trends and strategies for sign language recognition and to point out deficiencies in existing systems. This paper will act as a guide for other researchers to understand all materials and techniques like flex resistive sensor-based, vision sensor-based, or hybrid system-based technologies used for sign language until now.

Keywords: K-Nearest Neighbor; accelerometer sensor; artificial intelligence; decision tree; discriminant analysis; flex sensors; gesture recognition; gyroscope; machine learning; man-machine interface; sensor; sign language; supervised and unsupervised learning; support vector machine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Processing steps for the vision sensor-based SLR system.
Figure 2
Figure 2
Processing steps for the sensor-based SLR system.
Figure 3
Figure 3
Processing steps for the hybrid SLR system.
Figure 4
Figure 4
Sign recognition components.
Figure 5
Figure 5
Flex sensor or bend sensor [12,14,26].
Figure 6
Figure 6
Tactile sensor or force resistive sensor [101].
Figure 7
Figure 7
Smart glove with Hall sensors connected to the fingertip [25].
Figure 8
Figure 8
The ADXL335 3-axis ACC with a three-output analog pin x, y, and z [19,20,21].
Figure 9
Figure 9
The six DoF IMU MPU6050 chip consists of a 3-axis ACC and 3-axis gyroscope [20,35,38,83].
Figure 10
Figure 10
The 9 DoF IMU, MPU-9250 breakouts [30].
Figure 11
Figure 11
(a) ATmega microcontroller, (b) MSP430G2553 microcontroller, (c) Arduino Uno board, and (d) Android XU4 minicomputer [17].
Figure 12
Figure 12
Number of articles on each variety of gestures.
Figure 13
Figure 13
Article filtration procedure for literature review.
Figure 14
Figure 14
Categorical division of filtered articles used in this paper.
Figure 15
Figure 15
Databases used for Sign language recognition-based articles.
Figure 16
Figure 16
Article distribution based on region, gesture type, and number of hands used.
Figure 17
Figure 17
Motivational domains of sign Language.
Figure 18
Figure 18
Overview of key points regarding motivational domains of sign language.
Figure 19
Figure 19
Challenging domains of sign language.
Figure 20
Figure 20
Key points of challenging domains of sign language models.
Figure 21
Figure 21
Recommendation perspective of sign language.
Figure 22
Figure 22
Overview of recommendation domains of the sign language model.
See this image and copyright information in PMC

References

    1. Bhatnagar V., Magon R., Srivastava R., Thakur M. A Cost Effective Sign Language to voice emulation system; Proceedings of the 2015 Eighth International Conference on Contemporary Computing (IC3); Noida, India. 20–22 August 2015.
    1. Masieh M.A. Smart Communication System for Deaf-Dumb People; Proceedings of the International Conference on Embedded Systems, Cyber-physical Systems, and Applications (ESCS); Athens, Greece. 2017; [(accessed on 21 December 2021)]. Available online: https://www.proquest.com/openview/2747505eab9eb43cb1717f9654ca7d16/1?pq-....
    1. Kashyap A.S. Digital Text and Speech Synthesizer Using Smart Glove for Deaf and Dumb. Int. J. Adv. Res. Electron. Commun. Eng. (IJARECE) 2017;6:4.
    1. Amin M.S., Amin M.T., Latif M.Y., Jathol A.A., Ahmed N., Tarar M.I.N. Alphabetical Gesture Recognition of American Sign Language using E-Voice Smart Glove; Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC); Bahawalpur, Pakistan. 5–7 November 2020; pp. 1–6. - DOI
    1. Lokhande P., Prajapati R., Pansare S. Data Gloves for Sign Language Recognition System. Int. J. Comput. Appl. 2015;975:8887.

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