Correction: Sign language mobile apps: A systematic review of current app evaluation progress and solution framework

Dianes, David Emmanuel

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Type :	Article
Subject :	T Technology (General)
ISBN :	1868-6478
Main Author :	Dianes, David Emmanuel
Additional Authors :	Salem, Garfan Abdullah
Title :	Correction: Sign language mobile apps: A systematic review of current app evaluation progress and solution framework
Hits :	121

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran & Meta-Teknologi
Year of Publication :	2024
Notes :	Evolving Systems
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
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Abstract : Universiti Pendidikan Sultan Idris

Mobile apps for sign language are a fascinating area of research that merits a lot of attention. These apps are widely used due to their affordability and usability. Nevertheless, the quality of these apps needs to be evaluated if they are to have a significant influence. The outcomes of app quality assessment can also stimulate app development efforts and improvements to their functionality and accuracy. Therefore, to gain more in depth understanding, four academic databases (Science Direct, IEEE, Association for Computing Machinery, and Web of Science) were searched as part of a systematic literature review. The review highlighted that sign language apps require further development to support users in achieving the greatest possible usage and learning experience. We present a solution framework based on a quality assessment criterion for sign language mobile apps that could be adopted by academics and developers. Alternative identification, criteria, and development are the three primary aspects of the framework. The results demonstrate that the new assessment approach can establish a detailed set of criteria for sign language mobile app development. Future academics could gain a thorough understanding of mobile app development criteria from this study. The results are advantageous and open up a new field where researchers and developers could collaborate on sign language mobile app development.

References

Ahmed MA, Zaidan BB, Zaidan AA, Salih MM, Lakulu MM (2018) A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017. Sensors 18(7):2208.

Aly S, Aly W (2020) DeepArSLR: A novel signer-independent deep learning framework for isolated arabic sign language gestures recognition. IEEE Access 8:83199–83212.

Aly W, Aly S, Almotairi S (2019) User-independent American sign language alphabet recognition based on depth image and PCANet features. IEEE Access 7:123138–123150.

Amrutha C, Davis N, Samrutha K, Shilpa N, Chunkath JJPT (2016) Improving language acquisition in sensory deficit individuals with mobile application. Proced Technol 24:1068–1073.

Arman H, Hadi-Vencheh A, Arman A, Moslehi A (2021) Revisiting the approximated weight extraction methods in fuzzy analytic hierarchy process. Int J Intell Syst 36(4):1644–1667.

Baehaqi, M. N., Irzal, M., & Indiyah, F. H. (2019). Morphological Analysis of Speech Translation into Indonesian Sign Language System (SIBI) on Android Platform. Paper presented at the 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS).

Berke, J. (2020). Challenges of Learning Sign Language The Difficulty Depends on the Type. verywell health. Retrieved from https://www. veryw ellhe alth. com/ chall enges- of- learn ing- sign- language- 10492 96#: ~: text= One% 20of% 20the% 20cha lleng es% 20people,commu nicate% 20both% 20dyn amica lly% 20and% 20acc urate ly.

Bodjanova S (2006) Median alpha-levels of a fuzzy number. Fuzzy Sets Syst 157(7):879–891.

Chang PL, Hsu CW, Chang PC (2011) Fuzzy Delphi method for evaluating hydrogen production technologies. Int J Hydrogen Energy 36(21):14172–14179.

Cheng CH, Lin Y (2002) Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. Eur J Oper Res 142(1):174–186.

Chu H-C, Hwang G-J (2008) A Delphi-based approach to developing expert systems with the cooperation of multiple experts. Expert Syst Appl 34(4):2826–2840.

Chuan, N. K., Sivaji, A., Loo, F. A., Ahmad, W. F. W., & Nathan, S. S. (2017). Evaluating ‘Gesture Interaction’requirements of mobile applications for deaf users: discovering the needs of the hearing-impiared in using touchscreen gestures. Paper presented at the 2017 IEEE Conference on Open Systems (ICOS).

Chuckun, V., Coonjan, G., & Nagowah, L. (2019, 19–21 Sept. 2019). Enabling the Disabled using mHealth. Paper presented at the 2019 Conference on Next Generation Computing Applications (NextComp).

da Rosa Tavares JE, Victória Barbosa JL (2021) Apollo SignSound: An intelligent system applied to ubiquitous healthcare of deaf people. J Reliable Intell Environ 7(2):157–170.

Dahanayaka T, Madhusanka A, Atthanayake I (2021) A multi-modular approach for sign language and speech recognition for deafmute people. Eng J Instit Eng Sri Lanka 54:97. https:// doi. org/10. 4038/ engin eer. v54i4. 7474.

Deb S, Bhattacharya PJPCS (2018) Augmented Sign Language Modeling (ASLM) with interaction design on smartphone-an assistive learning and communication tool for inclusive classroom. Proced Comput Sci 125:492–500.

Disorder, N. I. o. D. a. O. C. (2019). Hearing and Balance. Retrieved from https:// www. nidcd. nih. gov/ health/ ameri can- sign- langu age.

Fang, B., Co, J., & Zhang, M. (2017). Deepasl: Enabling ubiquitous and non-intrusive word and sentence-level sign language translation. Paper presented at the Proceedings of the 15th ACM conference on embedded network sensor systems.

Fischer, S. (2015). Sign languages in their Historical Context. In (pp. 442–465).

Hartanto, R., & Kartikasari, A. (2016). Android based real-time static Indonesian sign language recognition system prototype. Paper presented at the 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE).

Hasanah A, Kusumah Y, Rahmi K (2020) Rounding-augmented reality book and smartphone for deaf students in achieving basic competence. J Phys Conf Ser 1521:032064. https:// doi. org/ 10.1088/ 1742- 6596/ 1521/3/ 032064.

HeeKo KK, Kim SK, Lee Y, Lee JY, Stoyanov SR (2022) Validation of a Korean version of mobile app rating scale (MARS) for apps targeting disease management. Health Inform J 28(2):14604582221091976. https:// doi. org/ 10. 1177/ 1460458222 10919 75.

Hou, J., Li, X.-Y., Zhu, P., Wang, Z., Wang, Y., Qian, J., & Yang, P. (2019). Signspeaker: A real-time, high-precision smartwatchbased sign language translator. Paper presented at the The 25th Annual International Conference on Mobile Computing and Networking.

Hussain MA, Ahsan K, Iqbal S, Nadeem AJIJ (2019) Supporting deafblind in congregational prayer using speech recognition and vibro-tactile stimuli. Int J Human-Comput Stud 123:70–96.

Joy J, Balakrishnan K, M.s, S. (2019) SiLearn: an intelligent sign vocabulary learning tool. J Enabling Technol Ahead Print. https:// doi. org/ 10. 1108/ JET- 03- 2019- 0014.

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