Skip to main content
SpringerLink
Log in

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

  • Review
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.verywellhealth.com/challenges-of-learning-sign-language-1049296#:~:text=One%20of%20the%20challenges%20people,communicate%20both%20dynamically%20and%20accurately.

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dahanayaka T, Madhusanka A, Atthanayake I (2021) A multi-modular approach for sign language and speech recognition for deaf-mute people. Eng J Instit Eng Sri Lanka 54:97. https://doi.org/10.4038/engineer.v54i4.7474

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

  • 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

    Article  Google Scholar 

  • 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/10.1177/14604582221091975

    Article  Google Scholar 

  • Hou, J., Li, X.-Y., Zhu, P., Wang, Z., Wang, Y., Qian, J., & Yang, P. (2019). Signspeaker: A real-time, high-precision smartwatch-based 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Katariya A, Rumale V, Gholap A, Dhamale A, Gupta A (2020) Voice to Indian Sign Language Conversion for Hearing Impaired People. SAMRIDDHI J Phys Sci Eng Technol 12(2):31–35

    Google Scholar 

  • Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. 2.

  • Ku, Y.-J., Chen, M.-J., & King, C.-T. (2019). A Virtual Sign Language Translator on Smartphones. Paper presented at the 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW).

  • Kumar P, Kaur S (2020) Sign language generation system based indian sign language grammar. Trans Asian Low-Resour Lang Inf Process. https://doi.org/10.1145/3384202

    Article  Google Scholar 

  • Liu Y, Jiang F, Gowda M (2020) Finger Gesture Tracking for Interactive Applications: A Pilot Study with Sign Languages. J Proc ACM Interact Mob Wearable Ubiquitous Technol 4(3):1–21. https://doi.org/10.1145/3414117

    Article  Google Scholar 

  • LlorensVernet P, Miró J (2020) The mobile app development and assessment guide: a delphi-based validity study (Preprint). JMIR mHealth uHealth. https://doi.org/10.2196/17760

    Article  Google Scholar 

  • Llorens-Vernet P, Miró JJJ (2020) The Mobile App Development and Assessment Guide (MAG): Delphi-Based Validity Study. JMIR Mhealth uhealth 8(7):e17760

    Article  Google Scholar 

  • Luangrungruang, T., & Kokaew, U. (2018, 14–17 Aug. 2018). Applying Universal Design for Learning in Augmented Reality Education Guidance for Hearing Impaired Student. Paper presented at the 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA).

  • Mahamud, M. S., & Zishan, M. S. R. (2017). Watch IT: An assistive device for deaf and hearing impaired. Paper presented at the 2017 4th International Conference on Advances in Electrical Engineering (ICAEE).

  • Mehta, A., Rathod, T., & Solanki, M. K. (2020). Evolution in Automated Translator for Real Time Voice to Sign Language Transformation for the Deaf and Dumb People. Paper presented at the National Conference on Recent Trends in Electronics and Communication Engineering-2020.

  • Mi X, Tang M, Liao H, Shen W, Lev B (2019) The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega 87:205–225

    Article  Google Scholar 

  • Assessing the quality of mobile health-related apps: interrater reliability study of two guides. JMIR mHealth uHealth, 9(4), e26471-e26471. Retrieved from https://news.unboundmedicine.com/medline/citation/33871376/Assessing_the_Quality_of_Mobile_Health_Related_Apps:_Interrater_Reliability_Study_of_Two_Guides_https://mhealth.jmir.org/2021/4/e26471/

  • Mohammed RT, Zaidan AA, Yaakob R, Sharef NM, Abdullah RH, Zaidan BB, Abdulkareem KH (2021) Determining importance of many-objective optimisation competitive algorithms evaluation criteria based on a novel fuzzy-weighted zero-inconsistency method. Int J Inform Technol Decis Mak. https://doi.org/10.1142/s0219622021500140

    Article  Google Scholar 

  • Muzahidin, S., & Rakun, E. (2020). Text-Driven Talking Head Using Dynamic Viseme and DFFD for SIBI. Paper presented at the 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE).

  • Nanaware, T., Sahasrabudhe, S., Ayer, N., & Christo, R. (2018). Fingerspelling-Indian Sign Language Training Tool. Paper presented at the 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT).

  • O’Rourke T, Pryss R, Schlee W, Probst T (2020) Development of a Multidimensional App Quality Assessment Tool for Health-Related Apps (AQUA). Dig Psychol. https://doi.org/10.24989/dp.v1i2.1816

    Article  Google Scholar 

  • Park H, Lee Y, Ko J (2021) Enabling real-time sign language translation on mobile platforms with on-board depth cameras. Proceed ACM Interact Mobile Wearable Ubiquitous Technol 5(2):1–30

    Article  Google Scholar 

  • Parvez K, Khan M, Iqbal J, Tahir M, Alghamdi A, Alqarni M, Javaid N (2019) Measuring effectiveness of mobile application in learning basic mathematical concepts using pakistan sign language. Sustainability 11:3064. https://doi.org/10.3390/su11113064

    Article  Google Scholar 

  • Paudyal P, Lee J, Banerjee A, Gupta SKS et al (2019a) A comparison of techniques for sign language alphabet recognition using armband wearables. ACM Trans Interact Intell Syst 9(2–3):1–26

    Article  Google Scholar 

  • Paudyal P, Lee J, Kamzin A, Soudki M, Banerjee A, Gupta SK (2019b) Learn2Sign: Explainable AI for Sign Language Learning. Paper presented at the IUI Workshops.

  • Rao GA, Kishore PJASEJ (2018) Selfie video based continuous indian sign language recognition system. Ain Shams Eng J 9(4):1929–1939

    Article  Google Scholar 

  • Ridzky D, Effendy V, Junaedi D (2019) User interface modelling for SIBI (sistem isyarat bahasa indonesia/indonesian sign language system) learning applications using the User-Centered Design Method. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1192/1/012033

    Article  Google Scholar 

  • Roberts AE, Davenport TA, Wong T, Moon H-W, Hickie IB, LaMonica HM (2021) Evaluating the quality and safety of health-related apps and e-tools: Adapting the Mobile App Rating Scale and developing a quality assurance protocol. Internet Interv 24:100379. https://doi.org/10.1016/j.invent.2021.100379

    Article  Google Scholar 

  • Romero RL et al (2019) Modifying the Mobile App Rating Scale With a Content Expert: Evaluation Study of Deaf and Hard-of-Hearing Apps. JMIR Mhealth Uhealth. https://doi.org/10.2196/14198

    Article  Google Scholar 

  • Samonte, M. J. C., Gazmin, R. A., Soriano, J. D. S., & Valencia, M. N. O. (2019). BridgeApp: An Assistive Mobile Communication Application for the Deaf and Mute. Paper presented at the 2019 International Conference on Information and Communication Technology Convergence (ICTC).

  • Sengupta, A., Mallick, T., & Das, A. (2019). A Cost Effective Design and Implementation of Arduino Based Sign Language Interpreter. Paper presented at the 2019 Devices for Integrated Circuit (DevIC).

  • Sobhan, M., Chowdhury, M. Z., Ahsan, I., Mahmud, H., & Hasan, M. K. (2019). A Communication Aid System for Deaf and Mute using Vibrotactile and Visual Feedback. Paper presented at the 2019 International Seminar on Application for Technology of Information and Communication (iSemantic).

  • Srithonratkul, B., Sintupetch, P., Saysaman, P., Pluempitiwiriyawej, C., & Chauksuvanit, T. (2016). New2Thai. Paper presented at the 2016 Fifth ICT International Student Project Conference (ICT-ISPC).

  • Swarnkar, S. K., Ambhaikar, A., Swarnkar, V. K., & Sinha, U. (2022). Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization and Regularization. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) (pp. 633–639): Springer.

  • Terhorst Y, Philippi P, Sander LB, Schultchen D, Paganini S, Bardus M, Schoeppe S (2020) Validation of the mobile application rating scale (mars). PLOS ONE 15(11):e0241480

    Article  Google Scholar 

  • van Dinter R, Tekinerdogan B, Catal C (2021) Automation of systematic literature reviews: A systematic literature review. Inform Softw Technol 136:106589. https://doi.org/10.1016/j.infsof.2021.106589

    Article  Google Scholar 

  • Xiao Y, Watson M (2017) Guidance on conducting a systematic literature review. J Plan Educ Res 39(1):93–112. https://doi.org/10.1177/0739456X17723971

    Article  Google Scholar 

  • Yousaf K, Mehmood Z, Saba T, Rehman A, Rashid M, Altaf M, Shuguang Z (2018) A novel technique for speech recognition and visualization based mobile application to support two-way communication between deaf-mute and normal peoples. Wirel Commun Mob Comput 2018:1013234. https://doi.org/10.1155/2018/1013234

    Article  Google Scholar 

  • Zhang Y, Min Y, Chen X (2021) Teaching chinese sign language with a smartphone. Virtual Real Intell Hardw 3(3):248–260

    Article  Google Scholar 

  • Zhang, Q., Wang, D., Zhao, R., & Yu, Y. (2019). MyoSign: enabling end-to-end sign language recognition with wearables. Paper presented at the Proceedings of the 24th international conference on intelligent user interfaces.

Download references

Author information

Authors and Affiliations

  1. Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia

    Dianes David, A. H. Alamoodi & Salem Garfan

  2. Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq

    A. S. Albahri

  3. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunrlin, Taiwan ROC

    B. B. Zaidan

  4. Discipline of Nutrition and Dietetics, Susan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2006, Australia

    Juliana Chen

  5. Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia

    O. S. Albahri

  6. Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq

    O. S. Albahri

  7. Charles Perkins Centre, The University of Sydney, Camperdown, NSW, 2006, Australia

    Juliana Chen

Authors
  1. Dianes David
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. H. Alamoodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. O. S. Albahri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Salem Garfan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. S. Albahri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. B. B. Zaidan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Juliana Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. H. Alamoodi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

David, D., Alamoodi, A.H., Albahri, O.S. et al. Sign language mobile apps: a systematic review of current app evaluation progress and solution framework. Evolving Systems 15, 669–686 (2024). https://doi.org/10.1007/s12530-023-09494-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-023-09494-0

Keywords

Search

Navigation