skip to main content
research-article

A Systematic Review of User Studies as a Basis for the Design of Systems for Automatic Sign Language Processing

Published: 11 November 2022 Publication History

Abstract

Deaf persons, whether or not they are sign language users, make up one of various existing marginalized populations that historically have been socially and politically underrepresented. Unfortunately, this also happens in technology design. Conducting user studies in which marginalized populations are represented is a step towards guaranteeing their right to participate in choices and decisions that are made for, with, and by them. This article presents and discusses results from a Systematic Literature Review (SLR) of user studies in the design of systems for Automatic Sign Language Processing (ASLP). Following our SLR protocol, from 2,486 papers initially found, we applied inclusion and exclusion criteria to finally select 37 papers in our review. We excluded publications that were not full papers, were not related to our main topic of interest, or that reported results that had been updated by more recent papers. All the selected papers focus on user studies as a basis for the design of three major aspects of ASLP: generation (ASLG), recognition (ASLR), and translation (ASLT). With regard to our specific area of interest, we analyzed four areas related to our research questions: goals and research methods, types of user involvement in the interaction design life cycle, cultural and collaborative aspects, and other lessons learned from the primary studies under review. Salient findings from our analysis show that numerical scale questionnaires are the most frequently used research instruments, co-designing ASLP systems with sign language users is not a common practice (as potential users are included mostly in the evaluation phase), and only seldom are Deaf persons who are sign language users included as members of research teams. These findings point to the need of conducting more inclusive and qualitative research for, with and by Deaf persons who are sign language users.

References

[1]
Matt Huenerfauth and Vicki Hanson. 2019. Sign language in the interface: Access for deaf signers. In The Universal Access Handbook. CRC Press. DOI:
[2]
Christian Vogler and Siome Goldenstein. 2008. Toward computational understanding of sign language. Technol. Disab. 20, 2 (2008), 109–119. DOI:
[3]
Raychelle Harris, Heidi M. Holmes, and Donna M. Mertens. 2009. Research ethics in sign language communities. Sign Lang. Stud. 9, 2 (Winter 2009), 104-131. DOI:
[4]
Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudreault, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, and Meredith Ringel Morris. 2019. Sign language recognition, generation, and translation: An interdisciplinary perspective. In ASSETS’19. Retrieved from.
[5]
CRPD. 2006. Convention on the Rights of Persons with Disabilities. Retrieved from t.ly/w5ok.
[6]
Ashley Walker, Yaxing Yao, Christine Geeng, Roberto Hoyle, and Pamela Wisniewski. 2019. Moving beyond “one size fits all”: Research considerations for working with vulnerable populations. Interactions (IX), XXVI. (Nov-Dec. Nov-Dec. 2019).
[7]
Sandra Sepúlveda Muñoz. 2018. La Torre de Babel 3.0: Los Derechos de las minorías lingüísticas desde las RRII. In Interculturalizaciones: Transiciones, Mediaciones y Conflictos en Lenguas, Comunidades y Educación Escolar. Universidad Autónoma Metropolitana, Unidad Iztapalapa, Consejo Editorial de Ciencias Sociales y Humanidades.
[8]
Daniela S. Cruzes and Tore Dybå. 2011. Research synthesis in software engineering: A tertiary study. Inf. Softw. Technol. 53, 5 (2011), 440–455. DOI:
[9]
Luis Naranjo-Zeledón, Jesús Peral, Antonio Ferrández, and Mario Chacón-Rivas. 2019. A systematic mapping of translation-enabling technologies for sign languages. Electronics 8, 9 (2019), 1047. DOI:
[10]
Soraia Silva Prietch, Polianna dos Santos Paim, Ivan Pineda Olmos, Josefina García Guerrero, and Juan Manuel Calleros Gonzalez. 2019b. The human and the context components in the design of automatic sign language recognition systems. In Human-Computer Interaction (Communications in Computer and Information Science). Springer, 1114.
[11]
Albert M. Cook and Jan M. Polgar. 2008. Cook & Hussey's Assistive Technologies: Principles and Practice. Mosby Elsevier, St. Louis, Mo.
[12]
Hernisa Kacorri. 2015. TR-2015001: A survey and critique of facial expression synthesis in sign language animation. Computer Science Technical Reports. City University of New York (CUNY) Academic Works. Retrieved from https://academicworks.cuny.edu/gc_cs_tr/403.
[13]
Oscar Koller. 2020. Quantitative Survey of the State of the Art in Sign Language Recognition. Retrieved from https://www.microsoft.com/en-us/research/people/oskoller/.
[14]
Mohamed Aktham Ahmed, Bilal Bahaa Zaidan, Aws Alaa Zaidan, Mahmood Maher Salih, and Muhammad Modi bin Lakulu. 2018. A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017. Sensors 18, 7 (2018), 2208. DOI:
[15]
Ruth Wario and Casam Nyaga. 2019. A survey of the constraints encountered in dynamic vision-based sign language hand gesture recognition. In Universal Access in Human-computer Interaction. Multimodality and Assistive Environments (Lecture Notes in Computer Science. Springer, Cham, 11573. DOI:
[16]
Mehrdad Ghaziasgar, Antoine Bagula, Christopher Thron, and Olasupo Ajayi. 2020. Automatic sign language manual parameter recognition (I): survey. In Implementations and Applications of Machine Learning. (Studies in Computational Intelligence. Springer, Cham, 782. DOI:
[17]
Razieh Rastgoo, Kourosh Kiani, and Sergio Escalera. 2021. Sign language recognition: A deep survey. Expert Syst. Applic. 164 (2021), 113794. DOI:
[18]
David Moher, Alessandro Liberati, Jennifer Tetzlaff, and Douglas G. Altman. 2009. The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. PLoS Med 6, 7 (2009), e1000097. DOI:
[19]
B. J. Shea, J. M. Grimshaw, G. A. Wells, M. Boers, N. Andersson, C. Hamel, et al. 2007. Development of AMSTAR: A measurement tool to assess the methodological quality of systematic reviews. BMC Med. Res. Methodol. 7, 10 (2007).
[20]
Chitu Okoli. 2015. A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37 (2015). Retrieved from http://aisel.aisnet.org/cais/vol37/iss1/43.
[21]
Apostolos Ampatzoglou, Stamatia Bibi, Paris Avgeriou, Marijn Verbeek, and Alexander Chatzigeorgiou. 2019. Identifying, categorizing and mitigating threats to validity in software engineering secondary studies. Inf. Softw. Technol. 106 (2019), 201–230. DOI:
[22]
Jacob O. Wobbrock and Julie A. Kientz. 2016. Research contributions in human-computer interaction. Interactions 23, 3 (Apr. 2016), 38–44. DOI:
[23]
Margarete Sandelowski and Julie Barroso. 2007. Handbook for Synthesizing Qualitative Research Springer. Retrieved from https://bit.ly/2VcCq5C.
[24]
Jonathan Lazar, Jinjuan Feng, and Harry Hochheiser. 2017. Research Methods in Human-computer Interaction. 2nd ed. Elsevier.
[25]
Sari Kujala, Virpi Roto, Kaisa Väänänen-Vainio-Mattila, and Arto Sinnelä. 2011. Identifying hedonic factors in long-term user experience. In Conference on Designing Pleasurable Products and Interfaces. Association for Computing Machinery, New York, NY. DOI:
[26]
Jennifer Preece, Yvonne Rogers, and Helen Sharp. 2002. Interaction Design: Beyond Human-computer Interaction. Wiley.
[27]
Maria Cecilia Calini Baranauskas, Maria Cecilia Martins, and Jose Armando Valente. 2013. Codesign de Redes Digitais: Tecnologia e Educação a Serviço da Inclusão Social. Penso Editora, Porto Alegre, RS.
[28]
Simone D. J. Barbosa and Bruno S. da Silva. 2017. Interação Humano-computador. Elsevier, Rio de Janeiro.
[29]
Sari Kujala. 2003. User involvement: A review of the benefits and challenges. J. Behav. Inf. Technol. 22 (2003). DOI:
[30]
Leela Damodaran. 1996. User involvement in the systems design process: A practical guide for users. Behav. Inf. Technol. 15, 6 (1996), 363–377.
[31]
Matti A. Kaulio. 1998. Customer, consumer and user involvement in product development: A framework and a review of selected methods. Total Qual. Manag. 9, 1 (1998), 141–149.
[32]
John D. Gould and Clayton Lewis. 1985. Designing for usability: Key principles and what designers think. Commun. ACM 28, 3 (Mar. 1985), 300–311. DOI:
[33]
Michael J. Muller and Allison Druin. 2002. Participatory design: The third space in HCI. The Human-computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications. L. Erlbaum Associates Inc., 1051–1068.
[34]
Richard E. Ladner. 2015. Design for user empowerment. Interactions 22, 2 (Mar.–Apr. 2015), 24–29, DOI:
[35]
Mark Orbe. 1998. Constructing Co-cultural Theory: An Explication of Culture, Power, and Communication. Sage.
[36]
Nicoletta Adamo-Villani, Jason Lestina, and Saikiran Anasingaraju. 2015. Does character's visual style affect viewer's perception of signing avatars? In eLEOT. Retrieved from https://www.springer.com/gp/book/9783319288826.
[37]
Diego Roberto Antunes, André L. P. Guedes, and Laura Sánchez García. 2015. A context-based collaborative framework to build sign language databases by real users. In UAHCI (LNCS, Vol. 9176). 327–338. DOI:
[38]
Soraia Silva Prietch, Juan Manuel Calleros Gonzalez, J. Alfredo Sánchez, Ivan Pineda Olmos, and Josefina García Guerrero. 2019c. Cultural aspects in the user experience design of an ASLR system. In CLIHC. Association for Computing Machinery, New York, NY. DOI:
[39]
Carla da Silva Flor, Tarcisio Vanzin, and Vânia Ulbricht. 2013. Recomendações da WCAG 2.0 (2008) e a acessibilidade de surdos em conteúdos da Web. Rev. bras. educ. espec., Marília 19, 2 (June 2013), 161–168. Retrieved from.
[40]
Gênesis Medeiros do Carmo, Débora Maria Barroso Paiva, and Maria Istela Cagnin. 2019. How to develop accessible web interfaces for deaf people? In IHC. Association for Computing Machinery, New York, NY. 1–10. DOI:
[41]
Sushant Kafle, Abraham Glasser, Sedeeq Al-khazraji, Larwan Berke, Matthew Seita, and Matt Huenerfauth. 2020. Artificial intelligence fairness in the context of accessibility research on intelligent systems for people who are deaf or hard of hearing. SIGACCESS Access. Comput. 125 (Oct. 2019). DOI:
[42]
Danielle Bragg, Naomi Caselli, Julie A. Hochgesang, Matt Huenerfauth, Leah Katz-Hernandez, Oscar Koller, Raja Kushalnagar, Christian Vogler, and Richard E. Ladner. 2021. The FATE landscape of sign language AI datasets: An Interdisciplinary perspective. ACM Trans. Access. Comput. 14, 2 (July 2021). DOI:
[43]
Ghazanfar Latif, Nazeeruddin Mohammad, Jaafar Alghazo, Roaa AlKhalaf, and Rawan AlKhalaf. 2019. ArASL: Arabic alphabets sign language dataset. Data Brief 23 (2019), 103777. DOI:

Cited By

View all
  • (2024)Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-HearingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642287(1-18)Online publication date: 11-May-2024
  • (2023)Metaverse-Based Softbot Tutors for Inclusive Industrial Workplaces: Supporting Impaired Operators 5.0Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures10.1007/978-3-031-43662-8_47(662-677)Online publication date: 14-Sep-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Accessible Computing
ACM Transactions on Accessible Computing  Volume 15, Issue 4
December 2022
302 pages
ISSN:1936-7228
EISSN:1936-7236
DOI:10.1145/3561961
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 November 2022
Online AM: 07 October 2022
Accepted: 29 August 2022
Revised: 21 July 2022
Received: 05 August 2020
Published in TACCESS Volume 15, Issue 4

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. User studies
  2. universal design
  3. assistive technology
  4. systematic review
  5. sign language
  6. automatic translation
  7. automatic recognition
  8. automatic generation

Qualifiers

  • Research-article
  • Refereed

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)182
  • Downloads (Last 6 weeks)39
Reflects downloads up to 14 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-HearingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642287(1-18)Online publication date: 11-May-2024
  • (2023)Metaverse-Based Softbot Tutors for Inclusive Industrial Workplaces: Supporting Impaired Operators 5.0Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures10.1007/978-3-031-43662-8_47(662-677)Online publication date: 14-Sep-2023

View Options

Get Access

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media