Abstract
This study aims to provide an in-depth understanding of assistive technology (AT) research development by investigating relationships between related keywords and concepts and uncovering how trends change over time. This study employed network analysis to identify the relationships between frequently cited papers and keywords in the AT literature and concept-linking analysis to uncover the key concepts and classifies them into clusters determining the changing trends in AT research. The network analysis results on author keywords co-occurrence and citation indicate that the development trend of AT is primarily observed in medicine and is related to medical devices used in rehabilitation or available for disabled and people for well-being improvement. Meanwhile, the concept linking analysis identifies seven groups of key AT concepts, including technical issues, education, health and disability, policy, user/people, and ways/medium. We also depict the yearly changes of the key concepts in AT research development. This research extracts the strongest key concepts, the most productive authors and countries, and the connecting relationship between authors and literature within the big data. It provides a comprehensive view of temporal patterns of AT developments and their development in terms of human–computer interactions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tao, G., et al.: Evaluation tools for assistive technologies: a scoping review. Arch. Phys. Med. Rehab. 101(6), 1025–1040 (2020)
O'Neill, S.J., et al.: Assistive technology: understanding the needs and experiences of individuals with autism spectrum disorder and/or intellectual disability in Ireland and the UK. Assist. Technol. 32, 1–9 (2019)
Perfect, E., Jaiswal, A., Davies, T.C.: Systematic review: Investigating the effectiveness of assistive technology to enable internet access for individuals with deafblindness. Assist. Technol. 31(5), 276–285 (2019)
Tofani, M., et al.: The psychosocial impact of assistive device scale: Italian validation in a cohort of nonambulant people with neuromotor disorders. Assist. Technol. 32(1), 54–59 (2020)
ATIA.: What is assistive technology. Available from: https://www.atia.org/at-resources/what-is-at/
Dudgeon, B.J., et al.: Managing activity difficulties at home: a survey of medicare beneficiaries. Arch. Phys. Med. Rehabil. 89(7), 1256–1261 (2008)
Iancu, I., Iancu, B.: Designing mobile technology for elderly. A theoretical overview. Technol. Forecast. Social Change 155, 119977 (2020)
Hermawati, S., Pieri, K.: Assistive technologies for severe and profound hearing loss: beyond hearing aids and implants. Assist. Technol. 32(4), 182–193 (2020)
Özsungur, F.: A research on the effects of successful aging on the acceptance and use of technology of the elderly. Assist. Technol. 34, 1–14 (2019)
Oomens, I.M.F., Scholten, C.: Inclusion in social innovation through the primary and secondary use of technology: a conceptual framework. Int. Rev. Appl. Econ. 34(5), 672–686 (2020)
Bhowmick, A., Hazarika, S.M.: An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends. J. Mult. User Inter. 11(2), 149–172 (2017)
Elmannai, W., Elleithy, K.: Sensor-based assistive devices for visually-impaired people: current status, challenges, and future directions. Sensors (Basel) 17(3), 565 (2017)
Asghar, I., Cang, S., Yu, H.: Assistive technology for people with dementia: an overview and bibliometric study. Health Info. Libr. J. 34(1), 5–19 (2017)
Brims, L., Oliver, K.: Effectiveness of assistive technology in improving the safety of people with dementia: a systematic review and meta-analysis. Aging Ment. Health 23(8), 942–951 (2019)
Gibson, G., et al.: Personalisation, customisation and bricolage: how people with dementia and their families make assistive technology work for them. Ageing Soc. 39(11), 2502–2519 (2019)
de Oliveira, J.M., et al.: REHAB FUN: an assistive technology in neurological motor disorders rehabilitation of children with cerebral palsy. Neural Comput. Appl. 32(15), 10957–10970 (2020)
Geman, O., et al.: An intelligent assistive tool using exergaming and response surface methodology for patients with brain disorders. IEEE Access 7, 21502–21513 (2019)
Abbott, C., et al.: Emerging issues and current trends in assistive technology use 2007–2010: practising, assisting and enabling learning for all. Disabil. Rehabil. Assist. Technol. 9(6), 453–462 (2014)
Alnahdi, G.: Assistive technology in special education and the universal design for learning. Turkish Online J. Educ. Technol. 13 (2014)
Atanga, C., et al.: Teachers of students with learning disabilities: assistive technology knowledge, perceptions, interests, and barriers. J. Special Educ. Technol. 35(4), 0162643419864858 (2019).
Cowan, R., et al.: Recent trends in assistive technology for mobility. J. Neuroeng. Rehabil. 9, 20 (2012)
Ehlers, S.G., Field, W.E.: Accessing and operating agricultural machinery: advancements in assistive technology for users with impaired mobility. Assist. Technol. 31(5), 251–258 (2019)
Remillard, E.T., Fausset, C.B., Fain, W.B.: aging with long-term mobility impairment: maintaining activities of daily living via selection, optimization, and compensation. Gerontologist 59(3), 559–569 (2017)
Gavel, Y., Iselid, L.: Web of Science and Scopus: a journal title overlap study. Online Inf. Rev. 32(1), 8–21 (2008)
Agarwal, A., et al.: Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian J. Androl. 18(2), 296–309 (2016)
van Eck, N.J., Waltman, L.: Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2), 523–538 (2010)
Persson, O.: Identifying research themes with weighted direct citation links. J. Informet. 4(3), 415–422 (2010)
Kong, X., et al.: Academic social networks: Modeling, analysis, mining and applications. J. Netw. Comput. Appl. 132, 86–103 (2019)
Leung, X.Y., Sun, J., Bai, B.: Bibliometrics of social media research: a co-citation and co-word analysis. Int. J. Hosp. Manag. 66, 35–45 (2017)
Nishanth, K.J., et al.: Soft computing based imputation and hybrid data and text mining: the case of predicting the severity of phishing alerts. Expert Syst. Appl. 39(12), 10583–10589 (2012)
SAS, I.I.: Getting Started with SAS® Text Miner 12.1. 2012: Cary, NC: SAS Institute Inc.
SAS, SAS® Text Miner 15.1: Strength of Association for Concept Linking (2020)
Schalk, G., et al.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Hochberg, L.R., et al.: Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485(7398), 372 (2012)
Betke, M., Gips, J., Fleming, P.: The camera mouse: visual tracking of body features to provide computer access for people with severe disabilities. IEEE Trans. Neural Syst. Rehabil. Eng. 10(1), 1–10 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, Cw., Koziel, A.M., Yeh, Th. (2023). Research Development on Assistive Technology: A Network and Concept-Linking Analysis. In: Visvizi, A., Troisi, O., Grimaldi, M. (eds) Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-19560-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-19560-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19559-4
Online ISBN: 978-3-031-19560-0
eBook Packages: Computer ScienceComputer Science (R0)