Fourth Fifth International Conference on Science and Technology Metrics (STMet 2024)
 

 

Research Trends in Wearable Devices: A Bibliometric Assessment
Shri Ram, Sanjay Kataria, Shriram Pandey, Neeraj Singh
Thapar Institute of Engineering & Technology Patiala, India., Bennett University, Greater Noida, India., Central University of Haryana, Mahendragarh, India., Punjab University, Chandigarh, India
Abstract: Wearable devices or wearable technology are designed to be worn on the body, including smartwatches, fitness trackers, smart glasses, and headphones, primarily used for tracking activities and health monitoring. The paper aims to provide a comprehensive bibliometric analysis and mapping of the intellectual structure of wearable device research. The SCOPUS-based study retrieved 7172 records spanning 26 years (1999-2023) using relevant keywords associated with wearable devices. The findings of the study unearth that there has been a significant increase in publications on wearable devices since 2012. The United States and China emerged as leading contributors. Sensors Journal was identified as the most productive journal. The thematic analysis shows a significant technological advancement in wearable devices, which are applied prominently in health monitoring. The cluster analysis reveals the research hotspots in health-related wearable devices, wearable sensors powered by machine learning and deep learning, wearable electronics, smart clothing and textiles, and human activity monitoring. This study offers a bibliometric qualitative overview of wearable devices and provides quantitative research by providing insights into the field’s intellectual structure and thematic evolution. The study contributes to the existing knowledge domain by identifying key trends, influential contributors, and key thematic areas for future research.
Keywords: Wearable Devices, Wearable Technology, Smart Textiles, Bibliometric Analysis Research Trends in Wearable Devices: A Bibliometric Assessment
DOI:https://doi.org/10.6025/stm/2024/5/147-160
Full_Text   PDF    Download:   316  times
References:


[1] Ahmi, A. (2023). OpenRefine: An approachable tool for cleaning and harmonizing bibliographical data. 030006. https://doi.org/10.1063/5.0164724

[2] Ahmi, A. (2024). BIbliomagika. https://bibliomagika.com

 

[3] Das, A. K., Wazid, M., Kumar, N., Khan, M. K., Choo, K.-K. R., & Park, Y. (2018). Design of Secure and Lightweight Authentication Protocol for Wearable Devices Environment. IEEE Journal of Biomedical and Health Informatics, 22(4), 1310–1322. https://doi.org/10.1109/JBHI.2017.2753464

[4] Erickson, R. (2013). Animal-Borne Imaging: Embodied Point-of-View and the Ethics of Identification. In Cine-Ethics (pp. 214–231). Routledge.

[5] Evers, C. (2015). Researching action sport with a GoProTM camera: An embodied and emotional mobile video tale of the sea, masculinity and men-who-surf. In Res. Embodied Sport: Explor. Mov. Cult. (pp. 145–162). Taylor and Francis Inc.; Scopus. https://doi.org/10.4324/9781315761121

[6] Garfield, E. (1955). Citation indexes for science; a new dimension in documentation through association of ideas. Science (New York, N.Y.), 122(3159), 108–111. https://doi.org/10.1126/science.122.3159.108

[7] Garfield, E. (1999). Journal impact factor: A brief review. CMAJ: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 161(8), 979–980.

[8] Heilig, M. L. (1994). United States Patent office: Stereoscopic-television apparatus for individual use. ACM SIGGRAPH Computer Graphics, 28(2), 131–134.

[9] Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102

[10] Hou, W., & Zuckermann, G. (2020). Australian Aboriginal Sports Health Monitoring System based on Wearable Device and Data Center Technology. 2020 International Conference on Smart Electronics and Communication (ICOSEC), 305–308. https://doi.org/10.1109/ICOSEC49089.2020.9215265

[11] Hyland, K., & Zou, H. (Joanna). (2022). Titles in research articles. Journal of English for Academic Purposes, 56, 101094. https://doi.org/10.1016/j.jeap.2022.101094

[12] Jin, D., Adams, H., Cocco, A. M., Martin, W. G., & Palmer, S. (2020). Smartphones and wearable technology: Benefits and concerns in cardiology. Medical Journal of Australia, 212(2), 54. https://doi.org/10.5694/mja2.50446

[13] Kageyama, I., Kurata, K., Miyashita, S., Lim, Y., Sengoku, S., & Kodama, K. (2022). A Bibliometric Analysis of Wearable Device Research Trends 2001–2022—A Study on the Reversal of Number of Publications and Research Trends in China and the USA. International Journal of Environmental Research and Public Health, 19(24), 16427. https://doi.org/10.3390/ijerph192416427

[14] Khan, S., Parkinson, S., Grant, L., Liu, N., & Mcguire, S. (2021). Biometric Systems Utilising Health Data from Wearable Devices: Applications and Future Challenges in Computer Security. ACM Computing Surveys, 53(4), 1–29. https://doi.org/10.1145/3400030

[15] Lu, W., Liu, Z., Huang, Y., Bu, Y., Li, X., & Cheng, Q. (2020). How do authors select keywords? A preliminary study of author keyword selection behavior. Journal of Informetrics, 14(4), 101066. https://doi.org/10.1016/j.joi.2020.101066

[16] Luczak, T., Burch, R., Lewis, E., Chander, H., & Ball, J. (2020). State-of-the-art review of athletic wearable technology: What 113 strength and conditioning coaches and athletic trainers from the USA said about technology in sports. International Journal of Sports Science & Coaching, 15(1), 26–40. https://doi.org/10.1177/1747954119885244

[17] Mannini, A., & Sabatini, A. M. (2010). Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers. Sensors, 10(2), 1154–1175. https://doi.org/10.3390/s100201154

[18] Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339(jul21 1), b2535–b2535. https://doi.org/10.1136/bmj.b2535

[19] Muro-de-la-Herran, A., Garcia-Zapirain, B., & Mendez-Zorrilla, A. (2014). Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications. Sensors, 14(2), 3362–3394. https://doi.org/10.3390/s140203362

[20] Nandi, S., Mishra, M., & Majumder, S. (2022). Usage of AI and Wearable IoT Devices for Healthcare Data: A Study. In D. Ghai, S. L. Tripathi, S. Saxena, M. Chanda, & M. Alazab (Eds.), Machine Learning Algorithms for Signal and Image Processing (1st ed., pp. 315–337). Wiley. https://doi.org/10.1002/9781119861850.ch18

[21] Ometov, A., Shubina, V., Klus, L., Skibiñska, J., Saafi, S., Pascacio, P., Flueratoru, L., Gaibor, D. Q., Chukhno, N., Chukhno, O., Ali, A., Channa, A., Svertoka, E., Qaim, W. B., Casanova-Marqués, R., Holcer, S., Torres-Sospedra, J., Casteleyn, S., Ruggeri, G., … Lohan, E. S. (2021). A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges. Computer Networks, 193, 108074. https://doi.org/10.1016/j.comnet.2021.108074

[22] Sabatini, A. M. (2011). Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing. Sensors, 11(2), 1489–1525. https://doi.org/10.3390/s110201489

[23] Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022). Machine Learning for Healthcare Wearable Devices: The Big Picture. Journal of Healthcare Engineering, 2022, 1–25. https://doi.org/10.1155/2022/4653923

[24] Sankar, V., Nambi, A., Bhat, V. N., Sethy, D., Balasubramaniam, K., Das, S., Guha, M., & Sundara, R. (2020). Waterproof Flexible Polymer-Functionalized Graphene-Based Piezoresistive Strain Sensor for Structural Health Monitoring and Wearable Devices. ACS Omega, 5(22), 12682–12691. https://doi.org/10.1021/acsomega.9b04205

[25] Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Hassan, M., & Seneviratne, A. (2017). A Survey of Wearable Devices and Challenges. IEEE Communications Surveys & Tutorials, 19(4), 2573–2620. https://doi.org/10.1109/COMST.2017.2731979

[26] Starner, T. (1999). Wearable computing and contextual awareness. Massachusetts Institute of Technology.

[27] Teixeira Da Silva, J. A. (2020). CiteScore: Advances, Evolution, Applications, and Limitations. Publishing Research Quarterly, 36(3), 459–468. https://doi.org/10.1007/s12109-020-09736-y

[28] Thelwall, M., Pinfield, S. (2024). The accuracy of field classifications for journals in Scopus. Scientometrics. https://doi.org/10.1007/s11192-023-04901-4

[29] Van Eck, N. J., Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

[30] Van Eck, N. J., Waltman, L. (2014). Visualizing Bibliometric Networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring Scholarly Impact (pp. 285–320). Springer International Publishing. https://doi.org/10.1007/978-3-319-10377-8_13

[31] Wang, C., Qi, H. (2021). Visualising the knowledge structure and evolution of wearable device research. Journal of Medical Engineering & Technology, 45(3), 207–222. https://doi.org/10.1080/03091902.2021.1891314

[32] Yang, C.-C., Hsu, Y.-L. (2010). A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors, 10(8), 7772–7788. https://doi.org/10.3390/s100807772


Copyright © 2023 dline.info