IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v6y2021i8p92-d614696.html
   My bibliography  Save this article

Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions

Author

Listed:
  • Nirmalya Thakur

    (Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA)

  • Chia Y. Han

    (Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA)

Abstract

Falls, which are increasing at an unprecedented rate in the global elderly population, are associated with a multitude of needs such as healthcare, medical, caregiver, and economic, and they are posing various forms of burden on different countries across the world, specifically in the low- and middle-income countries. For these respective countries to anticipate, respond, address, and remedy these diverse needs either by using their existing resources, or by developing new policies and initiatives, or by seeking support from other countries or international organizations dedicated to global public health, the timely identification of these needs and their associated trends is highly necessary. This paper addresses this challenge by presenting a study that uses the potential of the modern Internet of Everything lifestyle, where relevant Google Search data originating from different geographic regions can be interpreted to understand the underlining region-specific user interests towards a specific topic, which further demonstrates the public health need towards the same. The scientific contributions of this study are two-fold. First, it presents an open-access dataset that consists of the user interests towards fall detection for all the 193 countries of the world studied from 2004–2021. In the dataset, the user interest data is available for each month for all these countries in this time range. Second, based on the analysis of potential and emerging research directions in the interrelated fields of Big Data, Data Mining, Information Retrieval, Natural Language Processing, Data Science, and Pattern Recognition, in the context of fall detection research, this paper presents 22 research questions that may be studied, evaluated, and investigated by researchers using this dataset.

Suggested Citation

  • Nirmalya Thakur & Chia Y. Han, 2021. "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions," Data, MDPI, vol. 6(8), pages 1-21, August.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:8:p:92-:d:614696
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/6/8/92/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/6/8/92/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Taekyoung Kim & Sang D Choi & Shuping Xiong, 2020. "Epidemiology of fall and its socioeconomic risk factors in community-dwelling Korean elderly," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-14, June.
    2. David John Hallford & Geoff Nicholson & Kerrie Sanders & Marita P McCabe, 2017. "The Association Between Anxiety and Falls: A Meta-Analysis," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 72(5), pages 729-741.
    3. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    4. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    5. José Carlos Castillo & Davide Carneiro & Juan Serrano-Cuerda & Paulo Novais & Antonio Fernández-Caballero & José Neves, 2014. "A multi-modal approach for activity classification and fall detection," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(4), pages 810-824, April.
    6. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.
    7. Shirin Wadhwaniya & Olakunle Alonge & Md. Kamran Ul Baset & Salim Chowdhury & Al-Amin Bhuiyan & Adnan A. Hyder, 2017. "Epidemiology of Fall Injury in Rural Bangladesh," IJERPH, MDPI, vol. 14(8), pages 1-13, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongtao Zhu & Huahu Xu & Xiaojin Ma & Minjie Bian, 2022. "Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention," Future Internet, MDPI, vol. 14(9), pages 1-17, August.
    2. Nirmalya Thakur & Shuqi Cui & Kesha A. Patel & Isabella Hall & Yuvraj Nihal Duggal, 2023. "A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions," Data, MDPI, vol. 8(11), pages 1-24, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    2. Juan Camilo Anzoátegui-Zapata & Juan Camilo Galvis-Ciro, 2020. "Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 99-122, November.
    3. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    4. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    5. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    6. Emanuele Ciani & Adeline Delavande & Ben Etheridge & Marco Francesconi, 2023. "Policy Uncertainty and Information Flows: Evidence from Pension Reform Expectations," The Economic Journal, Royal Economic Society, vol. 133(649), pages 98-129.
    7. Patrick Houlihan & Germán G. Creamer, 2021. "Leveraging Social Media to Predict Continuation and Reversal in Asset Prices," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 433-453, February.
    8. Sara Ayllón & Samuel Lado, 2022. "Food Hardship in the US During the Pandemic: What Can We Learn From Real‐Time Data?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(2), pages 518-540, June.
    9. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    10. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    11. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    12. Vicki Wei Tang, 2018. "Wisdom of Crowds: Cross‐Sectional Variation in the Informativeness of Third‐Party‐Generated Product Information on Twitter," Journal of Accounting Research, Wiley Blackwell, vol. 56(3), pages 989-1034, June.
    13. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
    14. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    15. Malyy, Maksim & Tekic, Zeljko & Podladchikova, Tatiana, 2021. "The value of big data for analyzing growth dynamics of technology-based new ventures," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    16. Livio Fenga, 2020. "Filtering and prediction of noisy and unstable signals: The case of Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 281-295, March.
    17. Tsoyu Calvin Lin & Shih-Hsun Hsu, 2020. "Forecasting Housing Markets from Number of Visits to Actual Price Registration System," International Real Estate Review, Global Social Science Institute, vol. 23(4), pages 505-536.
    18. Krzysztof Drachal & Daniel González Cortés, 2022. "Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data," IJERPH, MDPI, vol. 20(1), pages 1-24, December.
    19. Jolana Stejskalova, 2023. "We investigated the link between stock returns of automobile companies, Fama French factors, and behavioral attention, represented by demand for a selected car brand belonging to an automobile company," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(3), pages 202-221, March.
    20. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:6:y:2021:i:8:p:92-:d:614696. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.