Dataset of Country-Specific Interests towards Fall Detection from 2004–2021
Any work using this dataset should cite this paper as follows:
Nirmalya Thakur and Chia Y. Han, "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions", Journal of Data, Volume 6, Issue 8, pp. 1-21, 2021.
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. The modern-day 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, holds the potential towards addressing this challenge. Therefore, this work leverages this potential of the Internet of Everything lifestyle and aims to address above the above-mentioned challenges by presenting 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.
The work towards the development of this dataset, along with the associated research methods and data description, has been presented in the paper – "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions" (URL: https://www.mdpi.com/2306-5729/6/8/92). 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 also presents 22 research questions that may be studied, evaluated, and investigated by researchers using this dataset.
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The instructions on how to use this dataset are available in the above-mentioned paper.