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.AbstractFalls, 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.

任何使用本数据集的研究工作均应引用本文如下：Nirmalya Thakur 和 Chia Y. Han，《2004-2021年各国对跌倒检测的兴趣研究：一个开放获取数据集及研究问题》，数据期刊，第6卷，第8期，第1-21页，2021年。摘要：跌倒事件在全球老年人口中的发生率正以前所未有的速度上升，与医疗保健、医疗、护理人员和经济等多重需求密切相关，并对世界各国，尤其是低收入和中等收入国家造成了各种形式的负担。为了使这些国家能够预测、应对、解决和补救这些多样化的需求，无论是通过利用现有资源，还是通过制定新的政策或倡议，亦或是寻求致力于全球公共卫生的国际组织或其他国家的支持，及时识别这些需求和相关的趋势显得尤为必要。在当代万物互联的生活方式中，源自不同地理区域的谷歌搜索数据可以解读为理解特定主题的区域特定用户兴趣，这进一步彰显了公众对公共卫生需求的紧迫性，为解决这一挑战提供了潜在的可能性。因此，本研究利用万物互联生活方式的潜在优势，旨在通过提供一个开放获取的数据集，涵盖2004-2021年间全球193个国家的跌倒检测用户兴趣，来应对上述挑战。该数据集中的用户兴趣数据涵盖了上述时间段内所有这些国家每个月的数据。关于该数据集的开发、相关研究方法和数据描述已在论文《2004-2021年各国对跌倒检测的兴趣研究：一个开放获取数据集及研究问题》（URL：https://www.mdpi.com/2306-5729/6/8/92）中详细介绍。基于大数据、数据挖掘、信息检索、自然语言处理、数据科学和模式识别等领域中潜在和新兴的研究方向的分析，在跌倒检测研究的背景下，本文还提出了22个可能由研究人员使用本数据集进行研究、评估和探讨的研究问题。