A Comprehensive Study to Analyze Trends in Web Search Interests Related to Fall Detection Before and After COVID-19

ABSTRACT

Falls, considered a serious health-related concern for the elderly people, are associated with multiple diverse and dynamic needs for the elderly people themselves, their caregivers, their family members, and healthcare professionals. The modern-day Internet of Everything lifestyle is characterized by people using the internet for a multitude of reasons which also includes seeking and sharing information related to such needs. Such activity on the internet results in the generation of tremendous amounts of web behavior-based Big Data which can be studied and analyzed to investigate the trends in the underlining needs and the associated web search interests. The COVID-19 pandemic that the world is facing right now has impacted the elderly population to a significant extent. In fact, the elderly population is considered a demographic group that is most likely to get infected by this virus and develop serious symptoms, which could lead to hospitalizations and death. There hasn't been any study conducted in the field of aging research thus far that investigates how the COVID-19 pandemic may or may not have impacted the needs related to fall detection in the elderly. This work aims to address this research challenge. A dedicated methodology based on Google Trends is proposed in this paper that studies the web behavior-based Big Data related to fall detection from different countries both before and after the pandemic. The preliminary results presented from the analysis of the web behavior-based Big Data from 14 countries - USA, India, Germany, United Kingdom, Spain, Australia, Indonesia, Malaysia, Thailand, South Africa, Canada, Philippines, Sweden, and Ireland, which are amongst the countries worst hit by COVID-19, shows evidence that the pandemic had an impact towards increasing the web search interests related to fall detection in multiple countries. © 2022 ACM.