A bibliometric analysis of wearable sensors for fall-risk assessment in the elderly population

Falls pose a significant public health challenge for the elderly, impacting morbidity, mortality, and independence. Traditional assessment methods often lack precision and practicality, necessitating the development of innovative solutions. Wearable sensors, utilizing accelerometers, gyroscopes, and machine learning algorithms, have emerged as transformative tools for real-time fall-risk monitoring.

This study aimed to explore the research landscape of wearable sensors in fall-risk assessment through bibliometric analysis, identifying key trends, technological breakthroughs, and contributors that have shaped advancements in the field over the past 2 decades.

A systematic search of the Scopus database was conducted, analyzing scholarly outputs from 2000 to 2024. Using targeted keywords, 221 peer-reviewed studies were identified and aggregated into a dataset. Analytical tools like VOSviewer and Publish or Perish were utilized to visualize research networks, intellectual contributions, and citation metrics, offering insights into the field's evolution.

Research activity has surged since 2013, highlighting the growing importance of wearable technologies. The United States leads this domain, with significant contributions from Europe and Asia. Key thematic areas include medicine, computer science, and engineering, with keywords such as "balance," "gait," and "fall risk" predominating. Advances in machine learning and sensor technology have enhanced predictive accuracy and usability.

Wearable sensors are revolutionizing fall-risk assessment, offering precision, portability, and practicality. Addressing usability, affordability, and standardization will be critical for equitable access, and its promise lies not only in preventing falls but in empowering the elderly with confidence and improving their quality of life.