Batumalai Puspamalar, Thazhakkattu Vasu Deepak, Selvakumar Kiruthika, Choon Hian Goh
Department of Physiotherapy, M. Kandiah Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman (UTAR), Kajang, Selangor, Malaysia.
Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman (UTAR), Kajang, Selangor, Malaysia.
Medicine (Baltimore). 2025 Aug 29;104(35):e44118. doi: 10.1097/MD.0000000000044118.
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.
跌倒对老年人构成了重大的公共卫生挑战，影响发病率、死亡率和独立性。传统评估方法往往缺乏精确性和实用性，因此需要开发创新解决方案。利用加速度计、陀螺仪和机器学习算法的可穿戴传感器已成为实时跌倒风险监测的变革性工具。
本研究旨在通过文献计量分析探索可穿戴传感器在跌倒风险评估中的研究概况，识别过去20年中塑造该领域进展的关键趋势、技术突破和贡献者。
对Scopus数据库进行系统检索，分析2000年至2024年的学术成果。使用目标关键词，识别出221项经同行评审的研究并汇总成一个数据集。利用VOSviewer和Publish or Perish等分析工具来可视化研究网络、知识贡献和引用指标，从而深入了解该领域的发展。
自2013年以来，研究活动激增，凸显了可穿戴技术日益增长的重要性。美国在这一领域领先，欧洲和亚洲也做出了重大贡献。关键主题领域包括医学、计算机科学和工程学，“平衡”“步态”和“跌倒风险”等关键词占主导地位。机器学习和传感器技术的进步提高了预测准确性和可用性。
可穿戴传感器正在彻底改变跌倒风险评估，提供精确性、便携性和实用性。解决可用性、可承受性和标准化问题对于公平获取至关重要，其前景不仅在于预防跌倒，还在于增强老年人的信心并改善他们的生活质量。
