A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults

Manting CHEN, Hailiang WANG, Lisha YU, Eric Hiu Kwong YEUNG, Jiajia LUO, Kwok Leung TSUI, Yang ZHAO*

*Corresponding author for this work

Research output: Journal PublicationsReview articleOther Review

18 Citations (Scopus)


Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

Original languageEnglish
Article number6752
Number of pages18
Issue number18
Publication statusPublished - Sept 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 by the authors.


  • community-dwelling older adults
  • fall risk assessment
  • functional test
  • sensor technology


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