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Updated: Jun 8, 2025
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Emerging Digital Technologies Used for Fall Detection in Older Adults in Aged Care: A Scoping Review
Sriyani Padmalatha Konara Mudiyanselage1, Ching Teng Yao2, Sujeewa Dilhani Maithreepala3
1College of Nursing, Kaohsiung Medical University, Kaohsiung, Taiwan; Operation theatre Department, The National Hospital of Sri Lanka, Colombo, Sri Lanka; Institute of Behavioral Medicine, The National Cheng Kung University, Tainan, Taiwan.
Objective:
To explore a comprehensive overview of digital technologies used for fall detection in older adults, categorizing the types, functions, and usability of these systems.
Design:
A scoping review was conducted to search across 5 databases [Embase, Medline (OVID), CINAHL, Coherence and IEEE Explore] from January 2013 to September 2023.
Setting And Participants:
Studies in older adults living in nursing homes, care homes, residential homes, respite care homes, and all skilled and ambulatory care facilities (without context restrictions).
Methods:
This review followed the 6 methodological stages: (1) identification of research question; (2) identification of relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and an optional stage, (6) consulting with stakeholders regarding findings to explore pivotal concepts in emerging technology usage in long-term care for falls detection among older people. Data were extracted and categorized based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines.
Results:
A total of 73 studies met the inclusion criteria. Four main categories of fall detection technologies were identified: motion and sensor technologies, imaging and visual systems, environmental sensors, and robotic and autonomous systems. Commonly used devices: wearable accelerometers, gyroscopes, infrared array sensors, and smart carpet pressure sensors. Data storage methods were wearable devices, cameras, and floor-mounted sensors. Communication technologies included Bluetooth, Wi-Fi, and GPS, and notification methods ranged from alarms and SMS to cloud communications. Various health care response teams, including caregivers, health care providers, and emergency services, were integral to the fall detection systems.
Conclusions And Implications:
Most studies primarily focus on fall detection; however, we recommend further clinical research to emphasize both fall detection and, more importantly, fall prevention (both primary and secondary). Investigating the effectiveness of fall prevention technologies in real-world settings will be crucial for enhancing the safety and quality of life of the aging population.
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