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Related Experiment Video

Updated: Aug 23, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Fall detection using accelerometer-based smartphones: Where do we go from here?

Tristan Stampfler¹, Mohamed Elgendi¹, Richard Ribon Fletcher²

¹Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

Frontiers in Public Health

|November 3, 2022

Summary

This summary is machine-generated.

Falls are a major global health risk, especially for older adults. This review examines smartphone apps for fall detection, finding real-time capabilities but highlighting limitations in real-world applicability and data science methods.

Keywords:

behavioral tracking daily activity detection digital phenotyping mHealth mobile health remote patient monitoring smart assistive technology wearable devices

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Published on: December 11, 2015

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Last Updated: Aug 23, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Gerontology
Biomedical Engineering
Health Technology

Background:

Falls represent a significant global public health concern, ranking as the second leading cause of unintentional injury deaths worldwide.
Older adults are particularly susceptible to falls, necessitating advanced detection and reporting systems.
Smartphone accelerometers offer a pervasive platform for developing fall detection and alerting applications.

Conclusions:

While current smartphone-based fall detection systems show promise for real-time alerts, their real-world performance is questionable due to methodological limitations.
Future research must address the identified gaps in experimental design, activity scope, and robust data evaluation to improve algorithm reliability.
The review provides a ranking of studies by bias risk and offers 12 recommendations to guide future advancements in fall detection technology.