Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Benchmarking Time-Series Artificial Intelligence Architectures for Wearable Sensor-Based Fall Prediction: A Synthetic

Edward R Sykes¹, Mohammad Maghsoudimehrabani¹, Abdulrahman Al-Shanoon¹

¹School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada.

Sensors (Basel, Switzerland)

|June 12, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Monitoring Upper Extremity Function of Individuals With Breast Cancer: Development and Usability of the <i>StrongArms-Cancer</i> mHealth System.

Inquiry : a journal of medical care organization, provision and financing·2026

Same author

RemoteHealthConnect: Innovating patient monitoring with wearable technology and custom visualization.

Digital health·2024

Same author

Optimized virtual reality-based <i>Method of Loci</i> memorization techniques through increased immersion and effective memory palace designs: a feasibility study.

Virtual reality·2022

See all related articles

This study introduces a synthetic framework for predicting falls in older adults, finding that classical machine learning models offer better early-warning performance than temporal models. Careful calibration and alert design are crucial for effective fall-risk prediction systems.

Area of Science:

Gerontology
Biomedical Engineering
Artificial Intelligence

Background:

Falls in older adults are a significant cause of injury and loss of independence.
Current fall detection systems often lack early warning capabilities, detecting falls only after they occur.

Purpose of the Study:

To develop and present a synthetic benchmarking framework for early fall-risk prediction.
To compare the performance of classical and temporal machine learning architectures for early fall detection.

Main Methods:

Generated a synthetic dataset of 1000 sequences simulating normal activity, slips, and pre-fall instability using biomechanical, physiological, and contextual data.
Trained and evaluated eight baseline models and two augmented temporal variants using subject-wise splits to prevent data leakage.

Keywords:

early warning fall prediction multi-sensor fusion older adults synthetic data simulation temporal deep learning time-series classification wearable sensors

More Related Videos

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 13, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Assessed model performance using a realistic evaluation protocol, focusing on early-warning capabilities and alert triggering.

Main Results:

Classical machine learning baselines achieved superior macro-F1 scores compared to temporal models.
Early-warning performance varied significantly; some models failed to trigger alerts, while others increased false alarms with higher pre-fall trigger rates.
Model performance was highly dependent on the data partitioning strategy, calibration methods, and alert design.

Conclusions:

The proposed synthetic benchmarking framework offers a reproducible method for evaluating early-warning fall-risk prediction models.
Findings highlight the critical role of calibration and alert design in the operational effectiveness of fall prediction systems.
Further validation with real-world data and deployment-oriented strategies is recommended.