Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor.

A K Bourke1, G M Lyons

  • 1Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland.

Medical Engineering & Physics
|January 16, 2007
PubMed
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

Scalp hair, facial skin and pregnancy.

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology·2018
Same author

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016
Same author

Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring.

Medical engineering & physics·2014
Same author

Activity classification using a single chest mounted tri-axial accelerometer.

Medical engineering & physics·2011
Same author

Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities.

Journal of biomechanics·2010
Same author

Does size matter? The impact of calf muscle volume on venous return in patients with venous leg ulcers.

Phlebology·2008
Same journal

Enabling laboratory-based personalization of musculoskeletal spine models: a standardized rail-guided ultrasound method.

Medical engineering & physics·2026
Same journal

From diagnostic labels to radiology reports: a unified multi-modal framework for lesion detection and segmentation.

Medical engineering & physics·2026
Same journal

Dual-view cross-semantic graph neural network for predicting intraoperative complications in patients with acute myocardial infarction undergoing percutaneous coronary intervention.

Medical engineering & physics·2026
Same journal

Finite element analysis explores ablation depth and optical zone as key determinants of intraocular pressure reduction after refractive surgery.

Medical engineering & physics·2026
Same journal

A preliminary technique development for quantitative analysis of transhumeral bone, post-amputation, using a computed tomography BMD phantom.

Medical engineering & physics·2026
Same journal

Characteristics and performance evaluation of the Maastricht room calorimeter systems.

Medical engineering & physics·2026
See all related articles

A new algorithm accurately distinguishes falls from daily activities using a trunk-mounted gyroscope. This fall detection system achieves 100% accuracy, enhancing safety for the elderly.

Area of Science:

  • Biomedical Engineering
  • Gerontology
  • Wearable Technology

Background:

  • Falls are a significant risk for the elderly, leading to injury and loss of independence.
  • Accurate fall detection systems are crucial for timely intervention and improved healthcare outcomes.
  • Existing methods may struggle to reliably differentiate falls from Activities of Daily Living (ADL).

Purpose of the Study:

  • To develop and validate a threshold-based algorithm for distinguishing between falls and ADL.
  • To assess the efficacy of a gyroscope-based sensor array for fall detection.
  • To establish reliable detection parameters using trunk-mounted sensor data.

Main Methods:

  • Utilized a bi-axial gyroscope sensor array mounted on the trunk to measure pitch and roll angular velocities.

Related Experiment Videos

  • Collected data from simulated falls by young volunteers and ADL by elderly subjects.
  • Analyzed data using Matlab to determine angular accelerations, velocities, and trunk angle changes.

    • Main Results:

    • Identified three key thresholds for fall detection: resultant angular velocity (>3.1 rad/s), angular acceleration (>0.05 rad/s²), and change in trunk angle (>0.59 rad).
    • Achieved 100% accuracy in distinguishing falls from ADL across a dataset of 480 movements.
    • Demonstrated the algorithm's effectiveness in discriminating between eight different fall and ADL types.

    Conclusions:

    • The developed threshold-based algorithm, utilizing trunk-mounted gyroscope data, reliably distinguishes falls from ADL.
    • This system offers a highly accurate and promising solution for fall detection in elderly populations.
    • The findings support the potential of wearable sensor technology for enhancing elder care and safety.