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 Video

Updated: May 14, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

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

Published on: December 11, 2015

Towards the run and walk activity classification through step detection--an android application.

Melis Oner1, Jeffry A Pulcifer-Stump, Patrick Seeling

  • 1School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI 48859, USA. oner1m@cmich.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

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

A systematic review of CBRN vs traditional mass casualty triage system in disaster management.

American journal of disaster medicine·2026
Same author

DCPat-XFE: an explainable EEG model for psychogenic nonepileptic seizure detection.

Cognitive neurodynamics·2025
Same author

A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives.

Scientific reports·2024
Same author

Healing Hands: The Tactile Internet in Future Tele-Healthcare.

Sensors (Basel, Switzerland)·2022
Same author

Real-Time Compression for Tactile Internet Data Streams.

Sensors (Basel, Switzerland)·2021
Same author

Dataset for WWW landing pages webobject retrieval performance evaluation.

Data in brief·2020

This study developed a simple fall detection algorithm using smartphone accelerometer data. The system accurately tracks steps without requiring fixed sensor orientation, benefiting seniors and disabled individuals.

Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Gerontology

Background:

  • Falls are a major cause of irreversible injury, particularly for the elderly and disabled.
  • Early fall detection systems are crucial for mitigating fall consequences.
  • Existing systems often require rigid sensor placement, limiting usability.

Purpose of the Study:

  • To develop and validate a user-friendly fall detection algorithm.
  • To enable accurate step counting and activity monitoring using a smartphone's accelerometer.
  • To overcome the limitation of fixed sensor orientation in previous research.

Main Methods:

  • An algorithm was developed to analyze accelerometer data for step detection.
  • The system tracks the total number of steps taken by the user.

More Related Videos

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
07:51

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds
06:26

Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds

Published on: September 27, 2024

Related Experiment Videos

Last Updated: May 14, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

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

Published on: December 11, 2015

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
07:51

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds
06:26

Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds

Published on: September 27, 2024

  • No specific orientation for the smartphone (sensor) was required.

    • Main Results:

    • The algorithm successfully distinguished between basic human activities like walking and running.
    • Step counting accuracy was higher than a commercial pedometer.
    • The system demonstrated robustness regardless of sensor orientation.

    Conclusions:

    • A simple, effective fall detection algorithm using smartphone accelerometers was successfully implemented.
    • The developed system offers a practical solution for fall risk monitoring in vulnerable populations.
    • The algorithm's independence from fixed sensor orientation enhances its accessibility and ease of use.