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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Exploring the empowerment process in hip arthroplasty patients: Insights from rehabilitation nursing.

International journal of orthopaedic and trauma nursing·2026
Same author

Physiological responses to emotional video stimuli: ECG, EDA, and temperature data.

Data in brief·2026
Same author

Molecular Origin of Aneotropy and Related Surface Tension Anomalies in Hydrogenated and Fluorinated Alcohol Mixtures: New Experimental Data and Theoretical Molecular Modeling.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Characterising the cancer healthcare professional workforce in the UK.

British journal of nursing (Mark Allen Publishing)·2026
Same author

Management of tandem occlusions in acute ischaemic stroke.

Therapeutic advances in neurological disorders·2026
Same author

The family caregiver of the older person with hip fracture: perceptions about the transition to home.

International journal of orthopaedic and trauma nursing·2026

Related Experiment Video

Updated: Jul 2, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.1K

Mobile Data Gathering and Preliminary Analysis for the Functional Reach Test.

Luís Francisco1, João Duarte1, Carlos Albuquerque2,3,4

  • 1Electrotechnical Department, Polytechnic University of Leiria, 2411-901 Leiria, Portugal.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new tool using smartphone sensors to automate the Functional Reach Test (FRT), aiming to improve balance assessment accuracy and reduce practitioner bias in older adults.

Keywords:
functional reach testinertial sensorsmonitoring appssmart wearables

More Related Videos

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.7K
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

8.9K

Related Experiment Videos

Last Updated: Jul 2, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.1K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.7K
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

8.9K

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • The Functional Reach Test (FRT) is vital for assessing dynamic balance and fall risk in older adults and neurological patients.
  • Accurate FRT data aids in designing effective rehabilitation programs to enhance balance and mitigate fall risks.

Purpose of the Study:

  • To develop and evaluate a novel tool for automated data acquisition and analysis of the FRT using inertial sensors.
  • To minimize practitioner bias and streamline the FRT procedure through technological integration.

Main Methods:

  • Developed a mobile application for data collection via smartphone inertial sensors.
  • Employed sensor-fusion algorithms (e.g., Madgwick) for orientation estimation.
  • Investigated position estimation through double integration of accelerometer data.

Main Results:

  • Successfully gathered FRT data from 54 senior citizens using smartphones.
  • Orientation estimation was feasible, but accurate position estimation proved challenging, indicating areas for further research.
  • Demonstrated the potential and limitations of automated balance assessment using mobile device sensors.

Conclusions:

  • Automated balance assessment using mobile device sensors offers benefits and drawbacks compared to traditional methods.
  • Further research and development are needed to refine position estimation for comprehensive automated FRT.
  • Technology integration holds significant promise for enhancing conventional health evaluations and fall risk assessment.