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

The epidemiological burden and societal cost of 14 respiratory conditions in the World Health Organization European region: systematic evidence map and economic analysis.

ERJ open research·2026
Same author

Use of Wearable Technology for Measuring and Characterizing Sedentary Behavior in People With Mild Cognitive Impairment and Dementia: Systematic Review.

JMIR aging·2026
Same author

Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions.

Scientific reports·2026
Same author

Temperature exposure and time adolescents spend in physical activity across intensity levels.

Environmental epidemiology (Philadelphia, Pa.)·2026
Same author

Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.

BMJ open respiratory research·2026
Same author

Laboratory-based turning performance during walking in people with mild cognitive impairment and dementia.

Journal of Alzheimer's disease : JAD·2026

Related Experiment Video

Updated: Jun 27, 2025

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

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

Felix Kluge1, Yonatan E Brand2, M Encarna Micó-Amigo3

  • 1Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland.

JMIR Formative Research
|May 1, 2024
PubMed
Summary

Wrist-worn sensors can detect gait sequences in real-world settings, aiding mobility analysis. However, lower back sensors offer superior accuracy for gait detection across diverse patient groups.

Keywords:
Mobilise-Daccelerometerdigital healthdigital mobility outcomesinertial measurement unitvalidationwalkingwearable sensor

More Related Videos

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
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.8K

Related Experiment Videos

Last Updated: Jun 27, 2025

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
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.8K

Area of Science:

  • Digital Health
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Wrist-worn inertial sensors are crucial for real-world mobility evaluation in digital health.
  • Gait detection algorithms are essential for analyzing long-term sensor data, but arm movements complicate wrist-based detection.
  • A comparative validation of wrist-worn gait detection algorithms across diverse patient populations and sensor positions is lacking.

Purpose of the Study:

  • To validate gait sequence (GS) detection algorithms for wrist-worn sensors using real-world data.
  • To compare the performance of wrist-worn sensor algorithms against those used with lower back-worn sensors.

Main Methods:

  • Eighty-three participants (including patients with Parkinson disease, multiple sclerosis, hip fracture recovery, COPD, heart failure, and healthy older adults) wore wrist, lower back, and foot inertial sensors.
  • A multisensor reference system (including pressure insoles and infrared distance sensors) was used for validation.
  • Ten wrist-based gait detection algorithms were validated and compared to lower back-based algorithms.

Main Results:

  • The best wrist-based algorithm achieved mean sensitivities between 0.55-0.81 and specificities between 0.95-0.98.
  • Estimated walking time error for the best wrist algorithm ranged from 8.9% to 32.7%.
  • Lower back sensors demonstrated superior performance, with mean sensitivities of 0.71-0.91, specificities of 0.96-0.99, and walking time errors of 6.3%-23.5%, particularly in patients with severe gait impairments.

Conclusions:

  • Wrist-worn sensors can detect gait sequences effectively in real-world scenarios, facilitating gait parameter extraction.
  • The study provides evidence for informed decisions regarding sensor placement in clinical gait studies.
  • Lower back sensor placement generally yields higher gait detection accuracy compared to wrist placement, especially in complex patient populations.