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 8, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Human motion classification based on a textile integrated and wearable sensor array.

D Teichmann1, A Kuhn, S Leonhardt

  • 1Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, D-52074 Aachen, Germany. teichmann@hia.rwth-aachen.de

Physiological Measurement
|August 16, 2013
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

"Optimal timing for RNA isolation: Recommendations for placenta sample collection under clinical conditions".

Placenta·2025
Same author

Nurse-led renal cell carcinoma clinic: a single center review.

ANZ journal of surgery·2024
Same author

Kinematic Effects of Combined Subthalamic and Dorsolateral Nigral Deep Brain Stimulation in Parkinson's Disease.

Journal of Parkinson's disease·2024
Same author

Spatially multiplexed single-molecule translocations through a nanopore at controlled speeds.

Nature nanotechnology·2023
Same author

Cell Type-Specific Decomposition of Gingival Tissue Transcriptomes.

Journal of dental research·2021
Same author

Pathogen-specific antibody profiles in patients with severe systemic infections.

European cells & materials·2020
Same journal

Continuous tracking of aortic aneurysm diameter with peripheral pulse waves: a computational framework combining sequential Markov chain Monte Carlo with Kalman filtering.

Physiological measurement·2026
Same journal

The 2026 global roadmap for textile-integrated wearable technologies in health.

Physiological measurement·2026
Same journal

Augmenting single-lead ECG interpretation through QRS waveform decomposition and rotation.

Physiological measurement·2026
Same journal

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Physiological measurement·2026
Same journal

Dual warm-start fusion versus attention-based fusion in low-label ECG-PCG classification: a controlled ablation study.

Physiological measurement·2026
Same journal

Inter-patient multi-label ECG classification via low-rank adaptation fine-tuned large language models with dynamic graph convolutional network.

Physiological measurement·2026
See all related articles

A wearable magnetic induction device accurately classifies motion patterns using advanced algorithms. The bootstrap aggregating decision tree achieved 96% accuracy, offering a promising solution for motion analysis.

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Signal Processing

Background:

  • Non-contact monitoring systems are crucial for unobtrusive physiological and motion tracking.
  • Textile-integrated wearable devices offer enhanced comfort and continuous data acquisition.
  • Accurate classification of motion patterns is essential for various applications, including healthcare and sports analytics.

Purpose of the Study:

  • To present a novel system for motion pattern classification using a textile-integrated, wearable magnetic induction device.
  • To evaluate the performance of different machine learning classifiers for motion pattern recognition.
  • To investigate the impact of input sample length on classification accuracy.

Main Methods:

  • Development of a non-contact magnetic induction monitoring device integrated into textiles.

More Related Videos

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
04:06

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

Published on: January 12, 2024

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 8, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
04:06

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

Published on: January 12, 2024

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Implementation and comparison of three classifiers: neural network, support vector machine, and bootstrap aggregating decision tree.
  • Feature extraction using discrete wavelet transform and statistical measures.
  • Performance evaluation on a dataset of five distinct motion patterns.

    • Main Results:

    • All tested classifiers achieved over 93% accuracy in classifying motion patterns.
    • The bootstrap aggregating decision tree yielded the highest classification accuracy at 96%.
    • The support vector machine classifier demonstrated minimal sensitivity to variations in input sample length.

    Conclusions:

    • The textile-integrated magnetic induction device is effective for motion pattern classification.
    • Bootstrap aggregating decision tree offers superior performance for this specific application.
    • Support vector machines provide robust classification across different sample lengths, indicating potential for real-time applications.