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

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

949
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
949
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

1.9K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
1.9K
IR Spectrum01:19

IR Spectrum

1.1K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Interchannel Interference and Mitigation in Distributed MIMO RF Sensing.

Sensors (Basel, Switzerland)·2021
See all related articles

Related Experiment Video

Updated: Jul 23, 2025

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

9.0K

Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing.

Muhammad Muaaz1, Sahil Waqar1, Matthias Pätzold1

  • 1Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary

This study introduces a new radio frequency (RF) sensing method using two orthogonal radars to accurately detect human activities and falls, regardless of orientation. This advanced approach significantly improves upon traditional single-radar systems.

Keywords:
activity recognitiondata fusiondistributed mmWave MIMO radarfall detectionfeature extractionmean Doppler shiftmicro-Doppler signaturesupport vector machine

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

11.0K

Related Experiment Videos

Last Updated: Jul 23, 2025

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

9.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

11.0K

Area of Science:

  • Utilizes radio frequency (RF) sensing for human activity recognition (HAR) and fall detection.
  • Leverages millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) radar technology.

Background:

  • Current RF-based HAR systems often rely on a single monostatic radar.
  • Single monostatic radars have limitations in detecting motion orthogonal to their axis, hindering orientation-independent activity recognition.
  • This limitation prevents efficient recognition of diverse human activities and falls in real-world scenarios.

Purpose of the Study:

  • To develop a complementary RF sensing approach to overcome the limitations of single monostatic radar-based HAR systems.
  • To robustly recognize orientation-independent human activities and detect accidental falls.
  • To enhance the accuracy and reliability of human activity recognition systems.

Main Methods:

  • Employed a distributed mmWave MIMO radar system with two orthogonally placed monostatic radars.
  • Captured two distinct time-variant micro-Doppler signatures by illuminating subjects from different aspect angles.
  • Computed Mean Doppler Shifts (MDSs), extracted statistical, time-, and frequency-domain features, fused them at the feature level, and used a Support Vector Machine (SVM) for classification.

Main Results:

  • Achieved a high overall classification accuracy ranging from 98.31% to 98.54% on an orientation-independent human activity dataset.
  • The dataset comprised over 1350 activity trials from six volunteers performing five different activities in various orientations.
  • Demonstrated a 6% performance improvement over conventional single monostatic radar-based HAR systems.

Conclusions:

  • The proposed complementary RF sensing approach effectively overcomes the inherent limitations of single monostatic radar systems.
  • This method robustly recognizes orientation-independent human activities and detects falls with high accuracy.
  • The dual-orthogonal radar configuration offers a significant advancement in privacy-preserving human sensing.