Jove
Visualize
Contact Us

Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.1K
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...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Extracellular Space Barrier Dysfunction Disrupts Interstitial Fluid Drainage and Is Associated with Memory Heterogeneity in Cognitive Aging.

Aging and disease·2026
Same author

Robot-Assisted Gravity Compensation for Upper Limb Motor Rehabilitation: A Systematic Review.

Bioengineering (Basel, Switzerland)·2026
Same author

CRISPR-Mediated Metabolic Engineering of <i>Escherichia coli</i> W for Selective Biopurification of Stachyose from Soybean Molasses.

Microorganisms·2026
Same author

Differential Immune Cell Distribution Between Arterial and Venous Blood in Healthy Rats.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

Antibody conjugation of paclitaxel enables aqueous compatibility and enhances tumor-targeted efficacy <i>in vitro</i> and <i>in vivo</i>.

Frontiers in pharmacology·2026
Same author

Integrating Multi-Task Eye Tracking and Interpretable Machine Learning for High-Accuracy Screening of Amblyopia in Pediatric Populations.

Journal of eye movement research·2026
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 2, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

30.5K

An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.

Renas Ercan1,2, Yunjia Xia3, Yunyi Zhao3

  • 1UCL UCL WC1E 6BT London U.K.

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
|May 20, 2024
PubMed
Summary

This study introduces an ultra-low-power machine learning module for detecting motion artifacts in functional near-infrared spectroscopy (fNIRS) systems. The developed field-programmable gate array (FPGA) based classifier achieves high accuracy while meeting critical low-power and resource constraints for wearable devices.

Keywords:
Field-programmable gate array (FPGA)functional near-infrared spectroscopy (fNIRS)low powermachine learningmotion artifact detectionreal timesupport vector machines (SVMs)

More Related Videos

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.2K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

6.6K

Related Experiment Videos

Last Updated: May 2, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

30.5K
Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.2K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

6.6K

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Wearable Technology

Background:

  • Machine learning models demand significant computational resources, limiting their application in power-constrained wearable devices.
  • Motion artifacts are a major challenge in functional near-infrared spectroscopy (fNIRS) data acquisition, affecting signal quality and analysis.

Purpose of the Study:

  • To develop an ultra-low-power, real-time machine learning-based motion artifact detection module for fNIRS systems.
  • To enable the deployment of fNIRS technology in wearable devices with limited processing power and memory.

Main Methods:

  • Implementation of a machine learning classifier on a field-programmable gate array (FPGA).
  • Optimization for ultra-low power consumption and minimal resource utilization (LUTs, FFs).
  • Real-time processing of fNIRS data for motion artifact detection.

Main Results:

  • Achieved a high classification accuracy of 97.42% for motion artifact detection.
  • Demonstrated ultra-low dynamic power consumption of 0.021 W.
  • Reported low FPGA resource utilization (38,354 LUTs, 6,024 FFs), outperforming conventional CPU-based Support Vector Machine (SVM) methods.

Conclusions:

  • FPGA-based fNIRS motion artifact classification is feasible within low power and resource constraints.
  • The developed module meets crucial embedded hardware requirements for wearable systems.
  • High classification accuracy is maintained, paving the way for robust wearable fNIRS applications.