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 14, 2026

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
10:35

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Published on: October 17, 2016

Machine-Learning-Based Color Sensing Using Wearable SENSIPATCH Spectrometer Module: An Experimental Study.

Hamza Mustafa1,2, Federico Fina3, Mario Molinara1

  • 1Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

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

Correction: CAFusion: A progressive ConvMixer network for context-aware infrared and visible image fusion.

PloS one·2026
Same author

PGCFuse: a U-Net-based progressive gated convmixer network with global context aggregation for infrared-visible image fusion.

Scientific reports·2026
Same author

CAFusion: A progressive ConvMixer network for context-aware infrared and visible image fusion.

PloS one·2026
Same author

A Portable Measurement System Based on Nanomembranes for Pollutant Detection in Water.

Sensors (Basel, Switzerland)·2025
Same author

A Novel ML-Powered Nanomembrane Sensor for Smart Monitoring of Pollutants in Industrial Wastewater.

Sensors (Basel, Switzerland)·2025
Same author

Inter-modality feature prediction through multimodal fusion for 3D shape defect detection.

Neural networks : the official journal of the International Neural Network Society·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

This study presents a machine learning approach for spectral color sensing using the SENSIPATCH wearable system. The Multilayer Perceptron (MLP) model achieved high accuracy in classifying PANTONE colors, even with variations in sensor placement.

Area of Science:

  • Spectroscopy
  • Wearable technology
  • Machine learning

Background:

  • Accurate color classification is vital in industries like textiles and biomedicine.
  • Existing methods may lack portability or adaptability.
  • Wearable sensors offer potential for on-site, real-time color analysis.

Purpose of the Study:

  • To develop and evaluate a machine learning framework for spectral color classification using a wearable sensor.
  • To assess the performance of different machine learning models for this task.
  • To investigate the robustness of the system against variations in wearable sensor placement.

Main Methods:

  • Utilized the spectrometer module of the SENSIPATCH wearable system, featuring multi-wavelength LEDs and photodiodes.
  • Collected spectral data for 100 standardized PANTONE colors under controlled lighting.
Keywords:
LEDPANTONEclassificationcolor sensingcolorsmachine learningsensorsspectroscopywearable

Related Experiment Videos

Last Updated: May 14, 2026

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
10:35

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Published on: October 17, 2016

  • Applied data preprocessing techniques including baseline correction, bootstrapping, and Z-score normalization.
  • Trained and evaluated five machine learning models: Random Forest, SVM, MLP, CNN, and LSTM.

    • Main Results:

    • The Multilayer Perceptron (MLP) model exhibited the highest color classification performance.
    • The MLP model demonstrated consistent accuracy across both firm and loose sensor contact scenarios.
    • The results indicate robustness against variations in wearable placement.

    Conclusions:

    • Compact LED-based wearable spectroscopy is feasible for reliable color classification under controlled conditions.
    • The developed MLP model shows promise for practical applications requiring accurate color sensing.
    • Further research can extend this approach to variable lighting conditions.