Vinod Chandran

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data

Publisher: MDPI AG

Date: 17-10-2019

DOI: 10.3390/S19204509

Abstract: This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12–14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.

Representation of facial expression categories in continuous arousal–valence space: Feature and correlation

Publisher: Elsevier BV

Date: 12-2014

DOI: 10.1016/J.IMAVIS.2014.09.005

Near-surface interface detection for coal mining applications using bispectral features and GPR

Publisher: Springer Science and Business Media LLC

Date: 04-2005

DOI: 10.1007/S11220-005-0003-2

Toward a more robust facial expression recognition in occluded images using randomly sampled Gabor based templates

Publisher: IEEE

Date: 07-2011

DOI: 10.1109/ICME.2011.6012015

HOS-FingerCode: Bispectral invariants based contactless multi-finger recognition system using ridge orientation and feature fusion

Publisher: Elsevier BV

Date: 09-2022

DOI: 10.1016/J.ESWA.2022.117054

Automatic Emotion Recognition Using Temporal Multimodal Deep Learning

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/ACCESS.2020.3027026

Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review–Challenges, Current and Future Potential Techniques

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/RBME.2020.3004639

Geometry vs. Appearance for Discriminating between Posed and Spontaneous Emotions

Publisher: Springer Berlin Heidelberg

Date: 2011

DOI: 10.1007/978-3-642-24965-5_49

Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos

Publisher: Elsevier BV

Date: 12-2018

DOI: 10.1016/J.FIRESAF.2018.09.003

Face authentication test on the BANCA database

Publisher: IEEE

Date: 2004

DOI: 10.1109/ICPR.2004.1333826

Analysis of detection performance in spectrum sensing optimisation for long sensing periods

Publisher: IEEE

Date: 09-2011

DOI: 10.1109/PIMRC.2011.6140002

Evaluation of Texture and Geometry for Dimensional Facial Expression Recognition

Publisher: IEEE

Date: 12-2011

DOI: 10.1109/DICTA.2011.110

Evaluation of image resolution and super-resolution on face recognition performance

Publisher: Elsevier BV

Date: 2012

DOI: 10.1016/J.JVCIR.2011.06.004

3D face verification using a free-parts approach

Publisher: Elsevier BV

Date: 07-2008

DOI: 10.1016/J.PATREC.2008.01.018

Multi-spectral fusion for surveillance systems

Publisher: Elsevier BV

Date: 07-2010

DOI: 10.1016/J.COMPELECENG.2008.11.011

3D Face Tracking Using Stereo Cameras: A Review

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/ACCESS.2020.2994283

Object tracking using multiple motion modalities

Publisher: IEEE

Date: 12-2008

DOI: 10.1109/ICSPCS.2008.4813757

Texas Tech University

Location: United States of America

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Washington State University

Location: United States of America

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Indian Institute Of Technology Madras

Location: India

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Discovery Projects - Grant ID: DP0452676

Start Date: 02-2004

End Date: 12-2007

Amount: $150,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP110100827

Start Date: 2011

End Date: 12-2014

Amount: $255,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0669582

Start Date: 07-2006

End Date: 07-2011

Amount: $73,950.00

Funder: Australian Research Council