An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition

An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition

This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes MediaPipe to capture the complex dynamics of human movements and introduce an i...

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Bibliographic Details
Main Authors: Micheal Dutt, Morten Goodwin, Christian W. Omlin
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Artificial intelligence
machine learning
deep learning
human activity recognition
healthcare
computer vision
Online Access:https://ieeexplore.ieee.org/document/10606472/
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https://ieeexplore.ieee.org/document/10606472/

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