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What is it about?

This work presents a fusion HAR approach that combines data readings from wearable sensors such as accelerometer and gyroscope sensors and Images captured by vision-based sensors such as cameras incorporating the capabilities of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models.

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Why is it important?

Human activity recognition (HAR) has emerged as a fundamental capability in various disciplines, including ambient assisted living, healthcare, human-computer interaction, etc. This study proposes a novel approach for activity recognition by integrating IoT technologies with Artificial Intelligence and Edge Computing.

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This page is a summary of: Enhanced Aiot Multi‐Modal Fusion for Human Activity Recognition in Ambient Assisted Living Environment, Software Practice and Experience, December 2024, Wiley,
DOI: 10.1002/spe.3394.
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