A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition

A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition

Abstract Human Activity Recognition plays a vital role in various fields, such as healthcare and smart environments. Traditional HAR methods rely on sensor or video data, but sensor-based systems have gained popularity due to their non-intrusive nature. Current challenges in HAR systems include vari...

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Bibliographic Details
Main Authors: Murad Khan, Yousef Hossni
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Activity recognition
Deep learning
LSTM
Squeeze and excitation
Attention
Multi-head
Online Access:https://doi.org/10.1038/s41598-025-88378-6
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https://doi.org/10.1038/s41598-025-88378-6

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