FSID: a novel approach to human activity recognition using few-shot weight imprinting

FSID: a novel approach to human activity recognition using few-shot weight imprinting

Abstract Accurate recognition of human activities from gait sensory data plays a vital role in healthcare and wellness monitoring. However, conventional deep learning models for Human Activity Recognition (HAR) often require large labeled datasets and extensive training, which limits their effective...

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Bibliographic Details
Main Authors: Mohammad Belal, Taimur Hassan, Abdelfatah Hassan, Divya Velayudhan, Noureldin Elhendawi, Ahmad Aljarah, Irfan Hussain
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Human activity recognition
Deep learning
Few-shot learning
Fourier transform
Weight imprinting
Online Access:https://doi.org/10.1038/s41598-025-04323-7
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https://doi.org/10.1038/s41598-025-04323-7

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