Efficient human activity recognition on edge devices using DeepConv LSTM architectures

Efficient human activity recognition on edge devices using DeepConv LSTM architectures

Abstract Driven by the rapid development of the Internet of Things (IoT), deploying deep learning models on resource-constrained hardware has become an increasingly critical challenge, which has propelled the emergence of TinyML as a viable solution. This study aims to deploy lightweight deep learni...

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Bibliographic Details
Main Authors: Haotian Zhou, Xiujun Zhang, Yu Feng, Tongda Zhang, Lijuan Xiong
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
TinyML
IoT
Deep Learning
Edge Computing
Model Quantization
Human Activity Recognition
Online Access:https://doi.org/10.1038/s41598-025-98571-2
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https://doi.org/10.1038/s41598-025-98571-2

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