Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments

Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments

Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–co...

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Bibliographic Details
Main Authors: Yirga Yayeh Munaye, Metadel Addis, Yenework Belayneh, Atinkut Molla, Wasyihun Admass
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Algorithms
Subjects:
outdoor environment
human activity
deep learning
Wi-Fi signals
Online Access:https://www.mdpi.com/1999-4893/18/4/235
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