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Human motion activity recognition and pattern analysis using compressed deep neural networks

This work presents an on-device machine learning model with the ability to identify different mobility gestures called human activity recognition (HAR), which includes running, walking, squatting, jumping, and others. The data is collected through an Arduino Nano 33 BLE Sense board with a sampling r...

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Hoofdauteurs: Navita Kumari, Amulya Yadagani, Basudeba Behera, Vijay Bhaskar Semwal, Somya Mohanty
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Taylor & Francis Group 2024-12-01
Reeks:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
Onderwerpen:
Deep learning
internet of Medical Things (IoMT)
sensors
health monitoring
human activity recognition (HAR)
Online toegang:https://www.tandfonline.com/doi/10.1080/21681163.2024.2331052
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