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...
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Taylor & Francis Group
2024-12-01
|
| Serie: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Soggetti: | |
| Accesso online: | https://www.tandfonline.com/doi/10.1080/21681163.2024.2331052 |
| Tags: |
Nessun Tag, puoi essere il primo ad aggiungerne!!
|