Real-Time Activity Tracking using TinyML to Support Elderly Care
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
A vast majority of nursing home residents suffer from health issues such as incontinence, night wandering and pressure ulcers. The workload of nurses is noticeably increasing because of these problems. Previous research has shown that many of these complaints can be associated with specific movements in bed. This paper proposes the usage of accelerometer sensors in a non-invasive manner to detect these movements. Using deep learning on the edge, the discussed method provides immediate feedback to nurses to assist them with their care tasks.
Details
Originalsprache | Englisch |
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Titel | 2021 XXX International Scientific Conference Electronics (ET) |
Seiten | 1-6 |
ISBN (elektronisch) | 978-1-6654-4518-4 |
Publikationsstatus | Veröffentlicht - 2021 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Externe IDs
Scopus | 85119009404 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Bluetooth Low Energy, Convolutional Neural Network, Edge Computing, Edge Impulse, TinyML