Real-Time Activity Tracking using TinyML to Support Elderly Care

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Kristof T'Jonck - , KU Leuven (Autor:in)
  • Chandrakanth R. Kancharla - , KU Leuven (Autor:in)
  • Jens Vankeirsbilck - , KU Leuven (Autor:in)
  • Hans Hallez - , KU Leuven (Autor:in)
  • Jeroen Boydens - , KU Leuven (Autor:in)
  • Bozheng Pang - , KU Leuven (Autor:in)

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

OriginalspracheEnglisch
Titel2021 XXX International Scientific Conference Electronics (ET)
Seiten1-6
ISBN (elektronisch)978-1-6654-4518-4
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusJa
Extern publiziertJa

Externe IDs

Scopus 85119009404