Design and implementation of a wearable accelerometer-based motion/tilt sensing internet of things module and its application to bed fall prevention

Wen Yen Lin*, Chien Hung Chen, Ming Yih Lee

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

7 Scopus citations

Abstract

Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chippackaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.

Original languageEnglish
Article number428
JournalBiosensors
Volume11
Issue number11
DOIs
StatePublished - 11 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Accelerometer
  • Bed-fall prevention
  • Health-care
  • Internet-of-Things
  • Motion and tilt sensing
  • Wearable sensor

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