An inferential real-time falling posture reconstruction for Internet of healthcare things

Cong Zhang, Chin Feng Lai, Ying Hsun Lai, Zhen Wei Wu, Han Chieh Chao

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

This study constructs an approach to reproduce the real-time falls of humans, which uses a triaxial accelerometer and triaxial gyroscope to detect the occurrence of a fall, and an attitude algorithm to estimate the angles of each part of the human body, where Internet of healthcare things collects the information of each sensor, and a Bayesian Network deduces the next action. Inferential Bayesian probability could present more complete data of a fall to healthcare providers. Even if the data are damaged by the transmission network or equipment, the next action still could be deduced by Bayesian probability, and because the fall could be reproduced in a 3D Model on the client side, the fall occurrence is shown more intuitively, and could thus serve as reference for first aid.

Original languageEnglish
Pages (from-to)86-95
Number of pages10
JournalJournal of Network and Computer Applications
Volume89
DOIs
Publication statusPublished - 2017 Jul 1

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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