Next Article in Journal
A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield
Next Article in Special Issue
n+ GaAs/AuGeNi-Au Thermocouple-Type RF MEMS Power Sensors Based on Dual Thermal Flow Paths in GaAs MMIC
Previous Article in Journal
Breathing Analysis Using Thermal and Depth Imaging Camera Video Records
Previous Article in Special Issue
Design and Fabrication of Piezoelectric Micromachined Ultrasound Transducer (pMUT) with Partially-Etched ZnO Film
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(6), 1393;

An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier

Beijing Advanced Innovation Center for Future Internet Technology, Beijing 100124, China
Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China
School of Software Engineering, Beijing University of Technology, Beijing 100124, China
Author to whom correspondence should be addressed.
Academic Editor: Mustafa Yavuz
Received: 1 April 2017 / Revised: 28 April 2017 / Accepted: 2 May 2017 / Published: 16 June 2017
(This article belongs to the Special Issue MEMS and Nano-Sensors)
View Full-Text   |   Download PDF [3516 KB, uploaded 16 June 2017]   |  


Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall. View Full-Text
Keywords: fall detection; Kalman filter; Bayes network classifier; smart phone; Bluetooth fall detection; Kalman filter; Bayes network classifier; smart phone; Bluetooth

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

He, J.; Bai, S.; Wang, X. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier. Sensors 2017, 17, 1393.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top