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Sensors 2017, 17(6), 1393; doi:10.3390/s17061393

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

1,2,3
,
3
and
1,2,3,*
1
Beijing Advanced Innovation Center for Future Internet Technology, Beijing 100124, China
2
Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China
3
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]   |  

Abstract

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
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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.

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