An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier
AbstractFalls 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
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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.
He J, Bai S, Wang X. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier. Sensors. 2017; 17(6):1393.Chicago/Turabian Style
He, Jian; Bai, Shuang; Wang, Xiaoyi. 2017. "An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier." Sensors 17, no. 6: 1393.
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