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Author:

He, Jian (He, Jian.) | Zhang, Zihao (Zhang, Zihao.) | Wang, Weidong (Wang, Weidong.)

Indexed by:

EI PKU CSCD

Abstract:

According to the requirement of low power consumption and high accuracy of fall detection systems for the elderly in a community, a human body activity model based on tri-axial acceleration and angular velocity is first established. A sensor board integrated with low-power ZigBee and the MPU6050 sensor, which can sample and cache data in sleep mode, is developed. An interrupt-driven algorithm, which can collect and transmit the tri-axial acceleration and the angular velocity data of human activities through ZigBee technology under low power, is designed. Second, the data with regard to the tri-axial acceleration and angular velocity of human activities are received in real time by a server via the sliding window, and the range specification of the data is correspondingly mapped into three channel red-green-blue(RGB) pixels to realize the image representation of the data. Finally, on the basis of analyzing and comparing the differences between activities of daily living(ADLs) and fall images, a fall detection convolutional neural network(FD-CNN)algorithm is designed; the network is trained using ADLs and fall data published on the Internet. The experimental results show that the accuracy of the FD-CNN algorithm is 98.6%, and its sensitivity and specificity are 98.6% and 99.8%, respectively. Compared with the existing fall detection algorithms, the FD-CNN algorithm has significantly superior accuracy, sensitivity, and specificity in terms of fall detection. Meanwhile, the system presented in this study has significant features, such as long transmission distance, easy networking, and lower power consumption, in comparison to the Bluetooth-based fall detection system. Hence, the proposed system is very suitable for fall detection and alarm applications for the elderly in a community. © 2019, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.

Keyword:

Electric power utilization Angular velocity Zigbee Green computing Low power electronics Convolutional neural networks Convolution Finite difference method

Author Community:

  • [ 1 ] [He, Jian]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [He, Jian]Beijing Engineering Research Center for IOT Software and Systems, Beijing; 100124, China
  • [ 3 ] [Zhang, Zihao]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Wang, Weidong]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Wang, Weidong]Beijing Engineering Research Center for IOT Software and Systems, Beijing; 100124, China

Reprint Author's Address:

  • [he, jian]beijing engineering research center for iot software and systems, beijing; 100124, china;;[he, jian]faculty of information technology, beijing university of technology, beijing; 100124, china

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Source :

Journal of Tianjin University Science and Technology

ISSN: 0493-2137

Year: 2019

Issue: 10

Volume: 52

Page: 1045-1054

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

Affiliated Colleges:

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