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Article

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

1
Graduate School of Information Production and Systems, Waseda University, Kitakyushu 808-0135, Japan
2
School of Health Sciences, University of Occupational and Environmental Health, Kitakyushu 807-8555, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5957; https://doi.org/10.3390/s20205957
Received: 14 September 2020 / Revised: 19 October 2020 / Accepted: 20 October 2020 / Published: 21 October 2020
(This article belongs to the Special Issue Low Cost Mid-Infrared Sensor Technologies)
Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application. View Full-Text
Keywords: human motion detection; falling; infrared array sensor; privacy protection; three-dimensional convolutional neural network human motion detection; falling; infrared array sensor; privacy protection; three-dimensional convolutional neural network
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MDPI and ACS Style

Tateno, S.; Meng, F.; Qian, R.; Hachiya, Y. Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor. Sensors 2020, 20, 5957. https://doi.org/10.3390/s20205957

AMA Style

Tateno S, Meng F, Qian R, Hachiya Y. Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor. Sensors. 2020; 20(20):5957. https://doi.org/10.3390/s20205957

Chicago/Turabian Style

Tateno, Shigeyuki, Fanxing Meng, Renzhong Qian, and Yuriko Hachiya. 2020. "Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor" Sensors 20, no. 20: 5957. https://doi.org/10.3390/s20205957

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