Recognition of Human Continuous Action with 3D CNN

Yu, Gang; Li, Ting

doi:10.1007/978-3-319-68345-4_28

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…In this structure, each feature map in the convolutional layer is related to multiple adjacent consecutive frames in the previous layer, which is how it can capture motion information. The value of a certain position of a convolution map is obtained by convolving the local receptive field of the same position of several consecutive frames in the previous layer [ 55 ].…”

Section: Methodsmentioning

confidence: 99%

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

Tateno

Meng

Qian

et al. 2020

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%