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

Abstract

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.

Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

“…However, its detection is affected by all variables within the environment, resulting in low performance. The use of TSA in fall detection has emerged in recent years to bridge the gap between performance and user privacy concerns [10], [24], [25], [26]. TSA has also been proposed in other applications including human distance estimation [27], physical distancing [28], [29], occupancy estimation [14], [30], [31], [32], and human activity recognition [33], [34].…”

A Survey of Artificial-Intelligence Enabled Digital Transformation in Elderly Healthcare Field

“…The other kind is non-wearable systems based on external sensing devices. Non-wearable systems are composed of sensors placed around the human proximity for data collecting, the most common sensor used is video cameras [12,13], while some researches applied infrared array [14], radar [15], WIFI [16].…”

Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired From Thermopiles and Machine Learning

“…Singh et al [31] compared the application of various Machine Learning (ML) classifiers for the detection and activity recognition of multiple human subjects using thermopile sensors. Similarly, Tateno et al [32] and Tao et al [33] used deep learning networks for fall detection and activity recognition respectively by utilizing ceiling-mounted sensors. Gochoo et al [34] used a deep learning network to classify 26 separate yoga poses.…”