A Hierarchical Description-based Video Monitoring System for Elderly

Abstract

The increase in the number of elderly motivates academic researchers to develop technologies that can ensure selfsufficiency in their lives. In this research, prototype of an inexpensive video monitoring system for the elderly using a single RGB camera proposed. In the process is divided into two, namely vision and event recognition module. For event recognition, we use a hierarchical description-based approach with three attributes, namely posture (e.g., stand, sit and lie), location (e.g., walking zone, relaxing zone and toilet zone) and duration (e.g., short and long). Output this system is description activity recognized in the text. The experiment result shows our system can provide the effectiveness of the context description.

Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

“…2, many researchers have attempted to develop contactless monitoring systems for various applications. Previous methods for monitoring people can be divided into 1) vision-based [4][5][6][7][8][9][10][11][12][13], 2) wearable-based [14][15][16][17][18][19][20][21], and 3) head-gaze-based technologies [22][23][24][25][26][27][28][29][30][31]. Vision-based methods usually detect and track the pose or movements of medically vulnerable people using user images captured by cameras.…”

“…References [4][5][6] mainly detected the fall status of a person using a deep learning network or image processing algorithm. In addition, various approaches have attempted to recognize their behaviors (e.g., sitting, standing, and lying) [8,9] or analyze their sleep patterns [11,12]. In contrast, wearable-based systems monitor user conditions using wearable sensors or hand bands to measure the heart rate, blood pressure, and body temperature of users [17][18][19][20][21].…”

“…The captured images were fed to a vision-based deep learning network that was trained to detect the fall status. Next, [8][9][10] monitored user behavior in a care room by capturing users or detecting a bed. In Nari et al [8], a care room was divided into three zones, namely, relaxing, walking, and toilet zones; next, the system identified the location of the elderly and monitored their behaviors (e.g., sitting, standing, and lying).…”

“…Next, [8][9][10] monitored user behavior in a care room by capturing users or detecting a bed. In Nari et al [8], a care room was divided into three zones, namely, relaxing, walking, and toilet zones; next, the system identified the location of the elderly and monitored their behaviors (e.g., sitting, standing, and lying). In addition, [9,10] detected a bed in a care room by capturing RGB images from multiple views and capturing videos using a monocular camera, respectively.…”