Vision-Assisted Human Motion Analysis for Bed Exit Prediction Model Construction

Chen, Tianxiang; Hsiao, Rong-Shue; Kao, Chun-Hao; Lin, Hsin Piao; Jeng, Shiann-Shiun; Lin, Ding‐Bing

doi:10.1109/icice.2017.8479094

Cited by 3 publications

(1 citation statement)

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“…Image-based techniques are typically combined with deep learning model to detect a target object after a data training process. The common source of images can be video sequences [ 9 , 10 ], IR thermal images [ 11 ], and integrated images from RGB and IR cameras [ 12 , 13 ]. Compared with sensor-based techniques, image-based techniques can better preserve the spatial information for object detection and have the potential to achieve multi-object detection.…”

Section: Introductionmentioning

confidence: 99%

Bed-Exit Behavior Recognition for Real-Time Images within Limited Range

Lin

Wei

Liu

et al. 2022

Sensors

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In the context of behavior recognition, the emerging bed-exit monitoring system demands a rapid deployment in the ward to support mobility and personalization. Mobility means the system can be installed and removed as required without construction; personalization indicates human body tracking is limited to the bed region so that only the target is monitored. To satisfy the above-mentioned requirements, the behavior recognition system aims to: (1) operate in a small-size device, typically an embedded system; (2) process a series of images with narrow fields of view (NFV) to detect bed-related behaviors. In general, wide-range images are preferred to obtain a good recognition performance for diverse behaviors, while NFV images are used with abrupt activities and therefore fit single-purpose applications. This paper develops an NFV-based behavior recognition system with low complexity to realize a bed-exit monitoring application on embedded systems. To achieve effectiveness and low complexity, a queueing-based behavior classification is proposed to keep memories of object tracking information and a specific behavior can be identified from continuous object movement. The experimental results show that the developed system can recognize three bed behaviors, namely off bed, on bed and return, for NFV images with accuracy rates of 95~100%.

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