105,213 research outputs found
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow
Accurate fall detection for the assistance of older people is crucial to
reduce incidents of deaths or injuries due to falls. Meanwhile, a vision-based
fall detection system has shown some significant results to detect falls.
Still, numerous challenges need to be resolved. The impact of deep learning has
changed the landscape of the vision-based system, such as action recognition.
The deep learning technique has not been successfully implemented in
vision-based fall detection systems due to the requirement of a large amount of
computation power and the requirement of a large amount of sample training
data. This research aims to propose a vision-based fall detection system that
improves the accuracy of fall detection in some complex environments such as
the change of light condition in the room. Also, this research aims to increase
the performance of the pre-processing of video images. The proposed system
consists of the Enhanced Dynamic Optical Flow technique that encodes the
temporal data of optical flow videos by the method of rank pooling, which
thereby improves the processing time of fall detection and improves the
classification accuracy in dynamic lighting conditions. The experimental
results showed that the classification accuracy of the fall detection improved
by around 3% and the processing time by 40 to 50ms. The proposed system
concentrates on decreasing the processing time of fall detection and improving
classification accuracy. Meanwhile, it provides a mechanism for summarizing a
video into a single image by using a dynamic optical flow technique, which
helps to increase the performance of image pre-processing steps.Comment: 16 page
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
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