Abstract
Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.
Highlights
Individuals represent the fastest growing segment of the population worldwide [1]
The window size of 64 × 128, block size of 16 × 16, block stride of 8 × 8 and a cell size of 8 × 8 [72] are set as the parameters of histogram of oriented gradient (HOG) features
This study focuses on the effect of camera height on fall-detection methods, which has not been extensively studied
Summary
Individuals (over 65 years old) represent the fastest growing segment of the population worldwide [1]. The population of elderly individuals over 80 years old was 126.5 million in 2015 and is expected to be more than triple that by 2050, increasing to 446.6 million [1]. According to the World Health Organization, 28%–35% of elderly individuals have an accident involving a fall each year [4]. Accidents involving falls, which are the second leading cause of unintentional death, are considered one of the most hazardous incidents for the elderly, with over 424,000 deaths occurring in 2008 [4]. If a solitary elderly individual falls, he or she may be lying on the floor for a long time without any help. A fall-detection method that can automatically detect a fall in real time and send alerts to certain caregivers (such as family members, hospitals or health centers [7]) is important for solitary elderly individuals as well as playing an important part in the health care system for the elderly [8]
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