This paper deals with the problem of detecting fallen people lying on the floor by means of a mobile robot equipped with a 3D depth sensor. In the proposed algorithm, inspired by semantic segmentation techniques, the 3D scene is over-segmented into small patches. Fallen people are then detected by means of two SVM classifiers: the first one labels each patch, while the second one captures the spatial relations between them. This novel approach showed to be robust and fast. Indeed, thanks to the use of small patches, fallen people in real cluttered scenes with objects side by side are correctly detected. Moreover, the algorithm can be executed on a mobile robot fitted with a standard laptop making it possible to exploit the 2D environmental map built by the robot and the multiple points of view obtained during the robot navigation. Additionally, this algorithm is robust to illumination changes since it does not rely on RGB data but on depth data. All the methods have been thoroughly validated on the IASLAB-RGBD Fallen Person Dataset, which is published online as a further contribution. It consists of several static and dynamic sequences with 15 different people and 2 different environments.
There exist also more specific approaches addressing the detection of falls. These comprehend wearable devices, whose great popularity is linked to the spread of open-source platforms which are small, powerful and connectible to low-cost sensors @cite_18 . In most cases, such sensors include accelerometers @cite_12 @cite_32 @cite_10 . These technologies suffer from the difficulty of correctly distinguishing falls from common actions like sitting or lying down. Furthermore, the elderlies easily forget to wear them. Other approaches specifically addressing falls need the installation of environmental devices like microphones @cite_0 , cameras for person tracking @cite_2 @cite_21 @cite_25 , infrared or vibration sensors @cite_24 . Anyway, these approaches are less effective and, being invasive, less accepted.
@cite_18 @cite_21 @cite_32 @cite_0 @cite_24 @cite_2 @cite_10 @cite_25 @cite_12
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As we grow old, our desire for independence does not diminish; yet our health increasingly needs to be monitored. Injuries such as falling can be a serious problem for the elderly. If a person falls and is not able to get assistance within an hour, casualties arising from that fall can result in fatalities as early as 6 months later [1]. It would seem then that a choice between safety and independence must be made. Fortunately, as health care technology advances, simple devices can be made to detect or even predict falls in the elderly, which could easily save lives without too much intrusion on their independence. Much research has been done on the topic of fall detection and fall prediction. Some have attempted to detect falls using a variety of sensors such as: cameras, accelerometers, gyroscopes, microphones, or a combination of the like. This paper is aimed at reporting which existing methods have been found effective by others, as well as documenting the findings of our own experiments. The combination of which will assist in the progression towards a safe, unobtrusive monitoring system for independent seniors. One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them. Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls. This paper presents new approach for unobtrusive indoor fall detection by an IR thermal array sensor. Unlike existing methods that run fall detection at server and require high communication and processing rates, we perform fall detection within the sensor node by a computationally inexpensive algorithm that signals the server only when a fall occurs. Experiments with prototype design show that such formulation provides robust and real-time fall detection even in a noisy environment.