Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone. In recent years, deep convolutional nets are widely used in human action analysis, based on which a number of fall detection methods have been proposed. Despite their highly effective performances, the behaviors of how the convolutional nets recognize falls are still not clear. In this paper, instead of proposing a novel approach, we perform a systematical empirical study, attempting to investigate the underlying fall recognition process. We propose four tasks to investigate, which involve five types of input modalities, seven net instances and different training samples. The obtained quantitative and qualitative results reveal the patterns that the nets tend to learn, and several factors that can heavily influence the performances on fall recognition. We expect that our conclusions are favorable to proposing better deep learning solutions to fall detection systems.
Systematic reviews of fall recognition and detection systems can be found in @cite_43 and @cite_2 , which cover solutions based on diverse types of sensors. For vision-based methods, a typical processing pipeline consists of background subtraction, feature extraction and classification, as presented in @cite_23 @cite_41 and others. Each step in this pipeline is normally hand-crafted, separately considered and implemented based on certain heuristic rules. A frequently considered rule is that the background information is redundant for fall detection. Thus, background subtraction is performed by algorithms like training Gaussian mixture models, subspace clustering or other sophisticated approaches @cite_21 . Another heuristic rule is that the body shape is a pronounced feature of falling. Consequently, the silhouette of the human body @cite_23 @cite_24 , or the shape of the foreground bounding box @cite_41 @cite_28 , is extracted and analyzed. Nevertheless, heuristics are not always precise and comprehensive. The studies of @cite_34 and @cite_4 present effective fall detection solutions when considering the ground plane, indicating that the background information can be very useful.
@cite_4 @cite_41 @cite_28 @cite_21 @cite_24 @cite_43 @cite_23 @cite_2 @cite_34
2738996446 1930442888 2131889717 2155326828 2096300942 2076068958 2100716893
Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20 of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper we present a new , non-invasive system for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. Furthermore, our system consists of a reasoning module which formulates a number of measures to reason whether a person is fallen. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification. To reproduce our results, the implementation is publicly available to the scientific community. Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision. A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind. and dynamic environments. High-level knowledge was fused with low-level feature-based classification to handle a time-varying background, and a decision process based on a fuzzy logic inference system was used to detach the moving objects from the human silhouette. One central task in silhouette extraction is background modeling. Once a background model is established, those image regions with significantly different characteristics from the background are considered foreground objects. In (5), a Least Median of Squares method was used to construct a background model, and in (6), a differencing function was proposed to extract moving human silhouettes. (7) demonstrated a non-parametric kernel density estimation method to model and subtract the human from a background. The adaptive background updating handles small motion in the background scene, such as moving tree branches. A slightly different approach outlined in (8) is based on processing in the YUV color space, and is capable of detecting shadows and extracting 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. Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0 error with a multi-camera setup) compared with other common image processing methods. Fall detection is a major challenge in the public health care domain, especially for the elderly, and reliable surveillance is a necessity to mitigate the effects of falls. The technology and products related to fall detection have always been in high demand within the security and the health-care industries. An effective fall detection system is required to provide urgent support and to significantly reduce the medical care costs associated with falls. In this paper, we give a comprehensive survey of different systems for fall detection and their underlying algorithms. Fall detection approaches are divided into three main categories: wearable device based, ambience device based and vision based. These approaches are summarised and compared with each other and a conclusion is derived with some discussions on possible future work. A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.