Human Fall Research Articles

Human fall detection in surveillance video based on PCANet

Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.

A Mobile Robot for Fall Detection for Elderly-Care

Abstract In 2015, the population of people over the age of 65 is 25.0% in Japan. This means that Japan has already become a super-aging society. In such society, the number of elderly people living alone has been also increased. For such people, a fall accident is serious because it can lead to serious injury or death. Researches and services to monitor behaviours of such people have been proposed. For example, by monitoring the status of use of home appliances, something unusual happened to them can be predicted. However, such systems cannot recognize the detailed behaviours like fall. Surveillance cameras have been introduced only outside the house because of the privacy issues. In this paper, we propose a mobile robot to detect human fall and report it to their observers. The mobile robot consists of a household mobile robot (Yujin Robot's Kobuki), a sensor (Microsoft's Kinect), and a computer (PC) to detect a human and control the robot. For simplicity of the robot and accurate fall detection, the sensor is installed on the robot to follow the target harmoniously. Thus, the sensor can move around with the robot to minimize blind area. The results of our experiments show that improvement of up to 80% in fall detection rate compared to a conventional monitoring technique using position-fixed sensors. Finally, we discuss the capabilities and future works of the robot.

A smart safety cane for human fall detection

Lots of countries have gradually become an aging society, therefore, the safety issues of the elderly attract more and more attentions. Falling is one of the major reasons for the accidental injuries of the elderly, and many times such accidents have become irreparable regrets because of the delayed arrival of the ambulances. In this study, we propose to attach to the cane with intelligent embedded system with acceleration detection ability. Such system is able to collect the 3D acceleration data of the cane when it is used by the elderly in real time. We analyse the data with finite state machine in order to detect the fall accidents of the elderly. The smart cane is able to send alarm and location of the elderly to the hospital as soon as such accidents are detected, so that the ambulance can be dispatched immediately after the accident happened.

Comparison of Machine Learning Methods for the Purpose Of Human Fall Detection

Abstract According to several studies, the European population is rapidly aging far over last years. It is therefore important to ensure that aging population is able to live independently without the support of working-age population. In accordance with the studies, fall is the most dangerous and frequent accident in the everyday life of aging population. In our paper, we present a system to track the human fall by a visual detection, i.e. using no wearable equipment. For this purpose, we used a Kinect sensor, which provides the human body position in the Cartesian coordinates. It is possible to directly capture a human body because the Kinect sensor has a depth and also an infrared camera. The first step in our research was to detect postures and classify the fall accident. We experimented and compared the selected machine learning methods including Naive Bayes, decision trees and SVM method to compare the performance in recognizing the human postures (standing, sitting and lying). The highest classification accuracy of over 93.3% was achieved by the decision tree method.

Depth-based human fall detection via shape features and improved extreme learning machine.

Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras.

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.

Testing of medicinal products produced from pooled plasma

Medicinal products produced from human plasma fall under the administrative batch release procedure of the competent authority. In Germany, this has been carried out since 1995 by the Paul Ehrlich Institute (PEI), the responsible state control agency for blood products. Medicinal products released for the European and national market are tested for quality, efficacy and safety. Experimental testing of the final product and the starting materials, the plasma pools, as well as control of the production documentation guarantee a constantly high product safety. In the 28,000 batches tested since the beginning of the state controlled batch release testing of these blood products at the PEI, there has been no transmission of infectious viruses (HIV, HBV and HCV) to any patient. The batch release has made a contribution to the improvement of product quality. This procedure is still an important tool to ensure safety of blood products. The PEI is integrated in the batch release network of the European Directorate for the Quality of Medicines & Health Care (EDQM) in Strasbourg. Regulations and guidelines for official control authority batch release (OCABR) ensure harmonized procedures for mutual recognition of batch release on the European level. The EU certificates and German national certificates are requested and accepted in over 70 countries worldwide. Experimental testing in the EU and the requisite certificates have developed into a seal of quality for the world market.

Dinah Morris as Second Eve: The Fall and Redemption in Adam Bede

EVEN THOUGH GEORGE ELIOT was not a practicing Christian an adult, her experience growing up in a low church Anglican family, including her familiarity with scripture, had a significant impact on her writings. (1) This facet of Eliot's artistic vision is particularly evident in her first novel, Adam Bede. In this novel, Eliot draws extensively upon the theological motifs of Genesis, so to explore questions surrounding the Fall of humanity and whether humans have any reason to hope for redemption. As the plot of Adam Bede progresses, Eliot develops two narrative trajectories. The first of these (Dinah Morris's love story with Adam) follows the basic contours of a Victorian romance, and basically reaffirms the patriarchal and hierarchical order of the postlapsarian, or fallen, world. The second trajectory (Dinah's mediation of divine love to the tragic figure of Hetty Sorrel), though, involves a kind of apocalyptic irruption, which in subtle ways subverts the neat and tidy character of the novel's conclusion. Before getting to an analysis of how these two levels of the novel relate to each other, it will first prove helpful to lay some initial groundwork. The first section of the paper, therefore, will look briefly at the novel's use of typology, specifically in its appropriation and development of certain biblical ideas. After this piece is in place, the subsequent section will examine the significance of the names given to some of the main characters in Adam Bede, so to specify how the scriptural background to these names illumines the characters' roles in the novel. The third section of the article will consider how the work employs the resources mentioned above (i.e., typology and biblical motifs) to offer a unique interpretation of the Fall and its consequences. Finally, the conclusion will demonstrate how Dinah Morris's role in the novel a Second Eve operates an apocalyptic in-breaking amidst the suffering of Hayslope's citizens, and such signals reason for hope in a generally bleak world. First Adam, Second Eve: Eliot's Imagination To understand the significance of the biblical material in Adam Bede one must first have a firm grasp on how Eliot employs typology a literary technique. In an important study on this topic, Jo Ellen Parker argues that Eliot cultivated a typological imagination, in large part, through her encounter with Evangelical religion. (2) As she builds her case for the centrality of typology in Eliot's early novels, Parker offers a helpful explanation of typological interpretation. In her words, Typological interpretation subverts the common-sense perception of the linear nature of time, for it perceives two or more widely separate historical moments bound into one timeless instant of prophecy and fulfillment in the mind of God....It perceives all things existing simultaneously in two realms, the material and the spiritual. (3) Throughout Christian history this mode of interpretation has held a prominent position, but especially so during the Patristic era. (4) Within the tradition of Christian exegesis of scripture, biblical interpreters utilized typology a way of demonstrating the correspondence between the Old and New Covenants, and also to establish deeper levels of meaning in the events of salvation history. Some of the most famous instances of this method of interpretation can be found in the Pauline corpus--as for instance, when Paul describes Christ a Second Adam (I Cor 15:20-22) or when he compares the Israelites' passage through the Red Sea to Christian baptism (I Cor 10:1-5). (5) Although typological exegesis flagged somewhat during the Middle Ages, it returned to prominence during the post-Reformation era, as exegesis of the Old Testament [became for Protestants] an issue necessary to daily reading of Scripture. (6) By Eliot's time, typological exegesis was one of the foremost interpretive modes utilized by English Protestants, both low and high church, the research of George Landow has effectively demonstrated. …

The MobiFall Dataset

Fall detection is receiving significant attention in the field of preventive medicine, wellness management and assisted living, especially for the elderly. As a result, several fall detection systems are reported in the research literature or exist as commercial systems. Most of them use accelerometers and/ or gyroscopes attached on a person's body as the primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency when necessary. Nevertheless, automatic fall detection continues to present significant challenges, with the recognition of the type of fall being the most critical. The aim of this work is to introduce a human fall and activity dataset to be used in testing new detection methods, as well as performing objective comparisons between different reported algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different types of falls and nine different activities of daily living. Utilizing this dataset, the results of an elaborate evaluation of machine learning-based fall detection and fall classification are presented and discussed in detail.

Exploration and Comparison of the Pre-impact Lead Time of Active and Passive Falls Based on Inertial Sensors

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ± 112 ms and it is smaller than the value for passive falls, which is 731 ± 104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early pre-impact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.

Integrating the enriched feature with machine learning algorithms for human movement and fall detection

Movement detection is gaining more and more attention among various pattern recognition problems. Recognizing human movement activity types is extremely useful for fall detection for elderly people...

Associative Classification based Human Activity Recognition and Fall Detection using Accelerometer

Human fall poses serious health risks especially among aged people. The rate of growth of elderly population to the total population is increasing every year. Besides causing injuries, fall may even lead to death if not attended immediately. This demands continuous monitoring of human movements and classifying normal low-level activities from abnormal event like fall. Most of the existing fall detection methods employ traditional classifiers such as decision trees, Bayesian Networks, Support Vector Machine etc. These classifiers may miss to cover certain hidden and interesting patterns in the data and thus suffer high false positives rates. Hence, there is a need for a classifier that considers the association between patterns while classifying the input instance. This paper presents a pattern mining based classification algorithm called Frequent Bit Pattern based Associative Classification (FBPAC) that distinguishes low-level human activities from fall. The proposed system utilizes single tri-axial accelerometer for capturing motion data. Empirical studies are conducted by collecting real data from tri-axial accelerometer. Experimental results show that within a time-sensitive sliding window of 10 seconds, the proposed algorithm achieves 99% accuracy for independent activity and 92% overall accuracy for activity sequence. The algorithm gives reasonable accuracy when tested in real time.

HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

Falls in the elderly have always been a serious medical and social problem. To detect and predict falls, a hidden Markov model (HMM)-based method using tri-axial accelerations of human body is proposed. A wearable motion detection device using tri-axial accelerometer is designed and realized, which can detect and predict falls based on tri-axial acceleration of human upper trunk. The acceleration time series (ATS) extracted from human motion processes are used to describe human motion features, and the ATS extracted from human fall courses but before the collision are used to train HMM so as to build a random process mathematical model. Thus, the outputs of HMM, which express the marching degrees of input ATS and HMM, can be used to evaluate the risks to fall. The experiment results show that fall events can be predicted 200–400 ms ahead the occurrence of collisions, and distinguished from other daily life activities with an accuracy of 100%.

A fuzzy model for human fall detection in infrared video

Fall detection, especially for elderly people, is a challenging problem which demands new products and technologies. In this paper a fuzzy model for fall detection and inactivity monitoring in infr...

Frequent Bit Pattern Mining Over Tri-axial Accelerometer Data Streams for Recognizing Human Activities and Detecting Fall

Human fall causes injuries and may even lead to death in the case of older age. Due to increasing elderly population every year to the total population and the health problems and risks caused by fall especially among the age group of 60 and above, detecting fall at the earliest is essential in order to avoid human loss. Basically, fall detection is considered as a classification problem which requires developing a classifier model that recognizes and classifies normal human activities and abnormal activity like fall. Most of the existing fall detection methods are based on classifiers constructed using traditional methods such as decision trees, Bayesian Networks, Support Vector Machine etc. These classifiers may miss to cover certain hidden and interesting patterns in the data and thus suffer high false positives rates. This paper presents a novel algorithm called Frequent Bit Pattern based Associative Classification (FBPAC) that maps the tri-axial accelerometer data streams to bit patterns and mines the frequent bit pattern occurring for normal activities like sitting/standing, lying and walking within a time-sensitive sliding window. Unlike normal activities, fall have significant peak acceleration and it is detected by setting most significant bit of bit pattern and thus clearly distinguishes fall from lying activity, thereby reducing false positive rates. Empirical studies are conducted by collecting real time tri-axial accelerometer data from a wearable and unobtrusive sensing device. Experimental results show that within a time-sensitive sliding window of 10seconds, the proposed algorithm achieves up to 92% overall accuracy.

Lateral Inhibition in Accumulative Computation and Fuzzy Sets for Human Fall Pattern Recognition in Colour and Infrared Imagery

Fall detection is an emergent problem in pattern recognition. In this paper, a novel approach which enables to identify a type of a fall and reconstruct its characteristics is presented. The features detected include the position previous to a fall, the direction and velocity of a fall, and the postfall inactivity. Video sequences containing a possible fall are analysed image by image using the lateral inhibition in accumulative computation method. With this aim, the region of interest of human figures is examined in each image, and geometrical and kinematic characteristics for the sequence are calculated. The approach is valid in colour and in infrared video.

A fuzzy model for human fall detection in infrared video

Fall detection, especially for elderly people, is a challenging problem which demands new products and technologies. In this paper a fuzzy model for fall detection and inactivity monitoring in infrared video is presented. The classification features proposed include geometric and kinematic parameters associated with more or less sudden changes in the tracked human-related regions of interest. A complete segmentation and tracking algorithm for infrared video as well as a fuzzy fall detection and confirmation algorithm are introduced. The proposed system is capable of identifying true and false falls, enhanced with inactivity monitoring aimed at confirming the need for medical assistance and/or care. The fall indicators used as well as their fuzzy model is explained in detail. The fuzzy model has been tested for a wide number of static and dynamic falls, demonstrating exciting initial results.

Implementation of Fall Detection and Localized Caring System

We propose the fall detection and localized caring system to effectively detect the postures of a human and provide a service of remote connection with health care center. Especially, when elderly people fall down, they often need to be hospitalized. The system can inform the care center automatically through the wireless sensor network and send the rapid information of the incident for their family through the mobile phone. It also provides the location of incident for immediate rescue by dispatching medical staff from the center. The experimental results show that our system achieves the accurate rate of 99.9% for detecting a human fall and provides the care services effectively.

Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area

Population of old generation is growing in most countries. Many of these seniors are living alone at home. Falling is among the most dangerous events that often happen and may need immediate medical care. Automatic fall detection systems could help old people and patients to live independently. Vision-based systems have advantage over wearable devices. These visual systems extract some features from video sequences and classify fall and normal activities. These features usually depend on camera's view direction. Using several cameras to solve this problem increases the complexity of the final system. In this paper, we propose to use variations in silhouette area that are obtained from only one camera. We use a simple background separation method to find the silhouette. We show that the proposed feature is view invariant. Extracted feature is fed into a support vector machine for classification. Simulation of the proposed method using a publicly available dataset shows promising results.

Epistemic Addiction: Reading “Sonny's Blues” with Levinas, Kierkegaard, and Nietzsche

journal of speculative philosophy, vol. 26, no. 3, 2012 Copyright © 2012 The Pennsylvania State University, University Park, PA James Baldwin writes that “I underwent, during the summer that I became fourteen, a prolonged religious crisis.” Baldwin dealt with this crisis in his literary works through the strategic use of Christian theological themes and imagery to make compelling critiques of bourgeois cultural and Christian values. So “prolonged” was his “religious crisis” that Christian theological themes permeate Baldwin’s immense literary corpus. Baldwin thus both was influenced by Christianity and critiqued it in his works. Considering these two points, I think it appropriate to begin this essay with a biblical reference to the Christian narrative of the “fall” of humanity. I choose this biblical starting point not only because of Baldwin’s Christian background and literary inclinations but also because I think that this starting point accurately depicts the moral problem that is at work in Baldwin’s short story “Sonny’s Blues.” I shall call this moral problem “epistemic addiction.” Epistemic Addiction: Reading “Sonny’s Blues” with Levinas, Kierkegaard, and Nietzsche