Human Fall Research Articles

A Three-Stage Low-Complexity Human Fall Detection Method Using IR-UWB Radar

This paper proposes a novel three-stage low-complexity human fall detection method using an impulse radio ultra-wideband (IR-UWB) radar. The core idea lies in the three cascaded stages, namely large-motion detection, rough fall detection and enhanced fall detection. For the large-motion detection, we assume the fall is a very sparse event in daily life and achieve this by a checking of the high Doppler frequency energy. For the rough fall detection, we do not intuitively determine the fall events, but propose six time-frequency features and two position features, and use the support vector data description (SVDD) detector to divide the large-motions into non-fall and fall-like events. For the enhanced fall detection, we add a new feature and use a Mahalanobis distance classifier to finally determine whether a fall happened. The reasons that two classifiers cascaded instead of one classifier are that (1) we can reduce the difficulty of identifying falls directly from daily events by using a large number of non-fall samples to train the SVDD model for anomaly detection, and allowing a certain false alarm rate; and (2) we can achieve a higher fall detection accuracy in a much smaller searching space by identifying falls only from the fall-like events. Additionally, a real-time edge fall detection system with a commonly used micro control unit is developed. Experiment results show that the proposed method exhibits a low computational complexity, and a relative robustness and high fall detection accuracy under a low false alarm rate.

Automatic Recognition Method of Fall Movement of Sports Fitness Human Body Based on Posture Data Sequence

In order to improve the recognition accuracy of human falling actions, the impact of randomness of actions is reduced. To this end, this paper proposes an automatic recognition method for physical fitness human fall based on pose data sequence. The color camera is used to collect the fall motion images of the physical fitness personnel, and the motion image preprocessing is completed by extracting the fall motion features of the human body, tracking and adjusting the fall motion of the human body. A model of human body fall movement displacement feature extraction from posture data sequence is constructed. By tracking the displacement feature points, the automatic recognition of physical fitness human body fall movement based on posture data sequence is realized. The experimental results confirm that the proposed method can effectively obtain the details of the fall motion images of physical fitness. When the number of human falling actions reaches 500, the accuracy of action recognition is also as high as 77%, improving the recognition effect of human fall action.

Fall Detection Method for Embedded Devices

The challenges of the aging population is becoming more and more prominent worldwide. Among them, in the face of the elderly fall phenomenon, human fall detection technology research and development has practical application value. Because of a large number of network parameters in the field of fall detection and the limited computing power of embedded devices, which makes it difficult to run on the embedded platform, this paper proposes an OT-YOLOV3 (OpenCV+Tiny-YOLOV3) fall detection method. In this method, Gaussian processing and other operations are used to preprocess the fallen image to avoid the influence of the angle change of the image on the recognition result. Then, the feature extraction network in Tiny-YOLOV3 was replaced by the MobileNet network to increase the number of network layers and reduce the number of parameters and calculations in the model. At the same time, the multi-scale prediction method was used to improve detection accuracy. Experimental results show that the accuracy of the proposed model is 10% higher than that of the YOLOV3 (You Only Look Once Version three) model, 4% higher than that of the Tiny-YOLOV3 model, 3% higher than that of the YOLOV3 model, 3% higher than that of Tiny-YOLOV3 model, and the model size is only 45% of that of YOLOV3 model and 65% of Tiny-YOLOV3. Compared with YOLOV3 and Tiny-YOLOV3 processing methods, the drop recognition effect is significantly improved and the model memory is reduced, which meets the requirements of real-time and efficient detection for embedded devices.

Application of Accelerometer to Monitor Students' Exercise Load in 50 m Round Trip.

With the further advancement of microelectronics innovation and sensors, sensors can be broadly implanted in cell phone gadgets, compact gadgets, and so forth. The utilization of speed increase sensors for human running checking has expansive application possibilities. From one perspective, the everyday development of the human body is firmly connected with the physical and emotional wellness of the person. Observing the day-to-day developments of the human body is of incredible importance in planning a logical running activity plan and working on actual wellbeing. On the other hand, it is also of practical value to monitor human abnormal movements. This kind of abnormal movement caused by accidental falls can bring certain harm to the human body. Real-time monitoring of the fall can provide timely assistance to the person and reduce the risk brought by the fall. This article analyzes and summarizes the research theories and common research methods in the field of 50 m round-trip movement monitoring based on the acceleration sensor. According to the process of 50 m round-trip movement pattern recognition, the data collection, preprocessing, feature extraction, and selection of 50 m round-trip movement are evaluated. The classification and recognition of each module were analyzed. This article proposes a human body motion recognition mechanism based on acceleration sensors by looking at the three trademark upsides, the wavefront edge, wavefront limit, and time stretch between the pinnacle and valley of the speed increase sensor vertical information waveform, and joining the rule of choice tree order to accomplish the activities of hunching down, taking off, and running. To get an accurate recognizable proof and recognize ways of behaving, a human fall identification calculation is proposed. This calculation removes human movement attributes throughout the fall and focuses on four sorts of falls: forward fall, reverse fall, left fall, and right fall by utilizing the connection of the three tomahawks of the speed increase sensor. The trial results show that the normal right acknowledgment pace of the human body's 50 m full-circle running way of behaviour is more than 90%, which has specific useful application esteem.

A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People

A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People

Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation.

Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands.

It's About Eternal Life After All: Revisiting Catholic Beliefs by Edward Jeremy Miller

Reviewed by: It's About Eternal Life After All: Revisiting Catholic Beliefs by Edward Jeremy Miller Christopher Cimorelli It's About Eternal Life After All: Revisiting Catholic Beliefs BY EDWARD JEREMY MILLER Hobe Sound, FL: Divina Press, an Imprint of Lectio Publishing, LLC, 2021. xxxi + 263 pages. Paperback: $22. ISBN: 9781943901982. Many NSJ readers will be familiar with the life and work of Edward Jeremy Miller, long a contributor to the field of Newman studies.1 His works John Henry Newman on the Idea of the Church (1987)2 and the edited volume, Conscience and the Path to Holiness: Walking with Newman (2014),3 stand out in this regard and across a career spanning more than forty years. It's About Eternal Life After All: Revisiting Catholic Beliefs departs from these earlier academic works given that it contains Miller's memoirs and reflections on Catholic beliefs, as indicated by the subtitle.4 It would not be accurate to describe Eternal Life as a book about John Henry Newman, but Newman is nevertheless present throughout—explicitly mentioned or otherwise—given his influence on the author. Miller establishes two major facets of this influence at the outset, which include the following Newmanian distinctions: that is, the distinction between the visible and invisible world, on the one hand, and the distinction between notional and real assent, on the other.5 He ultimately aims to bolster the possibility of real assent in the Catholic faithful, so that many believers can come to the same realization made possible through Miller's own labor and contemplation, namely: that the invisible world is not some remote location, but rather is manifest here and now; the God who is love truly indwells the believer, drawing one further into the divine life that will be fully realized upon death. The concrete approach to support this aim includes prefatory material that contains an overview of Miller's life story, eight chapters, and six short appendices. The first chapter treats the way that the Bible works from a Catholic perspective, which sets the stage for a reconsideration of creation and the fall of humanity (ch. 2). Here, Miller reflects on, among other things, limitations stemming from the Council of Trent's literal understanding of Adam and Eve's fall from paradise. Chapters three and four treat the understanding of God and the rich subject of [End Page 94] prayer; the aforementioned idea of God's indwelling receives its form in these pages and is carried forward throughout the rest of the book. Chapters five and six explore the life, death, and resurrection of Jesus the Christ, as well as other features of resurrection belief—including thought-provoking considerations of traditional Catholic teachings like purgatory, hell, and the "timing" of the resurrection. Miller here makes the case that full resurrection life is enjoyed immediately upon death and is not something reserved for the end of time, which would lead to a view of disembodied—and thus not-quite-human—souls lurking about. These two chapters are rather substantial, which is apropos given their main topics and centrality to the Christian faith in general. Chapter seven considers the view of eternal life previously established, but in the concrete and imperfect setting of the church, which is further explored via its sacraments and controversial social issues. Chapter eight considers the relationship between faith and science, two areas that Miller's own educational background has connected in the pursuit of truth, as well as few final, but ancillary topics. In authoring these memoirs, Miller intended to not only give expression to his developing view of Catholic life and beliefs, but to do so in a simplified manner that could benefit Catholic laity—the primary audience of the text—who are invested in their faith and the quest to understand it more deeply, but who may not have had any formal theological education. Miller is largely successful in "translating" esoteric and dense theological topics into more relatable language and helpful analogies. Nevertheless, there are passages where even an invested layperson will need to sit with subsections treating complex theological content. An example includes the valuable treatment of the doctrine of Original Sin when reading the book...

DeFall: Environment-Independent Passive Fall Detection Using WiFi

Fall is recognized as one of the most frequent accidents among elderly people. Many solutions, either wearable or noncontact, have been proposed for fall detection (FD) recently. Among them, WiFi-based noncontact approaches are gaining popularity due to the ubiquity and noninvasiveness. The existing works, however, usually rely on labor-intensive and time-consuming training before it can achieve a reasonable performance. In addition, the trained models often contain environment-specific information and, thus, cannot be generalized well for new environments. In this article, we propose DeFall, a WiFi-based passive FD system that is independent of the environment and free of prior training in new environments. Unlike previous works, our key insight is to probe the physiological features inherently associated with human falls, i.e., the distinctive patterns of speed and acceleration during a fall. DeFall consists of an offline template-generating stage and an online decision-making stage, both taking the speed estimates as input. In the offline stage, augmented dynamic time-warping (DTW) algorithms are performed to generate a representative template of the speed and acceleration patterns for a typical human fall. In the online phase, we compare the patterns of the real-time speed/acceleration estimates against the template to detect falls. To evaluate the performance of DeFall, we built a prototype using commercial WiFi devices and conducted experiments under different settings. The results demonstrate that DeFall achieves a detection rate above 95% with a false alarm rate lower than 1.50% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios with one single pair of transceivers. Extensive comparison study verifies that DeFall can be generalized well to new environments without any new training.

5G Virtual Reality Internet of Things - Research on Human Fall Behavior Recognition and Prediction Based on Wearable Inertial Sensors

<p style="text-align: justify;"><span style="font-size:16px;font-family: 'Times New Roman','serif'">The 19th National Congress of the Communist Party of China put forward the strategy of rural revitalization, demanding that the development of agriculture and rural areas should be given priority, and rural revitalization should be promoted in an all-round way. This has brought new opportunities and hopes for rural development. All parts of Anyang City are also actively implementing the policy of the rural revitalization strategy, speeding up the industrialization of agriculture, adjusting the rural industrial structure, and constantly exploring the industrial model to revitalize the rural economy. The tourism industry in Anyang has developed rapidly in recent years and has become a strong driving force for regional economic development. In the context of the rural revitalization strategy, the coordinated development of the coupling of agriculture and tourism has pointed out the direction for the revitalization of the rural economy.</span>

Virtual reality in training artificial intelligence-based systems: a case study of fall detection

Artificial Intelligent (AI) systems generally require training data of sufficient quantity and appropriate quality to perform efficiently. However, in many areas, such training data is simply not available or incredibly difficult to acquire. The recent developments in Virtual Reality (VR) have opened a new door for addressing this issue. This paper demonstrates the use of VR for generating training data for AI systems through a case study of human fall detection. Fall detection is a challenging problem in the public healthcare domain. Despite significant efforts devoted to introducing reliable and effective fall detection algorithms and enormous devices developed in the literature, minimal success has been achieved. The lack of recorded fall data and the data quality have been identified as major obstacles. To address this issue, this paper proposes an innovative approach to remove the afformentioned obstacle using VR technology. In this approach, a framework is, first, proposed to generate human fall data in virtual environments. The generated fall data is then tested with state-of-the-art visual-based fall detection algorithms to gauge its effectiveness. The results have indicated that the virtual human fall data generated using the proposed framework have sufficient quality to improve fall detection algorithms. Although the approach is proposed and verified in the context of human fall detection, it is applicable to other computer vision problems in different contexts, including human motion detection/recognition and self-driving vehicles.

Human fall detection using neuro-fuzzy models based on ensemble learning

Human fall detection using neuro-fuzzy models based on ensemble learning

Research on Human Falling Recognition Based on Inertial Sensors

This article aims to recognize human fall behavior based on wearable inertial sensors. The experiment in this paper mainly adopts data fusion algorithm, which can extract various features that can represent activities in time domain, frequency domain and time-frequency domain from the original data of human motion to effectively distinguish activities. In addition to considering the validity of the data, we also need to consider the way the data relates to the real situation and the comfort of the user in real life. Experimental data shows that in the data collection process, in order to obtain datasets that are easy to calculate, accurate and effective, two aspects need to be considered: the location of the data collection device and the frequency of data collection. Experimental results show that feature extraction has a great influence on the accuracy of activity recognition. Using 6 features of the elderly specifically selected for activity recognition, the original sensor data is directly trained through LSTM-RNN, and the accuracy of activity recognition can reach 92.28%.

Human fall detection and activity monitoring: a comparative analysis of vision-based methods for classification and detection techniques

Human fall detection and activity monitoring: a comparative analysis of vision-based methods for classification and detection techniques

IoT-Based Human Fall Detection System

Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people’s homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.

Consequences of Practical Polytheism in Human Deviation in the Qur'an and Nahj Al-Balaghah

Shirk (polytheism) literally means the combination of two properties and two shares. Practical polytheism means associating (a) partner(s) with God along side Him, and it is overt (Jali) and covert (Khafi) polytheism. Covert polytheism appears in the stage of worship and obedience to God such as Obedience to the mundane wish and desires (Havaye Nafs), obedience to the devil, following the tyrants; Worldliness, selfishness, pursuit of certain habits and tendencies common among some believers for example, friendship with the deniers of God, Prophets or infallible Imams (as); Humility and submission to non-God; Being a slave and captive of others ( being controlled by others ) ; Honoring the rich and the oppressors; Also hypocrisy and Sum’ah... . The present study entitled "Consequences of practical polytheism in human fall" with the aim of enlightenment in this field; in order to protect faith from the influence of superstitions and false heresies, has been carried out in narrative and descriptive methods. It is a library research which made references to the Holy Book of the Qur'an, Nahj al-Balaghah, some commentary books of Quran, theological, moral, and lexical books. Practical polytheism has consequences such as actions being void (Habt ‘Amal); reduction of sustenance; anxiety and distress; shortening life in this world and the hereafter; going far from the mercy of God; the wrath of God in this world and the hereafter; wandering; lack of cheerfulness in worship, regret and so on.

A Response to: Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review [Letter

A Response to: Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review [Letter

Visual Surveillance for Human Fall Detection in Healthcare IoT

This article designs a visual surveillance framework for human fall detection. In order to solve the conventional issues in fall detection, such as unsatisfactory feature generalization, low recall rates, and large computational time, we design a model that incorporates the deep convolutional neural network and the aggregated heuristic visual features in detecting the occurrence of falls. First, the convolutional neural network (Openpose model) is utilized to extract human skeleton in the image. Second, the hand-crafted spatial features, such as the angle of human shank inclination, are aggregated to determine the fall presence. It should be noticed that our fall detection method has been integrated to healthcare Internet of Things (IoT) video surveillance architecture, which has multiple graphic processing unit groups to perform real-time monitoring and alarming for the elderly in need. The experimental results prove that our method is able to accurately distinguish fall and nonfall activities with a competitive false-alarm rate.

A biblical narrative of the theology of work

In this article, the investigation of the theology of work is carried through from the perspective of biblical narratives in view of the origin of work as stated in Genesis 2:15. The basis of this argument is derived from the premise that God instituted work, and that it was not the result of the human fall in the story of creation. The author further argues that work was given to humanity to be able to develop a relationship between humanity and the Creator. Furthermore, it is argued that the use of verbs in the Hebrew text, such as, bad and shamargive adescription of the theology of work in the creation story. The researcher discovered that our daily work was God's idea, and it was intended to benefit humanity. Purpose: To explore the theology of work in the Biblical narratives and to provide the evidence that work was given to human beings to strengthen the relationship between the Creator and men. The researcher also attempts to argue that the Sabbath rest presupposes work. Findings: The work of humanity was introduced in the Garden of Eden when Adam and Eve were given the task of caring for the garden (Gen 2:15). Furthermore, work was the primary focus in the prologue of Genesis (Gen 1:1–2:3) and this suggested that work was the theme of the Biblical narratives.

Machine learning based wearable sensor module for human fall detection - a fully functional solution

Machine learning based wearable sensor module for human fall detection - a fully functional solution

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors. In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters. Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images. To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.