Human Fall Research Articles

A Radar-Based System for Detection of Human Fall Utilizing Analog Hardware Architectures of Decision Tree Model

A Radar-Based System for Detection of Human Fall Utilizing Analog Hardware Architectures of Decision Tree Model

Fast and High-Precision Human Fall Detection Using Improved YOLOv8 Model

Fast and High-Precision Human Fall Detection Using Improved YOLOv8 Model

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

Mentoring families for spiritual growth in a quest to end family violence, infidelity and divorce

The fall of humanity negatively affected the relationship between God and Human beings. Isaiah 59:2 states that iniquities separate people from God and sins hid the face of God that He does not hear. There is a need therefore, to restore the relationship between God and people in order for life to be what God intended when he created man. Careful and incessant attention should be given to spiritual aspect of a person, which should result into a better relationship with the Creator. To have spiritual growth that will finally end into glorification, a positive relationship with God should be built. This may well be done in a family setting; because a family remains an important institution in the entire human history. Though important a family is, it is difficult to have a precise definition for the word family because it has many meanings depending on how it is used and the purpose for using it. Nevertheless, three elements are common when describing a family. These are marriage, parenthood and residence. In this article, the word family refers to a group of people who live together in a household due to marriage and parenthood. It is in this setting where mentorship should begin. In a family circle, effective mentors, who are dispensers of know-how are well placed in an age where morality has gone down and media has become the order of the day. Through family mentorship for spiritual growth, foundational values, for character development would be encouraged. These values are honesty, respect, kindness, empathy, and responsibility. The values can easily be promoted through family worship, Bible studies, prayer meetings and the involvement of the Holy Spirit in one’s daily life. In turn, positive relationships among family members would be the result that would end family violence, infidelity and divorce. Mentoring families for spiritual growth will also restore broken relationships. The goal of mentoring families for the spiritual growth is to have peaceful families that are waiting for the second coming of Jesus Christ.

Real-Time Posture Estimation-Based Human Fall Detection System

Special attention should be given to those in vulnerable age groups, such as children and the elderly, who live in solitary living arrangements and have an increased likelihood of experiencing falls. The primary objective of a fall detector is to minimize the duration of time an elderly person remains on the ground subsequent to experiencing a fall event. The length of time that an individual remains on the floor subsequent to a fall is a determining factor in assessing the severity of the incident. The act of engaging in prolonged deceit has been shown to elevate the likelihood of experiencing adverse health outcomes such as hypothermia, dehydration, and pressure ulcers. The ultimate objective of the detector system is to identify the occurrence of a fall and expeditiously notify a carer. The potential for individuals to inadvertently trigger a fall detector by engaging in rapid movements such as standing up and sitting down underscores the need for a dependable detector that can effectively differentiate between instances of falling and other related occurrences. The primary aim of this research is to enhance the understanding of fall-related mechanisms among medical technologists in the field of public health. The assessment of an individual's physique alteration potential relies on the calculation of the ratio between their height and width. To validate a human fall, it is necessary to measure the height and centre of the rectangle that encloses the individual, and thereafter compare these measurements to a specified threshold. An alert system has been developed to notify persons who are connected to the network in the event of a catastrophe, provided that a state of inactivity is maintained for 100 consecutive frames.

A Lightweight Human Fall Detection Network.

The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm's precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method's superiority and efficacy.

A Machine Learning Classification Paradigm for Automated Human Fall Detection

For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.
 The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.
 Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.

Human Fall Detection Based on Posture Estimation and Infrared Thermography

Human Fall Detection Based on Posture Estimation and Infrared Thermography

IR-ST: A Lightweight Transformer Network for Human Fall Detection Based on FMCW Radar

IR-ST: A Lightweight Transformer Network for Human Fall Detection Based on FMCW Radar

Computer vision based human fall detection and classification for real-time videos

Most recently, Human fall detection systems using deep learning models find major applications in all fields, especially in the held of healthcare. Even without doctor analysis, most Neurological and musculoskeletal diseases such as oncoming strokes and gait problems can be identified using these models and computer vision. In this article, automatic human fall detection is proposed using a convolutional neural network by applying real-time videos. In general, most of the research has been carried out using standard videos which will not apply to real-time applications. Hence this work concentrates about using convolutional neural networks as a system has real-time videos for the Human Fall Detection and monitoring system using three pre-trained models: (i) TinyYOLOv3-ones, (ii) AlphaPose and (iii) ST-GCN. The proposed Spatial temporal graph convolutional networks produce better accuracy with captured real-time video for human fall detection. The same method was also utilized for classification with different epochs. The results were compared and maximum accuracy of 100% is obtained for 500 epochs. Hence it is proved that the existing method can be utilized for human fall detection with greater accuracy.

Rethinking Fall Detection With Wi-Fi

The past decades have witnessed a surge in human fall detection with sensors, cameras, and wireless signals. Among them, Wi-Fi-based fall detection has been one of the most attractive solutions due to the ubiquitous and pervasive deployment of Wi-Fi infrastructures. However, these approaches are still difficult to be put into practical use. To push forward Wi-Fi-based fall detection for wide deployment, three major limitations concerning <i>environmental diversity</i>, <i>motion diversity</i>, and <i>user diversity</i> are required to be resolved. In this paper, we propose FallDar, a Wi-Fi-based deep learning-assisted fall detection system that outperforms state-of-the-art works on the three criteria simultaneously. First, to deal with environmental diversity, FallDar characterizes falls with the speed of the body, which is the most relevant and inherent feature of falling activities, making the system resilient to environmental changes. Second, to deal with motion diversity, FallDar simulates a large amount of fall data of various falling types with a DNN-based generative model. Training with these data, FallDar is endowed the capability of detecting more types of falls. Third, to deal with user diversity, FallDar proposes to incorporate the fall detection network with a user identification network. The network is designed to extract user-independent features, requiring no fall data from new users for system adjustment. We implement FallDar on commercial Wi-Fi devices and conduct experiments in home and office environments for six months. The evaluation results show that FallDar achieves a false alarm rate of 5.7&#x0025; and a missed alarm rate of 3.4&#x0025; across all factors, making a fundamental step towards ubiquitous fall detection with Wi-Fi.

Ionogel based triboelectric nanogenerator textiles for high-precision human fall recognition

Ionogel based triboelectric nanogenerator textiles for high-precision human fall recognition

Fall Detection Method Based on Convolutional Neural Network

With the improvement of people's living standards and social medical conditions, the average life expectancy is gradually lengthening, and the proportion of the elderly in the global population is also growing. China is a big country with a large population, and the problem of population aging is becoming increasingly prominent and severe. When elderly people live alone at home without others to care for them, falls become the most common and dangerous phenomenon due to the decline of physical function and the influence of certain diseases. Therefore, rapid, efficient and accurate identification and judgment of human falls are of great significance, which can effectively alleviate the threat to the life and health of the elderly and the social medical burden of falls. This paper mainly carried out a fall detection method based on convolutional neural networks, aiming to improve the accuracy of human fall detection through the natural advantages of convolutional neural networks in image recognition, and provide better solutions for medical monitoring and elderly care.

Dilated spatial–temporal convolutional auto-encoders for human fall detection in surveillance videos

Although methods based on supervised learning have demonstrated remarkable performance on fall detection, these existing fall detection algorithms require a substantial quantity of manually labeled training data. In this paper, we combine dilated convolution and LSTM based on auto-encoder, which can be trained on unlabeled data, further saving time and resources, and a novel fall score is computed based on the high-quality reconstructed frame to detect falls. Extensive experimental results indicate that the proposed method further boosts the performance, achieving recognition rate of 97.1%, sensitivity rate of 93.9% and precision rate of 95.1% on the UR dataset.

SDFA: Structure-Aware Discriminative Feature Aggregation for Efficient Human Fall Detection in Video

Older people are susceptible to fall due to instability in posture and deteriorating health. Immediate access to medical support can greatly reduce repercussions. Hence, there is an increasing interest in automated fall detection, often incorporated into a smart healthcare system to provide better monitoring. Existing systems focus on wearable devices which are inconvenient or video monitoring which has privacy concerns. Moreover, these systems provide a limited perspective of their generalization ability as they are tested on datasets containing few activities that have wide disparity in the action space and are easy to differentiate. Complex daily life scenarios pose much greater challenges with activities that overlap in action spaces due to similar posture or motion. To overcome these limitations, we propose a fall detection model, coined SDFA, based on human skeletons extracted from low-resolution videos. The use of skeleton data ensures privacy and low-resolution videos ensures low hardware and computational cost. Our model captures discriminative structural displacements and motion trends using unified joint and motion features projected onto a shared high dimensional space. Particularly, the use of separable convolution combined with a powerful GCN architecture provides improved performance. Extensive experiments on five large-scale datasets with a wide range of evaluation settings show that our model achieves competitive performance with extremely low computational complexity and runs faster than existing models.

One-Class Classification for Radar-Based Human Fall Event Detection

Existing deep learning and machine learning techniques for radar-based fall event detection require a large number of fall activity instances at the training stage to produce reliable results. However, to replicate real-life scenarios, falls should be treated as rare events. This work pioneers the application of one-class classification (OCC) technique for radar-based fall event detection. A comparative analysis of three different types of OCC techniques using abstract features extracted from six different pre-trained models is shown. An improved novel feature fused OCC model is proposed that achieves an area under the curve value of 0.912 for fall event detection. All evaluations are done on a publicly available dataset of human radar signatures.

Exploiting Deep Visual Geometry Group Architecture for Fall Detection in the Elderly People

Over the last couple of decades, human fall detection has gained considerable popularity, especially for the elderly. Elderly people need more attention as compared to others in their homes, hospitals, and care centers. Various solutions have been proposed to deal with this problem, yet, many aspects of this problem are still unresolved. The current study proposed an approach for human fall detection based on the Visual Geometry Architecture of deep learning. The presented approach was weighed up with state-of-the-art approaches including ResNet-50 and even ResNet-101 by using MCF and URFD datasets, outperforming them with an accuracy of 98%. The proposed approach also outperformed these deep architectures in terms of performance efficiency.

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

Unintentional human falls, particularly in older adults, can result in severe injuries and death, and negatively impact quality of life. The World Health Organization (WHO) states that falls are a significant public health issue and the primary cause of injury-related fatalities worldwide. Injuries resulting from falls, such as broken bones, trauma, and internal injuries, can have severe consequences and can lead to a loss of mobility and independence. To address this problem, there have been suggestions to develop strategies to reduce the frequency of falls, in order to decrease healthcare costs and productivity loss. Vision-based fall detection approaches have proven their effectiveness in addressing falls on time, which can help to reduce fall injuries. This paper introduces an automated vision-based system for detecting falls and issuing instant alerts upon detection. The proposed system processes live footage from a monitoring surveillance camera by utilizing a fine-tuned human segmentation model and image fusion technique as pre-processing and classifying a set of live footage with a 3D multi-stream CNN model (4S-3DCNN). The system alerts when the sequence of the Falling of the monitored human, followed by having Fallen, takes place. The effectiveness of the system was assessed using the publicly available Le2i dataset. System validation revealed an impressive result, achieving an accuracy of 99.44%, sensitivity of 99.12%, specificity of 99.12%, and precision of 99.59%. Based on the reported results, the presented system can be a valuable tool for detecting human falls, preventing fall injury complications, and reducing healthcare and productivity loss costs.

Fall Detection

Researchers from all across the world are interested in human fall detection and activity recognition. Fall detection is an exciting topic that may be tackled in several ways. Several alternatives have been suggested in recent years. These applications determine whether a person is walking, squatting, or falling, among other activities. Among these tasks detecting elderly falls is crucial. This is because it is a pretty typical and dangerous occurrence that affects people of all ages, with the elderly having a disproportionately negative impact. This is the motivation behind the development of an aged assistance system that detects falls and notifies the caretaker. One of the key characteristics is that it sends an email notification along with a call to the caretaker once a fall is detected.

A novel lightweight deep learning fall detection system based on global-local attention and channel feature augmentation

Abstract Background and Objective: Reducing the number of falls in nursing facilities is crucial to prevent significant injury, increased costs, and emotional harm. However, current fall detection systems face a trade-off between accuracy and inference speed. This work aimed to develop a novel lightweight fall detection system that can achieve high accuracy and speed while reducing computational cost and model size. Methods: We used convolutional neural networks and the channel-wise dropout and global-local attention module to train a lightweight fall detection model on over 10,000 human fall images from various scenarios. We also applied a channel-based feature augmentation module to enhance the robustness and stability of the model. Results: The proposed model achieved a detection precision of 95.1%, a recall of 93.3%, and a mean average precision of 91.8%. It also had a significantly smaller size of 1.09 million model parameters and a lower computational cost of 0.12 gigaFLOPS than existing methods. It could handle up to 20 cameras, simultaneously with a speed higher than 30 fps. Conclusion: The proposed lightweight model demonstrated excellent performance and practicality for fall detection in real-world settings, which could reduce the working pressure on medical staff and improve nursing efficiency.