Human Fall Detection Research Articles

Vision-Based Fall Detector for Elderly Based on Sliding Window Approach and Feature Engineering

A new vision-based fall detector is proposed that uses the tsfresh tool to generate features from the bounding box motion parameters of an object and performs classification in a sliding window mode. The efficiency of the generated features is demonstrated compared to the primary ones. Using the auto-sklearn library and a generalized dataset compiled from the UR Fall Detection and CAUCAFall datasets, the best human fall detection model is found. This model, based on a gradient boosting classifier, achieved 96% accuracy, which is not inferior to well-known detection algorithms, but uses only two primary motion parameters to generate secondary features. A PCA-based class separability study showed that for secondary features, 99% of the variance is captured by the first 4 principal components, while for primary features, the first 10 principal components contain only 80% of the data variance. Furthermore, the processing time for generating secondary features and making predictions was found to be relatively short, taking only a few seconds per sequence, highlighting the practical applicability of the proposed approach in real-time fall monitoring systems.

Survey on data fusion approaches for fall-detection

Human fall detection is a critical research area focused on developing methods and systems that can automatically detect and recognize falls, particularly among the elderly and individuals with disabilities. Falls are a major cause of injuries and deaths among these populations, and timely intervention can reduce the severity of consequences. This article presents a comprehensive review of fall detection systems, emphasizing the use of cutting-edge technologies such as deep learning, sensor fusion, and machine learning. The research explores a variety of methodologies and strategies employed in fall detection systems, including the integration of wearable sensors, smartphones, and cameras. By examining various fall detection techniques and their experimental results, the article highlights the effectiveness of these systems in identifying and classifying falls. The study also addresses the challenges and limitations associated with fall detection systems, emphasizing the need for ongoing research and advancements. In summary, this research contributes to the development of advanced fall detection systems, demonstrating their potential to improve the quality of life for the elderly, alleviate healthcare burdens, and provide reliable solutions for fall detection and classification.

Human Fall Detection with Ultra-Wideband Radar and Adaptive Weighted Fusion.

To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time-distance images, time-distance images, and distance-distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%.

Learning and Vision-based approach for Human fall detection and classification in naturally occurring scenes using video data

Learning and Vision-based approach for Human fall detection and classification in naturally occurring scenes using video data

State-of-the-art radar technology for remote human fall detection: a systematic review of techniques, trends, and challenges

State-of-the-art radar technology for remote human fall detection: a systematic review of techniques, trends, and challenges

A High-Precision Human Fall Detection Model Based on FasterNet and Deformable Convolution

To address the challenges of low accuracy and suboptimal real-time performance in fall detection, caused by lighting variations, occlusions, and complex human poses, a novel fall detection algorithm, FDT-YOLO, has been developed. This algorithm builds upon an improved YOLOv8 framework, featuring significant modifications for improved performance. The C2f module in the backbone network has been replaced with the FasterNet module. This substitution enhances feature reuse effectively and reduces computational complexity. Additionally, a deformable convolution module has been added to the neck section. This helps to decrease missed and false detections significantly, which are often caused by dramatic changes in fall poses. Furthermore, the triplet attention mechanism has been incorporated during multi-scale fusion. This mechanism effectively suppresses background interference, focusing more on the target area, thereby improving detection accuracy and robustness. Experimental results have demonstrated that improvements in FDT-YOLO lead to notable enhancements. The mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5 has been increased from 94.9% to 96.2%. The mAP for the range of 0.5 to 0.95 has been raised from 84.2% to 85.9%. Additionally, the parameter count has been reduced to 9.9 million, which not only enhances detection accuracy but also significantly reduces the rate of false detections.

Advanced Millimeter-Wave Radar System for Real-Time Multiple-Human Tracking and Fall Detection.

This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses.

Integrating Convolutional Neural Network and Weighted Moving Average for Enhanced Human Fall Detection Performance

This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.

A High-Precision Fall Detection Model Based on Dynamic Convolution in Complex Scenes

Falls can cause significant harm, and even death, to elderly individuals. Therefore, it is crucial to have a highly accurate fall detection model that can promptly detect and respond to changes in posture. The YOLOv8 model may not effectively address the challenges posed by deformation, different scale targets, and occlusion in complex scenes during human falls. This paper presented ESD-YOLO, a new high-precision fall detection model based on dynamic convolution that improves upon the YOLOv8 model. The C2f module in the backbone network was replaced with the C2Dv3 module to enhance the network’s ability to capture complex details and deformations. The Neck section used the DyHead block to unify multiple attentional operations, enhancing the detection accuracy of targets at different scales and improving performance in cases of occlusion. Additionally, the algorithm proposed in this paper utilized the loss function EASlideloss to increase the model’s focus on hard samples and solve the problem of sample imbalance. The experimental results demonstrated a 1.9% increase in precision, a 4.1% increase in recall, a 4.3% increase in mAP0.5, and a 2.8% increase in mAP0.5:0.95 compared to YOLOv8. Specifically, it has significantly improved the precision of human fall detection in complex scenes.

Model and Empirical Study on Multi-tasking Learning for Human Fall Detection

Many fall detection systems are being used to provide real-time responses to fall occurrences. Automated fall detection is challenging because it requires very high accuracy to be clinically acceptable. Recent research has tried to improve sensitivity while reducing the high rate of false positives. Nevertheless, there are still limitations in terms of having efficient learning approaches and proper datasets to train. To reduce false alarms, one approach is to add more nonfall data as negative samples to train the deep learning model. However, this approach increases class imbalance in the training set. To tackle this problem, we propose a multi-task deep learning approach that divides datasets into multiple training sets for multiple tasks. We prove this approach gives better results than a single-task model trained on all datasets. Many experiments are conducted to find the best combination of tasks for multi-task model training for fall detection.

Real-time video surveillance based human fall detection system using hybrid haar cascade classifier

Real-time video surveillance based human fall detection system using hybrid haar cascade classifier

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

Vision-based Human Fall Detection Systems: A Review

Falling is a significant threat for old people and disabled persons. Therefore, it is necessary to develop an automated fall detection system to make them more independent and to mitigate the physical and psychological consequences of falls. Such a system can lower the death rate among dependent people who live alone. This paper presents a review of vision-based techniques, algorithms and databases used in detecting falls. Recently proposed systems are analyzed and compared. The literature review underscores the promising potential of camera-based fall detection systems and highlights areas for further research and development.

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

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.