Human Fall 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.

Key point trajectory prediction method of human stochastic posture falls

ABSTRACT The human body will show very complex and diversified posture changes in the process of falling, including body posture, limb position, angle and movement trajectory, etc. The coordinates of the key points of the model are mapped to the three-dimensional space to form a three-dimensional model and obtain the three-dimensional coordinates of the key points; The construction decomposition method is used to calculate the rotation matrix of each key point, and the rotation matrix is solved to obtain the angular displacement data of the key points on different degrees of freedom. The method of curve fitting combined with the weight distribution kernel function based on self-organizing mapping theory is used to obtain the motion trajectory prediction equation of the human body falling in different degrees of freedom at random positions in three-dimensional space, determine the key point trajectory of human random fall behaviour. The experimental results show that the mapped 3D model is consistent with the real human body structure. This method can accurately determine whether the human body falls or squats randomly, and the prediction results of the key points of the human fall are consistent with the actions of the human body after the fall.

A Security Framework for Railway Platform Development

The different modes of transport are air, water, and land transport, in which land transport includes rail transport, road transport and off-road transport. Most commonly using transport system are Roadways, Railways and Airways. In these frequently we use the railways, because it is cheaper and it is more convenient than other transport systems. The third largest railway network in the world is Indian railway network. But the accident’s occurs in the railways is higher. Mostly the accidents in the railway station occur between the railway platform and to the train, because of the platform gap. The platform gap is the gap exists between the train and to the platform. To avoid these accidents that the Indian government come with an indicative to increase the height of the platform. But it is not easy job to increase the height. At the time of working we cannot access the platforms. By applying my project it is easy to avoid the accidents as well as we can use the platform too. A human fall down in the gap exist between the platform and to the train can be totally avoided by introducing Mechanical platform edge extensions known as platform gap fillers which is used to bridge the gap between platform and to the train. This is more useful in the case of safeguard the human life and also it is cheaper than the cost which is involved for increase the height of the platform. In this regard the safety barrier setup present at the bottom of the platform will be automatically uplifted with the help of sensor, so the platform gap is closed with the safety barrier. And then it will retract again into the bottom at the platform after leaving the station. By this project the platform gap accidents can be avoided.

Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention

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

Enhanced Classification of Human Fall and Sit Motions Using Ultra-Wideband Radar and Hidden Markov Models

In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems.

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.

Mitigating human fall injuries: A novel system utilizing 3D 4-stream convolutional neural networks and image fusion

Unintentional human falls, especially in seniors, lead to serious injuries, fatalities, and reduced standard of life. Vision-based fall detection methods have demonstrated their usefulness in timely fall response, helping to lessen such injuries. This paper presents an automated vision-based fall detection system that triggers immediate fall reporting. By incorporating human segmentation and image fusion in the pre-processing stage, the system enhances the accuracy of human action classification, thereby ensuring precise fall alerts. It further employs the innovative 4-stream 3D convolutional neural network (4S-3DCNN) model to learn different but consecutive spatial and temporal features. The system processes video input or live surveillance, segmenting human presence every 32 frames using a fine-tuned deep-learning model and applying a three-level image fusion to accentuate movement differences. This technique produces four pre-processed images, input to the 4S-3DCNN model for classification. Consecutive detection of “Falling” and “Fallen” actions triggers an alert for immediate intervention. The original 4S-3DCNN model is an end-to-end trained deep learning model with a fully connected layer serving as a classifier. The research also evaluates the performance of combining the 4S-3DCNN model with Autoencoders and Support Vector Machines (SVM) networks as classifiers. The SVM classifier demonstrated ideal fall detection performance with 100% accuracy using the MCFD, URFD, and Le2i FDD datasets. The proposed system is vital for detecting and preventing falls and reducing healthcare expenses and productivity losses.

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.

Applicability of the Madymo Pedestrian Model for forensic fall analysis

Forensic reconstruction and scenario evaluation are crucial in investigations of suspicious deaths related to falls from a height. In such cases, distinguishing between accidental falls, being pushed or jumping is an important but difficult task, since objective methods to do so are currently lacking. This paper explores the possibility of repurposing a passive rigid body model of a human from commercially available crash simulation software for forensic reconstruction and scenario evaluation of humans dropping from heights. To use this approach, a prerequisite is that the human body model can produce realistic movements compared to those of a real human, given similar environmental conditions. Therefore, this study assessed the validity of the commercially available Simcenter Madymo Pedestrian Model (MPM) for simulating human fall movements. Experimental kinematic and kinetic data was collected from nine participants, who dropped from a height in three different ways: passively tilting over, getting pushed, and jumping. Next, the performance of the MPM in reproducing the kinematics of the experimental falls was assessed by comparing the orientation of the body 0.3 s after platform release. The results show that the MPM currently does not consistently reproduce the experimentally recorded falling movements across multiple falling conditions and outcome measures. The MPM must therefore be adapted if to be used for forensic reconstruction and scenario evaluation, for example by implementing active movement.

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.

Investigation of the Effect of Physical Ability on the Fall Mitigation Motion Using the Combination of Experiment and Simulation

The simulation of fall plays a critical role in estimating injuries caused by fall. However, implementing human fall mitigation motions on a simulator proves challenging due to the complexity and variability of fall movement. Our simulator estimates fall motion by extrapolating the motion observed in fall experiments. By incorporating actual fall motion data for the upper limbs, we enhanced the realism of the fall simulation. The application of forward dynamics control to the lower limbs allowed for the adjustment of mitigation motions, taking into account individual physical capabilities. In this study, fall simulations were conducted under the constraints of maximum joint torque and maximum torque change rate, emulating the physical capabilities of both the elderly and young adults. Our results successfully demonstrated the mitigation motion facilitated by the stance leg reduced the descent velocity of the center of mass by 0.75 m/s for elderly individuals and by 1.25 m/s for young adults, compared to a zero torque condition. This indicates that our study introduced a novel method for quantifying the impact of the lower limbs’ physical capabilities on fall velocity. Such a method represents a significant advancement in understanding how mitigation motions can influence fall injury simulations.

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

TAMNICA KAO PAD I APOKALIPSA U PRIPOVEDAČKOJ PROZI IVE ANDRIĆA

The paper examines the semantics of the dungeon in Ivo Andric’s short stories, primarily in the writer’s dungeon stories. In the theoretical footsteps of Northrop Frye, Nikolai Berdyaev and Michel Foucault, we point to the dungeon as two symbolic forms of human fall, external and internal. The outer dungeon is reflected in the social determinants of the ideologically conceived identities of Andric’s heroes, while the inner dungeon is explained through the psychological and ontological aspects of their existence, through the metaphysical rethinking of the idea of freedom as an impossible free will in the context of modernity which revalues the Biblical concept of innocence. In accordance with such representations, the dungeon is also contextualized through two apocalyptic visions of the human fall that correspond to Berdjaev’s dialectic of external (worldly) and internal (spiritual) apocalypse. It is concluded that the Biblical-Christian semantics of the Fall is deconstructed in Andric’s stories, in order that his heroes become bearers of an absurd desire for ontological salvation, fall as a feeling of modern(istic) self-exile.

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.