Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016) Deep Learning. MIT Press, Cambridge, MA, https://www.deeplearningbook.org/

Joseph Redmon and Santosh Kumar Divvala and Ross B. Girshick and Ali Farhadi (2015) You Only Look Once: Unified, Real-Time Object Detection. CoRR abs/1506.02640dblp computer science bibliography, https://dblp.org, https://dblp.org/rec/journals/corr/RedmonDGF15.bib, Mon, 13 Aug 2018 16:48:08 +0200, 1506.02640, arXiv, http://arxiv.org/abs/1506.02640

Jocher, Glenn. YOLOv11: Next-Generation Real-Time Object Detection. https://github.com/ultralytics/YOLOv11, 2025

Bazarevsky, Valentin and Zhang, Fan and Vakunov, Andrey and Sung, Gabriel and Grundmann, Matthias (2020) BlazePose: On-device Real-time Body Pose Tracking. CVPR Workshop

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Imen Charfi and Johel Miteran and Julien Dubois and Mohamed Atri and Rached Tourki (2013) {Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification}. Journal of Electronic Imaging 22(4): 041106 https://doi.org/10.1117/1.JEI.22.4.041106, https://doi.org/10.1117/1.JEI.22.4.041106, Video, Cameras, Education and training, Feature extraction, Object detection, Video surveillance, Image classification, Feature selection, Tunable filters, Actinium, SPIE

Bogdan Kwolek and Michal Kepski. Human fall detection on embedded platform using depth maps and wireless accelerometer. Assistive technology,Depth image analysis,Fall detection,Sensor technology for smart homes, Since falls are a major public health problem in an ageing society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.

Manning, Christopher D. and Raghavan, Prabhakar and Sch ütze, Hinrich (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge, 978-0-521-86571-5

Wang, Xueyi and Ellul, Joshua and Azzopardi, George (2020) Elderly Fall Detection Systems: A Literature Survey. Frontiers in Robotics and AI Volume 7 - 2020 https://doi.org/10.3389/frobt.2020.00071, <p >Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial. </p >, 2296-9144, https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2020.00071

Xi Cai and Xiangcheng Wang and Yin Jiao and Yinuo Chen and Guang Han (2025) Multi-scale motion perception fall detection algorithm based on video swin transformer. Signal, Image and Video Processing 19 https://doi.org/10.1007/s11760-025-04358-3, Springer Science and Business Media Deutschland GmbH, 10, Computer vision,Deep learning,Fall detection,Video swin transformer, 10, 18631711, Fall detection is a prominent subject in healthcare. Advancements in modern monitoring and deep learning have sparked significant social interest in visual fall detection. Despite the success of various deep learning methods in video fall detection owing to their superior feature extraction capabilities, they still encounter challenges in analyzing long-range or short-range spatiotemporal correlations. Taking this into account, a multi-scale motion perception fall detection algorithm based on video swin transformer is proposed in this study. Our proposed method employs video swin transformer as the backbone to fully model the global and local spatiotemporal information from videos and optimizes the backbone with two integrated modules. On one hand, we design a multi-scale motion information aggregation module to overcome the difficulty of the model in focusing on key multi-scale motion features. On the other hand, we propose a token pruning module to reduce the computational cost by pruning redundant temporal tokens. Experimental results demonstrate that the proposed algorithm exhibits promising outcomes, with an accuracy of 96.11% and 97.05% on the Le2i and UR fall detection datasets, respectively, thus outperforming some existing advanced algorithms.

Jungpil Shin and Abu Saleh Musa Miah and Rei Egawa and Koki Hirooka and Md Al Mehedi Hasan and Yoichi Tomioka and Yong Seok Hwang (2025) Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation. Scientific Reports 15 https://doi.org/10.1038/s41598-025-95508-7, Nature Research, 40148548, 12, Ageing people,AlphaPose,Classification,Fall detection (FD),Graph convolutional network (GCN),Human activity recognition (HAR),Multi-stream deep learning,Sep-TCN,Spatio-temporal, 1, 20452322, The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent suicide attempts resulting from medication overdose, underscores the critical importance of accurate and efficient methods of detecting a fall. This makes a computer-aided fall detection system necessary to save elderly people ’s lives worldwide. Many researchers have been working to develop fall detection systems. However, the existing systems often struggle with problems such as unsatisfactory accuracy, limited robustness, high computational complexity, and sensitivity to environmental factors. In response to these challenges, this paper proposes a novel three-stream spatio-temporal feature-based human fall detection system. Our system incorporates joint skeleton-based spatial and temporal Graph Convolutional Network (GCN) features, joint motion-based spatial and temporal GCN features, and residual connections-based features. Each stream employs adaptive graph-based feature aggregation and consecutive separable convolutional neural networks (Sep-TCN), significantly reducing the computational complexity and the number of parameters of the model compared to prior systems. Experimental results on multiple datasets demonstrate the superior effectiveness and efficiency of our proposed system, with accuracies of 99.68%, 99.97%, 99.47 % and 98.97% achieved on the ImViA, Fall-UP, FU-Kinect and UR-Fall datasets, respectively. The remarkable performance of our system highlights its superiority, efficiency, and generalizability in real-world human fall detection scenarios, offering significant advancements in healthcare and societal well-being.

Fangping Fu (2025) A VIDEO-BASED FALL DETECTION USING 3D SPARSE CONVOLUTIONAL NEURAL NETWORK IN ELDERLY CARE SERVICES. Machine Graphics and Vision 34: 53-74 https://doi.org/10.22630/MGV.2025.34.1.3, Institute of Information Technology, Warsaw University of Life Sciences - SGGW, 3D convolutional neural network,fall detection,jitter buffer,sparse convolution, 1, 2720250X, Falls in the elderly have become one of the major risks for the growing elderly population. Therefore, the application of automatic fall detection system for the elderly is particularly important. In recent years, a large number of deep learning methods (such as CNN) have been applied to such research. This paper proposed a sparse convolution method 3D Sparse Convolutions and the corresponding 3D Sparse Convolutional Neural Network (3D-SCNN), which can achieve faster convolution at the approximate accuracy, thereby reducing computational complexity while maintaining high accuracy in video analysis and fall detection task. Additionally, the preprocessing stage involves a dynamic key frame selection method, using the jitter buffers to adjust frame selection based on current network conditions and buffer state. To ensure feature continuity, overlapping cubes of selected frames are intentionally employed, with dynamic resizing to adapt to network dynamics and buffer states. Experiments are conducted on Multi-camera fall dataset and UR fall dataset, and the results show that its accuracy exceeds the three compared methods, and outperforms the traditional 3D-CNN methods in both accuracy and losses.

Xi Cai and Xiangcheng Wang and Kexin Bao and Yinuo Chen and Yin Jiao and Guang Han (2025) Visual perception enhancement fall detection algorithm based on vision transformer. Signal, Image and Video Processing 19 https://doi.org/10.1007/s11760-024-03652-w, Springer Science and Business Media Deutschland GmbH, 1, Attention mechanism,Computer vision,Deep learning,Fall detection,Vision transformer, 1, 18631711, Fall detection is a crucial research topic in public healthcare. With advances in intelligent surveillance and deep learning, vision-based fall detection has gained significant attention. While numerous deep learning algorithms prevail in video fall detection due to excellent feature processing capabilities, they all exhibit limitations in handling long-term spatiotemporal dependencies. Recently, Vision Transformer has shown considerable potential in integrating global information and understanding long-term spatiotemporal dependencies, thus providing novel solutions. In view of this, we propose a visual perception enhancement fall detection algorithm based on Vision Transformer. We utilize Vision Transformer-Base as the baseline model for analyzing global motion information in videos. On this basis, to address the model ’s difficulty in capturing subtle motion changes across video frames, we design an inter-frame motion information enhancement module. Concurrently, we propose a locality perception enhancement self-attention mechanism to overcome the model ’s weak focus on local key features within the frame. Experimental results show that our method achieves notable performance on the Le2i and UR datasets, surpassing several advanced methods.

Duncan Kibet and Min Seop So and Hahyeon Kang and Yongsu Han and Jong Ho Shin (2024) Sudden Fall Detection of Human Body Using Transformer Model. Sensors 24 https://doi.org/10.3390/s24248051, Multidisciplinary Digital Publishing Institute (MDPI), 39771788, 12, fall detection,pose estimation,speed-based anomaly detection,time-series analysis,transformers, 24, 14248220, In human activity recognition, accurate and timely fall detection is essential in healthcare, particularly for monitoring the elderly, where quick responses can prevent severe consequences. This study presents a new fall detection model built on a transformer architecture, which focuses on the movement speeds of key body points tracked using the MediaPipe library. By continuously monitoring these key points in video data, the model calculates real-time speed changes that signal potential falls. The transformer ’s attention mechanism enables it to catch even slight shifts in movement, achieving an accuracy of 97.6% while significantly reducing false alarms compared to traditional methods. This approach has practical applications in settings like elderly care facilities and home monitoring systems, where reliable fall detection can support faster intervention. By homing in on the dynamics of movement, this model improves both accuracy and reliability, making it suitable for various real-world situations. Overall, it offers a promising solution for enhancing safety and care for vulnerable populations in diverse environments.

Lei Liu and Yeguo Sun and Yinyin Li and Yihong Liu (2025) A hybrid human fall detection method based on modified YOLOv8s and AlphaPose. Scientific Reports 15 https://doi.org/10.1038/s41598-025-86429-6, Nature Research, 39837978, 12, Computer vision,Fall detection,Human pose estimation,Object detection, 1, 20452322, To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function. These methods can effectively improve the accuracy of small object detection and significantly reduce the model size. Secondly, we improve the fall recognition accuracy by introducing a hybrid fall detection algorithm based on human skeletal nodes. Lastly, we build a multi-person fall detection dataset (MPFDD) to test the model ’s effectiveness in multi-person scenarios. Validated on datasets Le2i and MPFDD, our method improves accuracy by 4.30%, F1 by 4.57%, and FPS by 37.50% faster than the AlphaPose. Compared with other models, our model improves accuracy by 5.33% on average, F1 by 5.51%, and FPS by 43.05% faster on average. Therefore, HFDMIA-Pose has significantly improved performance compared to the original model and it also demonstrates strong competitiveness over other advanced human fall detection models. Furthermore, it has the advantages of high detection accuracy, fewer model size, and fast speed, which makes it more suitable for resource constrained edge environments and can meet industrial and daily scenarios.

F. Xavier Gaya-Morey and Cristina Manresa-Yee and Jos é M. Buades-Rubio (2024) Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review. Applied Intelligence 54: 8982-9007 https://doi.org/10.1007/s10489-024-05645-1, Springer, 10, Ambient assisted living,Computer vision,Deep learning,Elderly,Fall detection,Human activity recognition, 19, 15737497, Abstract: As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies. Graphical abstract: (Figure presented.).

Angelo Capodici and Claudio Fanconi and Catherine Curtin and Alessandro Shapiro and Francesca Noci and Alberto Giannoni and Tina Hernandez-Boussard (2025) A scoping review of machine learning models to predict risk of falls in elders, without using sensor data. Diagnostic and Prognostic Research 9 https://doi.org/10.1186/s41512-025-00190-y, Springer Science and Business Media LLC, 5, 1, This scoping review assesses machine learning (ML) tools that predicted falls, relying on information in health records without using any sensor data. The aim was to assess the available evidence on innovative techniques to improve fall prevention management. Studies were included if they focused on predicting fall risk with machine learning in elderly populations and were written in English. There were 13 different extracted variables, including population characteristics (community dwelling, inpatients, age range, main pathology, ethnicity/race). Furthermore, the number of variables used in the final models, as well as their type, was extracted. A total of 6331 studies were retrieved, and 19 articles met criteria for data extraction. Metric performances reported by authors were commonly high in terms of accuracy (e.g., greater than 0.70). The most represented features included cardiovascular status and mobility assessments. Common gaps identified included a lack of transparent reporting and insufficient fairness assessments. This review provides evidence that falls can be predicted using ML without using sensors if the amount of data and its quality is adequate. However, further studies are needed to validate these models in diverse groups and populations.

Jianjun Yan and Xueqiang Wang and Jiangtao Shi and Shuai Hu (2023) Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks. Sensors 23 https://doi.org/10.3390/s23042153, MDPI, 36850753, 2, fall detection,multiple inertial sensors,skeleton,spatial-temporal graph convolutional networks, 4, 14248220, The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.

Mohammadamin Salimi and Jos é J.M. Machado and Jo ão Manuel R.S. Tavares (2022) Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation. Sensors 22 https://doi.org/10.3390/s22124544, MDPI, 35746325, 6, computer vision,deep learning,image analysis,machine learning, 12, 14248220

Jinxi Zhang and Zhen Li and Yu Liu and Jian Li and Hualong Qiu and Mohan Li and Guohui Hou and Zhixiong Zhou (2024) An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design. Journal of Medical Internet Research 26 https://doi.org/10.2196/56750, JMIR Publications Inc., 39102676, MobiFall,Sisfall,accelerometer,deep learning,fall detection,gyroscope,human health,self-attention,wearable sensors, 14388871, Background: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors –based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning –based FDSs using manual feature extraction, and deep learning (DL) –based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. Objective: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Methods: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. Results: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). Conclusions: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Tin-Han Chi and Kai-Chun Liu and Chia-Yeh Hsieh and Yu Tsao and Chia-Tai Chan. PREFALLKD: PRE-IMPACT FALL DETECTION VIA CNN-VIT KNOWLEDGE DISTILLATION. Index Terms-Inertial measurement units,Knowledge distillation,Pre-impact fall detection,Vision transformer,Wearable sensors, Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms).

{World Health Organization}. Ageing and health. Fact sheet, updated Oct.\ 1, 2024. October, 2024, https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

{World Health Organization}. Falls. Fact sheet, updated Jan.\ 26, 2024. January, 2024, https://www.who.int/news-room/fact-sheets/detail/falls

P. J. Mercy and Sandhya K. Neelamana and Vijayan C. Parameswaran Nair (2024) Prevalence and risk factors for falls among the community dwelling older adults of Thrissur: A pilot study. Journal of Family Medicine and Primary Care 13: 875-880 https://doi.org/10.4103/jfmpc.jfmpc_2441_22, Medknow, 3, 3, 2249-4863

Amrit Pal Kaur and Ejay Nsugbe and Amy Drahota and Matthew Oldfield and Iman Mohagheghian and Radu A. Sporea (2025) State-of-the-art fall detection techniques with emphasis on floor-based systems —A review. Biomedical Engineering Advances 9: 100179 https://doi.org/10.1016/J.BEA.2025.100179, https://www.sciencedirect.com/science/article/pii/S2667099225000350?ref=pdf_download &fr=RR-2 &rr=97c281838fb43eff#page=10 &zoom=100,0,0, Elsevier, 6, 2667-0992, Abstract The Arthritis patients and aging population has challenged society to develop safer, independent living environments. Falls, associated injuries, and delays in fall treatment are major causes of morbidity and death in older adults. Therefore, fall detection systems are fundamental to reducing fall risks and building safer environments. Designing fall detection systems is an emerging field of research. The development of the system relies on a sensing mechanism, processing unit, and communication to alert the emergency facilities. Each module is crucial in providing a cost-effective, accurate, reliable, and robust solution. Technological advancements in fall detection systems, particularly wearable and non-wearable devices, offer promising solutions. Wearable systems are prevalent due to their cost-effectiveness and ease of installation, but they can be unreliable if not worn consistently. Non-wearable systems, including smart flooring, provide continuous monitoring but are expensive and complex to maintain. This article reviews the development and deployment of fall detection technologies, examining their practical limitations and emphasizing floor-based detection systems as a viable solution for fostering independent living among older adults.

Gomez, Diego and Medina, Mar ía and Rojas, Jes ús and Inga-Ortega, Juan (2024) Intelligent Elderly Monitoring System Using Computer Vision. 10.1109/COLCOM62950.2024.10720249, 1-6, 08

Adri án N ú ñez-Marcos and Ignacio Arganda-Carreras (2024) Transformer-based fall detection in videos. Engineering Applications of Artificial Intelligence 132 https://doi.org/10.1016/j.engappai.2024.107937, Elsevier Ltd, 6, Computer vision,Fall detection,Health,Transformer, 09521976, Falls pose a major threat for the elderly as they result in severe consequences for their physical and mental health or even death in the worst-case scenario. Nonetheless, the impact of falls can be alleviated with appropriate technological solutions. Fall detection is the task of recognising a fall, i.e. detecting when a person has fallen in a video. Such an algorithm can be implemented in lightweight devices which can then cater to the users ’ needs, e.g. alerting emergency services or caregivers. At the core of those systems, a model capable of promptly recognising falls is crucial for reducing the time until help comes. In this paper we propose a fall detection solution based on transformers, i.e. state-of-the-art neural networks for computer vision tasks. Our model takes a video clip and decides if a fall has occurred or not. In a video stream, it would be applied in a sliding-window fashion to trigger an alarm as soon as it detects a fall. We evaluate our fall detection backbone model on the large UP-Fall dataset, as well as on the UR fall dataset, and compare our results with existing literature using the former dataset.

Micheal Dutt and Aditya Gupta and Morten Goodwin and Christian W. Omlin (2024) An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection. Applied Sciences (Switzerland) 14 https://doi.org/10.3390/app14114722, Multidisciplinary Digital Publishing Institute (MDPI), 6, OpenPose,classification module,computational efficiency,deep learning,fall detection,gradient-weighted class activation mapping,privacy preservation,real-time monitoring,short-time Fourier transform,video-based systems, 11, 20763417, Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This paper introduces an innovative automated fall-detection framework that includes a Gaussian blur module for privacy preservation, an OpenPose module for precise pose estimation, a short-time Fourier transform (STFT) module to capture frames with significant motion selectively, and a computationally efficient one-dimensional convolutional neural network (1D-CNN) classification module designed to classify these frames. Additionally, integrating a gradient-weighted class activation mapping (GradCAM) module enhances the system ’s explainability by visually highlighting the movement of the key points, resulting in classification decisions. Modular flexibility in our system allows customization to meet specific privacy and monitoring needs, enabling the activation or deactivation of modules according to the operational requirements of different healthcare settings. This combination of STFT and 1D-CNN ensures fast and efficient processing, which is essential in healthcare environments where real-time response and accuracy are vital. We validated our approach across multiple datasets, including the Multiple Cameras Fall Dataset (MCFD), the UR fall dataset, and the NTU RGB +D Dataset, which demonstrates high accuracy in detecting falls and provides the interpretability of results.

Jiangjiao Li and Mengqi Gao and Peng Wang and Bin Li (2024) Fall detection algorithm based on pyramid network and feature fusion. Evolving Systems 15: 1957-1970 https://doi.org/10.1007/s12530-024-09601-9, Springer Nature, 10, Fall detection,Feature fusion,Gate recurrent unit,Pyramid network,Transformer, 5, 18686486, Accidental falls are the second leading cause of accidental death of the elderly. Early intervention measures can reduce the problem. However, so far, there are few related studies using Transformer coding module for fall detection feature extraction, and the real-time performance of existing algorithms is not so good. Therefore, we propose a fall detection method based on Transformer to extract spatiotemporal features. Specifically, we use an image reduction module based on a convolutional neural network to reduce the image size for computation. Then, we design a pyramid network based on an improved Transformer to extract spatial features. Finally, we design a feature fusion module that fuses spatial features of different scales. The fused features are input into the gate recurrent unit to extract time features and complete the recognition of falls and normal postures. Experimental results show that the proposed approach achieves an accuracy of 99.61% and 99.33% when tested with UR Fall Detection Dataset and Le2i Fall Detection Dataset. Compared with the state-of-the-art fall detection algorithms, our method has high accuracy while maintaining high detection speed.

Awais Khan and Jung-Yeon Kim and Chomyong Kim and Muhammad Attique Khan and Hyojin Shin and Jiyoung Woo and Yunyoung Nam (2025) Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization. Scientific Reports 15 https://doi.org/10.1038/s41598-025-11031-9, Springer Science and Business Media LLC, 8, 1, 20452322

Ali Raza and Muhammad Haroon Yousaf and Waqar Ahmad and Sergio A. Velastin and Serestina Viriri (2025) Human fall detection using pose estimation: From traditional machine learning to vision transformers. Engineering Applications of Artificial Intelligence 143 https://doi.org/10.1016/j.engappai.2024.109809, Elsevier Ltd, 3, Deep learning,Fall detection,Human pose estimation,Machine learning, 09521976, Human activity recognition research for healthcare has drawn global attention in recent era. Recent advancements have led to various approaches capable of detecting diverse movements like walking, running, jumping, and falling. Fall detection is crucial due to its potential fatality, especially for older individuals. Sensors are widely employed to perceive environmental changes, and they can be integrated into wearable devices like phones, necklaces, or wristbands. However, these devices may be uncomfortable or unsuitable for continuous use. Video imagery, in principle, surpasses wearable sensors for fall detection. The proposed method uses video frames to identify falls, reducing the need for environmental sensors. We present an empirical analysis of vision-based human fall detection, employing multiple techniques to estimate human poses including a transformer-based pose estimation technique. These techniques yield foundational features used for training diverse networks, including machine learning classifiers to vision transformers. Our methodology achieves cutting-edge outcomes across the UR-Fall, UP-Fall, and Le2i fall detection datasets.

Mingze Ma and Xiaofeng Hu (2025) A deep learning and edge computing integrated approach for fall behavior detection in buildings. Journal of Safety Science and Resilience 6 https://doi.org/10.1016/j.jnlssr.2025.100218, KeAi Communications Co., 12, Building,Deep learning,Edge computing,Fall behavior detection,STGCN,YOLOv11, 4, 26664496, This study proposes a model integrating YOLOv11 and STGCN for accurate and real-time detection of fall behaviors within buildings. A specialized video dataset comprising fall behaviors performed by six volunteers was developed and used to validate the model's effectiveness in cloud computing and edge computing environments. The results obtained in the cloud computing environment were characterized by ample computational resources and the absence of real-time constraints. The model achieved precision, recall, and F1-score for fall behaviors exceeding 0.98. The model was integrated into edge computing devices in an actual test environment to directly process real-time video stream data. A missed detection rate of 18 % was observed on the Jetson ORIN NX 16GB device, while the Jetson AGX Orin 64GB recorded a lower missed detection rate of 15 %. Similarly, a false alarm rate of 16 % was observed on the Jetson ORIN NX 16GB device and 12 % on the Jetson AGX Orin 64GB device. These performance differences between the high-performance cloud computing cluster and edge computing devices, as well as among different edge computing devices, may be attributed to variations in computational resources, data quality, and device parameters. The results demonstrate the potential of the proposed model for real-time fall detection in resource-constrained environments.

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