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

Salimi, Mohammadamin; Machado, J.J.M.; Tavares, João Manuel R. S.

doi:10.3390/s22124544

Cited by 18 publications

(12 citation statements)

References 28 publications

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“…In pooling layer, a process called sub-sampling is used to obtain more detailed feature maps at a lower resolution. The fully connected layer generally comes before the output layer to forward features to final classification phase [ 71 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Bostancı

Koçak

Ünal

et al. 2023

Sensors

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Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Bostancı

Koçak

Ünal

et al. 2023

Sensors

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“…Their efficiency and accuracy are verified through specific case studies that identify assaults and falls. Meanwhile, studies on pedestrian abnormal behavior analysis methods applicable to GPU-equipped servers are described in [14][15][16][17]. Reference [14] proposes a lingering behavior detection method based on pedestrian activity area classification, which dynamically calculates pedestrian activity areas to classify lingering behavior into three categories and uses an algorithm that can accurately and robustly detect different types of lingering behavior without complex trajectory computation.…”

Section: Pedestrian Abnormal Behavior Detection In Surveillance Videosmentioning

confidence: 99%

“…By utilizing 3D convolutional neural networks, we develop a methodology to detect and predict violent behavior in real time, especially in public spaces, which can contribute to the early detection of and response to violent incidents. Reference [17] explores the use of deep neural networks based on pose estimation to detect falls in human objects. The research focuses on developing high-performance algorithms to analyze human posture in real time and accurately identify abnormal movements such as falls.…”

Section: Pedestrian Abnormal Behavior Detection In Surveillance Videosmentioning

confidence: 99%

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Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTV Environments

Song,

Nang

2024

Applied Sciences

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As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

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“…Sallmi et. Al [4] proposes a combined Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1 Dimensional Convolutional Neural Network (1D-CNN) model to detect human fall action based on the fast pose estimation method. Currently, there are many human fall related tools and datasets.…”

Section: Related Workmentioning

confidence: 99%

Multiple feature fusion algorithm for human fall detection in intelligent monitor video

Tang¹,

Qiu²,

Wang³

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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Human fall detection is main abnormal event in intelligent power distribution network monitor scenario. Due to diverse body postures and human actions before human fall, it is difficult to detect human fall action effectively. To solve this problem, we studied five human fall-related features and proposed multiple feature fusion algorithm to detect human fall action in intelligent monitor video. Experiment results and field test both demonstrated its effectiveness of the proposed algorithm.

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

Cited by 18 publications

References 28 publications

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTV Environments

Multiple feature fusion algorithm for human fall detection in intelligent monitor video

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