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Sensitivity and Specificity of Wearables for Atrial Fibrillation in Elderly Populations: A Systematic Review

CURRENT CARDIOLOGY REPORTS 2023年第7期25卷 761-779页

作者： Babar, Faiza Cheema, Abdul Manan Ahmad, Zubair Sarfraz, Azza Sarfraz, Zouina Ashraff, Huma Tayyab, Hamnah Bajwa, Ali Mustafa Ali, Haider Sarfraz, Muzna Shahzad, Anum Sabzwari, Syed Rafay Ali CMH Lahore Med Coll Lahore Pakistan Sargodha Med Coll Sargodha Pakistan Independent Med Coll Faisalabad Pakistan Aga Khan Univ Karachi Pakistan Fatima Jinnah Med Univ Lahore Pakistan Shalamar Med & Dent Coll Lahore Pakistan King Edward Med Univ Lahore Pakistan Bahria Univ Karachi Pakistan Albert Einstein Med Ctr Philadelphia PA USA Univ Colorado Aurora CO USA

Purpose of ReviewThis study aims to evaluate the sensitivity and specificity of wearable devices for AF detection in older adults, as well as examine the incidence of AF across various studies, contextual factors impacting AF detection, and safety and adverse events associated with wearable *** FindingsA systematic search of three databases identified 30 studies on wearables for AF detection in older adults, encompassing 111,798 participants. Both PPG-based and single-lead electrocardiography-based wearables show scalable potential for screening and managing *** results of this systematic review demonstrate that wearable devices, such as smartwatches, can effectively identify arrhythmias like AF in older adults, with scalable potential in PPG-based and single-lead electrocardiography-based wearables. As wearable technologies continue to gain prominence in healthcare, it is crucial to understand their challenges and incorporate them as preventative and monitoring tools for AF detection in elderly populations to improve patient care and prevention techniques.

关键词： Atrial fibrillation Wearable technology Elderly population Sensitivity and specificity detection algorithms Cardiovascular disease Stroke risk Electrocardiogram Systematic review Adverse events

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Fluctuation-enhanced gas sensing by two-dimensional materials

Fluctuation-enhanced gas sensing by two-dimensional material...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Smulko, Janusz Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

ISBN: (纸本)9798350380903;9798350380910

The perspectives of gas sensing by flicker noise measurements have been presented for two-dimensional materials characterized by a high surface-to-volume ratio. These materials exhibit a wide range of electrical properties, from almost metallic (e.g., graphene) to semiconducting (e.g., MoS2, WS2, ZrS3, etc.) of various energy bandgaps. Two-dimensional materials exhibit imperfections, partially responsible for the flicker noise generated in their structures. Recent results show how these imperfections can be modulated to advance gas selectivity. Some materials exhibit photocatalytic phenomena induced by irradiation of multiple wavelengths. This effect can adjust the gas sensor to improve its selectivity and sensitivity. Low-frequency noise mechanisms in two-dimensional materials at different operating conditions will be presented, focusing on possible practical applications. The problems of noise measurements will also be enclosed.

关键词： gas sensors two-dimensional materials flicker noise detection algorithms noise measurements

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Multi-scale traffic sign detection algorithm based on improved YOLO V4 25

Multi-scale traffic sign detection algorithm based on improv...

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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Li, Sihan Cheng, Xin Zhou, Zhou Zhao, Ben Li, Shaoqian Zhou, Jingmei Changan Univ Sch Informat Engn Xian 710064 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Changan Univ Xian Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Traffic sign detection and recognition is a key technology for intelligent assisted driving. This paper proposes a key technology for traffic sign detection and recognition based on improved YOLO V4 in complex urban scenes. The technology uses K-means++ clustering to generate an anchor that conforms to traffic signs;designs a CSPDarknet33 backbone network that greatly reduces the number of weight calculations optimizing the structure of the YOLO V4 neural network;uses Focal Loss and balanced cross entropy function to optimize the loss function of YOLO V4, and completes the traffic sign detection work. Then this paper designs an improved VGG16 neural network, which is used to train three models that recognize warning signs, prohibition signs and indication signs. Finally, according to the results of traffic sign detection, the traffic sign recognition work is further completed, the mAP is 94.48% and the FPS is 27, its detection performance is significantly higher than other networks.

关键词： Histograms Roads Neural networks Object detection Entropy detection algorithms Optimization

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Autoexplorer: Autonomous Exploration of Unknown Environments using Fast Frontier-Region detection and Parallel Path Planning

Autoexplorer: Autonomous Exploration of Unknown Environments...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Han, Kyung Min Kim, Young J. Ewha Womans Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

We propose a fully autonomous system for mobile robot exploration in unknown environments. Our system employs a novel frontier detection algorithm based on the fast front propagation (FFP) technique and uses parallel path planning to reach the detected front regions. Given an occupancy grid map in 2D, possibly updated online, our algorithm can find all the frontier points that can allow mobile robots to visit unexplored regions to maximize the exploratory coverage. Our FFP method is six similar to seven times faster than the state-of-theart wavefront frontier detection algorithm in terms of finding frontier points without compromising the detection accuracy. The speedup can be further accelerated by simplifying the map without degrading the detection accuracy. To expedite locating the optimal frontier point, We also eliminate spurious points by the obstacle filter and the novel boundary filter. In addition, we parallelize the global planning phase using the branch-andbound A*, where the search space of each thread is confined by its best knowledge discovered during the parallel search. As a result, our parallel path-planning algorithm operating on 20 threads is about 30 times faster than the vanilla exploration system that operates on a single thread. Our method is validated through extensive experiments, including autonomous robot exploration in both synthetic and real-world scenarios. In the real-world experiment, we show that an autonomous navigation system using a human-sized mobile manipulator robot equipped with a low-end embedded processor that fully integrates our FFP and parallel path-planning algorithms.

关键词： Buildings Filtering algorithms Manipulators Path planning Planning Mobile robots detection algorithms

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High-Precision Moving Target Shadow detection Algorithm for ViSAR Based on Information Geometry

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 12728-12739页

作者： Yan, Shangqu Fu, Yaowen Yu, Ruofeng Luo, Chenyang Zhang, Wenpeng Yang, Wei Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Video synthetic aperture radar (ViSAR) attracts much attention in the field of moving target detection due to its high frame rate and high-resolution imaging capability. In ViSAR, the moving target's shadow does not offset and defocus, which is widely used as a feature for moving target detection. To address the shortcomings of high false and missing alarms in existing detection algorithms, this study proposes a high-precision moving target shadow detection algorithm for ViSAR based on information geometry. First, the shadow enhancement and background suppression algorithm is used to enhance the saliency of moving targets' shadows in ViSAR images to reduce the false and missing alarms caused by complex background. Then, based on the information geometry theory, the enhanced ViSAR image is mapped to a Gaussian statistical manifold, and the Jensen-Shannon (JS) divergence matrix with pixel context information is extracted from this manifold. And the JS divergence matrix and the grayscale information from the enhanced ViSAR image are used to construct the joint probability distribution function. The 2-D minimum cross-entropy principle is used to calculate the threshold to extract the regions of interest. Finally, the final detection results are obtained by applying single-frame and multiple-frame false alarms suppression. The experimental results, based on real ViSAR dataset published by Sandia National Laboratories, verify the effectiveness of our proposed algorithm. Specifically, the Recall rate is 85.97%, the Precision is 96.22%, and the F1 score achieves 0.91.

关键词： Feature extraction Manifolds Information geometry Gray-scale detection algorithms Data mining Detectors Clutter Synthetic aperture radar Radar imaging False alarms suppression information geometry moving target shadow detection video synthetic aperture radar (ViSAR)

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Heuristic Optimization based Abnormal Posture detection Algorithm

Heuristic Optimization based Abnormal Posture Detection Algo...

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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Li, Yufeng Shang, Lin Pan, Peng Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

This paper studies the abnormal posture detection algorithm based on heuristic optimization. Using the data collected by sensors, the features such as acceleration and angular velocity are extracted and put into the classifiers for training. We select the appropriate heuristic algorithm according to different classifier models for optimization. The results demonstrate that, in binary classification experiment, the accuracy ratio of the K-Nearest Neighbor (KNN) model is 99.54%, and the AUC is 0.99. In quad classification experiment, The Support Vector Machine (SVM) model has a 94.32% accuracy ratio and a 0.95 AUC, which has the optimal performance.

关键词： Support vector machines Training Heuristic algorithms Sensor phenomena and characterization Feature extraction Data mining detection algorithms

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Anomaly Monitoring Framework in Lane detection With a Generative Adversarial Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第3期22卷 1603-1615页

作者： Kim, Hayoung Park, Jongwon Min, Kyushik Huh, Kunsoo Hanyang Univ Dept Automot Engn Seoul 04763 South Korea

The safety of an automated vehicle requires accurate information of surrounding conditions, because a false sensor output can lead to a fatal accident during driving. Thus, monitoring of abnormalities in every sensor is important for robust perception of the environment. Since it is difficult to obtain anomalous data, it is hard to develop a robust detection algorithm using only a relatively small number of anomalies. In this paper, we propose a data augmentation method for oversampling minority anomalies in lane detection. Using a generative adversarial network that makes the generator learn to estimate the distribution of anomalous data, it generates synthesized minority anomalies. The generated anomalies are used to train an anomaly detection network while minimizing latency for use in real situations. During training, the generated anomalies, with various mixed quality, are sampled differently according to their quality. This helps the detection network to be optimized with better quality data. Experimental result shows that when using the proposed anomaly detection framework for monitoring lane abnormality, it improves the performance by 12% when compared to the vanilla recurrent neural network.

关键词： Anomaly detection Monitoring Generative adversarial networks Training Gallium nitride Robustness detection algorithms Anomaly detection deep learning generative adversarial network data augmentation lane abnormality monitoring

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A YOLO-Based Intelligent detection Algorithm for Risk Assess-Ment of Construction Sites

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第4期2卷 1-18页

作者： Ruiyang Feng Yu Miao Junxing Zheng School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

Construction safety accidents have become increasingly frequent in recent years, leading to numerous casualties and substantial property losses. These in-cidents are often attributed to inadequate supervision on construction sites and workers' low safety awareness. Traditional manual management methods, which are labor-intensive and resource-consuming, are no longer effective. Therefore, this study proposes a novel single-stage model based on YOLOv8s, designed for two primary purposes: detecting workers' personal protective equipment and monitoring and recognizing when workers enter hazardous ar-eas. The model provides real-time feedback on detection results to reduce the incidence of construction accidents. Additionally, a brief design for distance calculation was introduced. The model was trained for 200 iterations on a Roboflow dataset comprising 103,500 annotated images. Experimental results showed that YOLOv8s outperformed YOLOv8n, YOLOv5s, and YOLOv5n in de-tection performance, achieving a mean average precision with the intersection over union (IoU) threshold set to 50% (mAP50) of 84.0%, precision of 85.0%, and recall of 60.5% across 9 detection classes. By leveraging artificial intelligence technology, this study aims to offer an effective method for enhancing construction site safety, which can be further improved with additional images and a more robust network architecture.

关键词： YOLO Training Adaptation models Accuracy Surveillance Refining Real-time systems Safety detection algorithms Accidents

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Modeling Human Response to Robot Errors for Timely Error detection

Modeling Human Response to Robot Errors for Timely Error Det...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Stiber, Maia Taylor, Russell Huang, Chien-Ming Johns Hopkins Univ Dept Comp Sci Baltimore MD 21205 USA IEEE Piscataway NJ USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In human-robot collaboration, robot errors are inevitable-damaging user trust, willingness to work together, and task performance. Prior work has shown that people naturally respond to robot errors socially and that in social interactions it is possible to use human responses to detect errors. However, there is little exploration in the domain of nonsocial, physical human-robot collaboration such as assembly and tool retrieval. In this work, we investigate how people's organic, social responses to robot errors may be used to enable timely automatic detection of errors in physical human-robot interactions. We conducted a data collection study to obtain facial responses to train a real-time detection algorithm and a case study to explore the generalizability of our method with different task settings and errors. Our results show that natural social responses are effective signals for timely detection and localization of robot errors even in non-social contexts and that our method is robust across a variety of task contexts, robot errors, and user responses. This work contributes to robust error detection without detailed task specifications.

关键词： Location awareness Collaboration Human-robot interaction Data collection Real-time systems Task analysis detection algorithms

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Trained and Robust Parameter Based Path Sampling for Low Complexity MIMO detection in 5G-NR 95

Trained and Robust Parameter Based Path Sampling for Low Com...

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IEEE 95th Vehicular Technology Conference: (VTC-Spring)

作者： Qian, Jing Wang, Hao Huawei Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

In this paper, a low complexity MIMO detection algorithm based on path sampling is proposed to achieve near-optimal performance with limited number of paths. Conventional list sphere decoders (LSD) have good detection performance but at cost of high computation complexity, while the random sampling (RS) based MIMO detectors may suffer from inevitable performance loss and sample repetition. To overcome these issues, we first propose a data-driven method, called the trained parameter based path sampling (TPbPS), to optimize the parameter of sampling distribution, and design an approach to improve the generalization performance. Further, we take advantages of the well-trained sampling distribution and derandomize the RS to develop the robust parameter based path sampling (RPbPS), while all the sampling paths are determined by the trained sampling distribution as well as the fixed and uniformly-distributed numbers. Combining these techniques yields an enhanced MIMO detection design that non-trivially advances the state-of-the-art, and can provide significant performance and complexity gains over the traditional LSD and RS methods.

关键词： Vehicular and wireless technologies Costs Detectors Performance gain Parity check codes Decoding detection algorithms

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