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Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

作者： Faisal B. Albasu Subhankar Dey Anton Yu. Dolganov Oussama EL Hamzaoui Wisam M. Mustafa Aleksei E. Zhdanov Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Yekaterinburg Russia Machine Learning and Data Analytics Lab University of Erlangen-Nuremberg Erlangen Germany

Electroretinography is an established electrophysiological technique for clinical investigation of visual health function. The test involves the non-invasive assessment of the retinal function using light stimulation. This study presents a comprehensive database of five types of signals, including Scotopic 2.0 ERG response, Photopic 2.0 ERG response, Maximum 2.0 ERG response, Photopic 2.0 EGR Flicker response and Scotopic 2.0 ERG Oscillatory Potentials. The database includes 1975 signals from 323 patients, consisting of both adults and children. This study seeks to evaluate and analyze the classical parameters of three of the five signals, specifically Scotopic 2.0 ERG response, Photopic 2.0 ERG response, and Maximum 2.0 ERG response, using time-domain analysis. Correlation analysis was conducted in order to gain insights into the potential relationship between a patient's age and the classical parameters. However, the results suggest little to no correlation between these factors. The database presented in this study will serve as a useful resource for further research in the field of electrophysiology. Future research in this area could explore the use of other advanced analysis methods, such as frequency and time-frequency domain analyses, in order to gain further insights into the nuances of these signals and to better differentiate between healthy patients and those with pathologies. Additionally, further research is needed to establish norms for pediatric signals in order to better understand retinal health in children.

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NC-ALG: Graph-Based Active learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Smart Annotation Tool for Multi-sensor Gait-based Daily Activity data

Smart Annotation Tool for Multi-sensor Gait-based Daily Acti...

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IEEE International Conference on Pervasive Computing and Communications

作者： Christine F. Martindale Nils Roth Julius Hannink Sebastijan Sprager Bjoern M. Eskofier Machine Learning and Data Analytics Lab Computer Science Department Friedrich-Alexander University Erlangen-Nürnberg Germany Faculty of Computer and Information Science University of Ljubljana Slovenia

ISBN: (纸本)9781538632284

The monitoring of patients within a natural, home environment is important in order to close knowledge gaps in the treatment and care of neurodegenerative diseases, such as quantifying the daily fluctuation of Parkinson's patients' symptoms. The combination of machine learning algorithms and wearable sensors for gait analysis is becoming capable of achieving this. However, these algorithms require large, labelled, realistic datasets for training. Most systems used as a ground truth for labelling are restricted to the laboratory environment, as well as being large and expensive. We propose a study design for a realistic activity monitoring dataset, collected with inertial measurement units, pressure insoles and cameras. It is not restricted by a fixed location or capture volume and still enables the labelling of gait phases or, where non-gait movement such as jumping occur: on-the-ground, off-the-ground phases. Additionally, this paper proposes a smart annotation tool which reduces annotation cost by more than 80%. This smart annotation is based on edge detection within the pressure sensor signal. The tool also enables annotators to perform assisted correction of these labels in a post-processing step. This system enables the collection and labelling of large, fairly realistic datasets where 93% of the automatically generated labels are correct and only an additional 10% need to be inserted manually. Our tool and protocol, as a whole, will be useful for efficiently collecting the large datasets needed for training and validation of algorithms capable of cyclic human motion analysis in natural environments.

关键词： Image edge detection Tools Cameras labeling Pressure sensors Intelligent sensors Edge detection Pressure Sensor Device Component Cameras intelligent sensors ANNOTATIONS laboratory environment dataset Wearable sensors Measurement units Research Design INSOLE Packaging

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Achieving Efficient and Realistic Full-Radar Simulations and Automatic data Annotation by Exploiting Ray Meta data from a Radar Ray Tracing Simulator

Achieving Efficient and Realistic Full-Radar Simulations and...

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IEEE National Conference on Radar

作者： Christian Schüβler Marcel Hoffmann Vanessa Wirth Björn Eskofier Tim Weyrich Marc Stamminger Martin Vossiek Institute of Microwaves and Photonics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Chair of Visual Computing Machine Learning and Data Analytics Lab Chair of Digital Reality

In this work, a novel radar simulation concept for efficiently simulating realistic radar data for range, Doppler, and arbitrary antenna positions is introduced. With the concept, the simulated radar signal can also be automatically annotated by splitting it into multiple parts. Annotations that are almost perfect - including the annotation of exotic effects, such as multi-path - can be produced with this approach. Signal parts originating from different parts of an object can be labelled with it as well. To this end, the computation process used in a Monte Carlo shooting and bouncing rays (SBR) simulator is adapted. By considering the hits of each simulated ray, various meta data can be stored, such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict path-length changes caused by object motion to obtain Doppler information or to apply specific ray filter rules to obtain radar signals that only fulfil specific conditions, such as multiple bounces, or signals that contain specific object IDs. Using this approach, perfect, and otherwise almost impossible, annotation schemes can be realized.

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Correction: contactless radar-based heart rate Estimation in palliative care- a feasibility study and possible use in symptom management

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BMC palliative care 2025年第1期24卷 113页

作者： Stefan G Grießhammer Anke Malessa Hui Lu Julia Yip Julie Leuschner Florian Christgau Nils C Albrecht Marie Oesten Thanh Truc Tran Robert Richer Maria Heckel Bjoern M Eskofier Alexander Koelpin Tobias Steigleder Christoph Ostgathe Department of Palliative Medicine Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. stefan.griesshammer@uk-erlangen.de. Department of Palliative Medicine Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. Institute of High-Frequency Technology Hamburg University of Technology (TUHH) Hamburg Germany. Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus-Senftenberg Cottbus Germany. Machine Learning and Data Analytics Lab Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU) Erlangen Germany.

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End-to-End learning for Human Pose Estimation from Raw Millimeter Wave Radar data

End-to-End Learning for Human Pose Estimation from Raw Milli...

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Asilomar Conference on Signals, Systems & Computers

作者： Jonas Mueller Lukas Engel Daniel Krauss Ingrid Ullmann Martin Vossiek Bjoern M. Eskofier Eva Dorschky Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnber Erlangen Germany Institute of Microwaves and Photonics Friedrich-Alexander-Universität Erlangen-Nürnber Erlangen Germany

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Millimeter wave radar has gained considerable attention for its potential to analyze human motion, for example, within home environments. Here, radar offers a privacy-preserving and lighting-insensitive alternative to vision-based systems, particularly in smart home applications such as gesture control or human activity monitoring. Current methods involve pre-processing the raw radar data through multiple Fourier transforms, extracting essential parameters such as range, Doppler, azimuth, and elevation information, and applying a constant false alarm rate algorithm for point cloud extraction. However, this approach relies on numerous hyperparameters, requires expert knowledge, and may result in information loss. Our novel approach involves end-to-end learning of neural networks, resulting in a trainable, data-driven, and sparse method. This method automatically extracts important features from the raw radar data, which increases overall accuracy. First, a neural network architecture incorporating attention mechanism is introduced to train an encoder to represent raw radar data as features in an informative low-dimensional vector space. Second, the mean per joint position error of human poses estimated from these features is evaluated using a dataset encompassing a diverse range of human motions. Third, the results are discussed, and conclusions about large-scale attention-based modeling in the radar-based human pose estimation domain are given.

关键词： Adaptation models Attention mechanisms Pose estimation Neural networks Radar Millimeter wave radar Feature extraction Sensors Doppler radar data mining

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Achieving Efficient and Realistic Full-Radar Simulations and Automatic data Annotation by exploiting Ray Meta data of a Radar Ray Tracing Simulator

arXiv

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arXiv 2023年

作者： Schüßler, Christian Hoffmann, Marcel Wirth, Vanessa Eskofier, Björn Weyrich, Tim Stamminger, Marc Vossiek, Martin Institute of Microwaves and Photonics Germany Chair of Visual Computing Germany Chair of Digital Reality Germany Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

In this work a novel radar simulation concept is introduced that allows to simulate realistic radar data for Range, Doppler, and for arbitrary antenna positions in an efficient way. Further, it makes it possible to automatically annotate the simulated radar signal by allowing to decompose it into different parts. This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object. This is possible by adapting the computation process of a Monte Carlo shooting and bouncing rays (SBR) simulator. By considering the hits of each simulated ray, various meta data can be stored such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict the change of path lengths introduced by object motion to obtain Doppler information or to apply specific ray filter rules in order obtain radar signals that only fulfil specific conditions, such as multiple bounces or containing specific object IDs. Using this approach, perfect and otherwise almost impossible annotations schemes can be realized. Copyright © 2023, The Authors. All rights reserved.

关键词： Metadata

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Unsupervised harmonic frequency-based gait sequence detection for Parkinson's disease

Unsupervised harmonic frequency-based gait sequence detectio...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Martin Ullrich Julius Hannink Heiko Gaßner Jochen Klucken Bjoern M. Eskofier Felix Kluge Machine Learning and Data Analytics Lab University of Erlangen-Nürnberg (FAU) Erlangen Germany Portabiles HealthCare Technologies GmbH Erlangen Germany Department of Molecular Neurology University Hospital Erlangen Erlangen Germany

Sensor-based gait analysis is a valuable tool in diagnosis and assessment of Parkinson's disease. Especially for large data sets, efficient analysis pipelines are required. Pre-segmentation of long time series into chunks of interest is a possible approach to increase efficiency. Therefore, we developed an unsupervised algorithm for the detection of gait sequences from continuous sensor signals. In the proposed method, gyroscope signals representing the angular rate of the feet are analyzed in the frequency domain using moving windows. A gait sequence was detected, if the frequency spectrum of a given window contained harmonic frequencies. The approach was tested on two data sets that differed in the ratio of clinical gait and cyclic movement tests. Sensitivity in both data sets was higher than 99% in a stride-to-stride comparison with ground truth. The specificity was measured with 76.1% (data set 1) and 94.5% (data set 2) for tests against sequences of other cyclic movements. In conclusion, the algorithm offers a reliable and efficient approach for the detection of gait sequences in time series data and is also promising for the application in long-term home-monitoring scenarios.

关键词： Harmonic analysis Sensitivity Foot Legged locomotion Frequency measurement Parkinson's disease Hospitals

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Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

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Revue d'Intelligence Artificielle 2019年第6期33卷 461-466页

作者： Reddy Chirra, Venkata Rami Uyyala, Srinivasulu Reddy Kishore Kolli, Venkata Krishna Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Department of Computer Science and Engineering VFSTR Guntur522213 India

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

关键词： Face recognition

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