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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 lab.l 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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Computational Approach for Respiratory Pressure Parameters in Neonatal Ventilation 24

Computational Approach for Respiratory Pressure Parameters i...

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24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Lipchak, Daria Chupov, Aleksey Dolganov, Anton Zhdanov, Aleksei Jt. Stock Co. Prod. Association Urals Optical and Mechanical Plant Named after Mr. E. S. Yalamov Medical Devices R and D Bureau Yekaterinburg Russia Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Named after the First President of Russia B.N. Yeltsin Yekaterinburg Russia University of Erlangen-Nuremberg Machine Learning and Data Analytics Lab Erlangen Germany

ISBN: (纸本)9798350336870

In the context of mechanical ventilation, the accurate calculation of secondary pressure parameters is essential for clinicians to effectively assess a patient's clinical condition. In the case of neonatal resuscitation with the T-piece, both the inspiratory and expiratory triggers are manually operated. The device provides a constant flow at the initiation of inspiration and reduces the flow to limit output pressure according to the set peak inspiratory pressure. However, without an algorithmic classification of the inspiration and expiration pressure, data analysis may only be performed postfactum. To address the need for the separate trend analysis of PIP and PEEP in non-invasive mechanical ventilation with a T-piece, there is a requirement to develop and implement an accurate algorithm for respiratory pressure parameters calculation. In this study, we review the current state-of-the-art methods for the calculation of respiratory pressure parameters for non-invasive mechanical ventilation with a T-piece. We then present the development and implementation of an algorithm for respiratory pressure parameters calculation and discuss the results of the analysis. This algorithm is designed to provide accurate and reliable measurements of PIP and PEEP in neonatal resuscitation with the T-piece, allowing clinicians to more effectively monitor and assess a patient's clinical condition during mechanical ventilation. The results demonstrate the effectiveness of the algorithm in accurately calculating PIP and PEEP values, providing valuable insights into the patient's respiratory mechanics and helping to inform clinical decision-making. © 2023 IEEE.

关键词： Resuscitation

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Don’t Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

arXiv

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arXiv 2022年

作者： Löffler, Christoffer Lai, Wei-Cheng Eskofier, Björn M. Zanca, Dario Schmidt, Lukas Mutschler, Christopher Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nuremberg Carl-Thiersch-Straße 2b Erlangen91052 Germany Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Positioning and Networks Division Erlangen Germany

The correct interpretation and understanding of deep learning models are essential in many applications. Explanatory visual interpretation approaches for image, and natural language processing allow domain experts to validate and understand almost any deep learning model. However, they fall short when generalizing to arbitrary time series, which is inherently less intuitive and more diverse. Whether a visualization explains valid reasoning or captures the actual features is difficult to judge. Hence, instead of blind trust, we need an objective evaluation to obtain trustworthy quality metrics. We propose a framework of six orthogonal metrics for gradient-, propagation- or perturbation-based post-hoc visual interpretation methods for time series classification and segmentation tasks. An experimental study includes popular neural network architectures for time series and nine visual interpretation methods. We evaluate the visual interpretation methods with diverse datasets from the UCR repository and a complex, real-world dataset and study the influence of standard regularization techniques during training. We show that none of the methods consistently outperforms others on all metrics, while some are sometimes ahead. Our insights and recommendations allow experts to choose suitable visualization techniques for the model and task. © 2022, CC BY.

关键词： Time series

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Accuracy-Aware Compression of Channel Impulse Responses using Deep learning

Accuracy-Aware Compression of Channel Impulse Responses usin...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Thomas Altstidl Sebastian Kram Oskar Herrmann Maximilian Stahlke Tobias Feigl Christopher Mutschler Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Machine Learning and Data Analytics Lab Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Germany Institute of Information Technology Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Germany

Ultra-wideband (UWB) systems based on Channel State Information (CSI) estimate the position of mobile nodes within an environment by using Channel Impulse Responses (CIRs) of multiple stationary nodes. These contain spatial information caused by environment interactions such as reflections and scattering. To estimate positions from CSI of stationary nodes, we must transmit them to a centralized node. This introduces considerable communication *** present a large-scale study to determine whether CSI can be compressed into a small set of underlying latent variables that describe the most valuable information. We evaluate multiple neural network architectures containing encoding (compressing) and decoding (reconstructing) components and compare them to the state-of-the-art compression techniques Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). We show that fully connected autoencoders achieve the lowest error, outperforming both DCT and DWT. Further experiments prove that the reconstructed CSI can be used for positioning with only mild performance deterioration at a compression of >97% and even when trained on a different environment.

关键词： Training Neural networks Scattering Transforms Reflection Discrete wavelet transforms Decoding

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Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using machine learning

Prediction of Stress Coping Capabilities from Nightly Heart ...

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

作者： Linda Vorberg Siri Pflueger Robert Richer Katharina M. Jaeger Arne Küderle Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Stress is related to short- and long-term alterations in stress systems, including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). While it is well established that stress experienced during the day can affect sleep quality, less is known about how it affects stress systems during the night. We assume that stress coping strategies can have an impact on how stress carries over into the night and that individuals with bad coping mechanisms show elevated activation of stress systems during sleep. For that reason, we recorded the heart rate (HR) and heart rate variability (HRV) of 21 healthy participants on two consecutive nights during sleep and the first hour after awakening and extracted cortisol and alpha-amylase from saliva samples collected in the first hour after awakening. Stress coping capabilities were assessed using self-reports. We performed backward stepwise multiple regression models to analyze the relationship between HR(V) and stress coping and trained different machine learning-based regression algorithms to predict positive (SVF Pos ) and negative (SVF Neg ) stress coping capabilities, respectively. Our results show that individuals with higher SVF Neg scores showed higher SNS activity during the night, whereas higher SVF Pos scores indicated lower SNS activity. SVF Pos was predicted with a mean absolute error (MAE) of 1.51 ± 0.73 and SVF Neg with an MAE of 2.79 ± 1.53. Our findings indicate an association between nightly HR(V) and the individual’s capability of coping with stress. This provides further information about how stress influences sleep and might be used for tailored intervention and feedback on successful stress coping.

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SVC-onGoing: Signature Verification Competition

arXiv

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arXiv 2021年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Gonzalez-Garcia, Carlos Fierrez, Julian Morales, Aythami Ortega-Garcia, Javier Ruiz-Garcia, Juan Carlos Romero-Tapiador, Sergio Rengifo, Santiago Caruana, Miguel Jiang, Jiajia Lai, Songxuan Jin, Lianwen Zhu, Yecheng Galbally, Javier Diaz, Moises Ferrer, Miguel Angel Gomez-Barrero, Marta Hodashinsky, Ilya Sarin, Konstantin Slezkin, Artem Bardamova, Marina Svetlakov, Mikhail Saleem, Mohammad Szücs, Cintia Lia Kovari, Bence Pulsmeyer, Falk Wehbi, Mohamad Zanca, Dario Ahmad, Sumaiya Mishra, Sarthak Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Spain South China University of Technology China European Commission - Joint Research Centre Italy Universidad de las Palmas de Gran Canaria Spain Hochschule Ansbach Germany Tomsk State University of Control Systems and Radioelectronics Russia Budapest University of Technology and Economics Hungary Machine Learning and Data Analytics Lab FAU Germany Jamia Millia Islamia India

This article presents SVC-onGoing, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future studies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition. © 2021, CC BY-NC-ND.

关键词： Deep learning

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