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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Markus Wirth Stefan Gradl Wolfgang A. Mehringer Richard Kulpa Hannes Rupprecht Dino Poimann Annemarie F. Laudanski Bjoern M. Eskofier Machine Learning & Data Analytics Univ. of Erlangen-Nürnberg (FAU) Erlangen Germany MM2S Lab University Rennes 2 Rennes France FC Red Bull Salzburg Academy Salzburg Austria Department of Kinesiology University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Assessment of personality traits is highly relevant in team sports in order to analyze the performance of an athlete under pressure when in competitive situations, for team-strategic decisions, to optimize command transmission, and ultimately to understand top-level performers. It further facilitates the development and application of personalized exercises, coaching to improve performance in competition, and can be considered a valuable criterion for talent scouting and development. The current state of the art method to assess personality traits in sports relies on validated questionnaires. However, these often provide non-sport-specific, subjective self-reported information and lack the ability to measure how these characteristics are reflected in context-based *** developed a virtual reality (VR) tool for the assessment of personality traits in team sports, in our case for soccer. An evaluation of this tool within a study with 24 subjects yielded a benchmark of its immersion through user experience and provided an objective description of athletes’ personalities based on performance indicators extracted from activity-tracking. Within the tool, we implemented two realistic virtual soccer environments to assess the motivational orientation of soccer players (i.e. action- and state-orientation) which we discerned from the gold standard *** show that user experience and presence of the implemented virtual environments scored significantly higher compared to benchmark measurements. Additionally, a significant difference between the two groups of action and state-oriented athletes could be observed. Measures of failure rate, pass accuracy, number of perceived opponents, and achieved bonus goals are parameters that differ significantly among the two athlete groups. These findings show that VR technology is applicable for the assessment of athletes’ motivational orientation and thus demonstrate the feasibility of virtual environments as func

关键词： Training Task analysis Tools Decision making Games Virtual reality

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WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography

WebPPG: Feasibility and Usability of Self-Performed, Browser...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Michael Nissen Madeleine Flaucher Katharina M. Jaeger Hanna Huebner Nina Danzberger Adriana Titzmann Constanza A. Pontones Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser’s insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical *** relevance— WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation

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Correction to: Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards?

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European geriatric medicine 2022年第4期13卷 825-826页

作者： Malte Ollenschläger Felix Kluge Matthias Müller-Schulz Rupert Püllen Claudia Möller Jochen Klucken Bjoern M Eskofier Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Carl-Thiersch-Str. 2b 91052 Erlangen Germany. malte.ollenschlaeger@fau.de. Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Carl-Thiersch-Str. 2b 91052 Erlangen Germany. AGAPLESION DIAKONIEKLINIKUM HAMBURG Hamburg Germany. AGAPLESION MARKUS KRANKENHAUS Frankfurt am Main Germany. AGAPLESION gAG Frankfurt am Main Germany. Centre Hospitalier de Luxembourg Luxembourg Institute of Health University of Luxembourg Esch-sur-Alzette Luxembourg.

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NC-ALG: Graph-Based Active learning Under Noisy Crowd

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

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International Conference on data Engineering

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

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

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.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks labeling Noise measurement

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XPASC: Measuring Generalization in Weak Supervision by Explainability and Association

arXiv

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arXiv 2022年

作者： März, Luisa Asgari, Ehsaneddin Braune, Fabienne Zimmermann, Franziska Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria AI Innovation & Pre-Development Data:Lab Volkswagen AG Munich Germany UniVie Docotoral School Computer Science Vienna Austria CAPE Analytics Mountain ViewCA United States

Weak supervision is leveraged in a wide range of domains and tasks due to its ability to create massive amounts of labeled data, requiring only little manual effort. Standard approaches use labeling functions to specify signals that are relevant for the labeling. It has been conjectured that weakly supervised models over-rely on those signals and as a result suffer from overfitting. To verify this assumption, we introduce a novel method, XPASC (eXPlainability-Association SCore), for measuring the generalization of a model trained with a weakly supervised dataset. Considering the occurrences of features, classes and labeling functions in a dataset, XPASC takes into account the relevance of each feature for the predictions (explainability) of the model as well as the connection of the feature with the class and the labeling function (association), respectively. The explainability is measured using occlusion in this work. The association in XPASC can be measured in two variants: XPASC-CHI SQUARE measures associations relative to their statistical significance, while XPASC-PPMI measures association strength more generally. We use XPASC to analyze KNOWMAN, an adversarial architecture intended to control the degree of generalization from the labeling functions and thus to mitigate the problem of overfitting. On one hand, we show that KNOWMAN is able to control the degree of generalization through a hyperparameter. On the other hand, results and qualitative analysis show that generalization and performance do not relate one-to-one, and that the highest degree of generalization does not necessarily imply the best performance. Therefore methods that allow for controlling the amount of generalization can achieve the right degree of benign overfitting. Our contributions in this study are i) the XPASC score to measure generalization in weakly-supervised models, ii) evaluation of XPASC across datasets and models and iii) the release of the XPASC implementation. © 2022, CC BY.

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Automated Long-Term Monitoring of Stereotypical Movement in Polar Bears Under Human Care Using machine learning

SSRN

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SSRN 2024年

作者： Zuerl, Matthias Stoll, Philip Brehm, Ingrid Sueskind, Jonas Raab, René Petermann, Jan Zanca, Dario Simon, Ralph von Fersen, Lorenzo Eskofier, Bjoern Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91052 Germany Animal Physiology Department of Biology Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Behavioral Ecology and Conservation Lab Nuremberg Zoo Nuremberg90480 Germany

The welfare of captive animals is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant *** behaviour monitoring methods, however, are labour-intensive and subject to observer *** study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in captive animals, exemplified by a longitudinal study of two polar *** designed an animal tracking framework to localise and identify individual animals in the *** determining their position on the enclosure map via homographic transformation, we cleaned the resulting trajectories using a particle ***, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %.The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological *** work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated monitoring of stereotypical behaviour, paving the way for its application across different species and settings. © 2024, The Authors. All rights reserved.

关键词： Enclosures

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Detecting Respiratory Effort-Related Arousals in Polysomnographic data Using LSTM Networks

Detecting Respiratory Effort-Related Arousals in Polysomnogr...

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Computers in Cardiology (CinC)

作者： Sven Schellenberger Kilin Shi Melanie Mai Jan P Wiedemann Tobias Steigleder Björn Eskofier Robert Weigel Alexander Kölpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute for Electronics Engineering Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany Department of Palliative Care Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9781728109244

To diagnose sleep disorders, hours of sleep data from lots of different physiological sensors have to be analyzed. To do so, experts have to look through all the data which is time-consuming and error-prone. Automatic detection and classification of sleep related breathing disorders and arousals would significantly simplify this task. This years Physionet/CinC Challenge deals with this topic. This paper examines the use of a Long Short-Term Memory network for automatic arousal detection. On the test set, an AUPRC score of 0.14 was achieved.

关键词： Training Feature extraction Sleep Electroencephalography Physiology Electromyography Electrocardiography

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization

BIM: Improving Graph Neural Networks with Balanced Influence...

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International Conference on data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings

A Lightweight Robust Approach for Automatic Heart Murmurs an...

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Computers in Cardiology (CinC)

作者： Hui Lu Julia Beatriz Yip Tobias Steigleder Stefan Grießhammer Maria Heckel Naga Venkata Sai Jitin Jami Bjoern Eskofier Christoph Ostgathe Alexander Koelpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute of High-Frequency Technology Hamburg University of Technology Hamburg Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Cardiac auscultation provides an efficient and cost-effective way for cardiac disease pre-screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs and clinical outcomes with heart sound recordings from multiple auscultation locations. Our team HearHeart proposed a lightweight convolutional neural network (CNN) to detect heart murmurs and a random forest model to classify clinical outcomes. 128 Melspectrogram features and wide features like the socio-demographic data and statistical features are extracted. Different techniques are employed to migrate the data imbalance and model the overfitting problem. We used two data augmentation methods, noise injection and spectrogram augmentation in time and frequency domain to increase the training samples and avoid overfitting during training. Besides, weighted loss functions are applied to both tasks to deal with data imbalance. In the end, we ensembled the models from cross-validation and used voting for the final classification. We achieved a murmur score of 0.791, and a clinical outcome score of 11731.64 on 5-fold cross-validation in the hidden validation set. While on the hidden test set, we achieved a murmur score of 0.780, and a clinical outcome score of 12110, placing our team 1st and 10th in the challenge tasks, respectively.

关键词： Heart Training Frequency-domain analysis Feature extraction data models Recording Convolutional neural networks

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