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The Journal of machine learning Research 2022年第1期23卷 4632-4660页

作者： Christoffer Löffler Christopher Mutschler Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany

Active learning prioritizes the labeling of the most informative data samples. However, the performance of active learning heuristics depends on both the structure of the underlying model architecture and the data. We propose IALE, an imitation learning scheme that imitates the selection of the best-performing expert heuristic at each stage of the learning cycle in a batch-mode pool-based setting. We use DAGGER to train a transferable policy on a dataset and later apply it to different datasets and deep classifier architectures. The policy reects on the best choices from multiple expert heuristics given the current state of the active learning process, and learns to select samples in a complementary way that unifies the expert strategies. Our experiments on well-known image datasets show that we outperform state of the art imitation learners and heuristics.

关键词： active learning deep neural networks imitation learning dataset aggregation transferable policy

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Automatic clinical gait test detection from inertial sensor data

Automatic clinical gait test detection from inertial sensor ...

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

作者： Stefan Fischer Martin Ullrich Arne Küderle Heiko Gaßner Jochen Klucken Bjoern M. Eskofier Felix Kluge Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Germany University Hospital Erlangen Germany

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.

关键词： Turning Diseases Instruments Legged locomotion Gyroscopes Hospitals Reliability

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A.I. and data-Driven Mobility at Volkswagen Financial Services AG

arXiv

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arXiv 2022年

作者： Jawed, Shayan Arif, Mofassir Ul Islam Rashed, Ahmed Madhusudhanan, Kiran Elsayed, Shereen Jameel, Mohsan Schmidt-Thieme, Lars Volk, Alexei Hintsches, Andre Kornfeld, Marlies Lange, Katrin Information Systems and Machine Learning Lab University of Hildesheim Germany Data Analytics & A.I Unit Volkswagen Financial Services AG Germany

machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financial Services (VWFS), as a market leader in vehicle leasing services, aims to leverage existing proprietary data and the latest research to enhance existing and derive new business processes. The collaboration between Information Systems and machine learning Lab (ISMLL) and VWFS serves to realize this goal. In this paper, we propose methods in the fields of recommender systems, object detection, and forecasting that enable data-driven decisions for the vehicle life-cycle at VWFS. © 2022, CC BY-NC-ND.

关键词： machine learning

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Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

TechRxiv

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TechRxiv 2023年

作者： Küderle, Arne Ullrich, Martin Roth, Nils Ollenschläger, Malte Ibrahim, Alzhraa Moradi, Hamid Richer, Robert Seifer, Ann-Kristin Zürl, Matthias Sîmpetru, Raul Herzer, Liv Prossel, Dominik Kluge, Felix Eskofier, Bjoern Friedrich-Alexander-Universität Erlangen-Nürnberg Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Germany

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A Generic and Robust System for Automated Detection of Different Classes of Arrhythmia

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Procedia Computer Science 2020年 167卷 1801-1810页

作者： Vandana Singh U. Srinivasulu Reddy G. Manju Bhargavia M.Tech (Data Analytics) National Institute of Technology Tiruchirappalli 620015 India Assistant Professor Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli 620015 India Post-Doc Department of Physics National Institute of Technology Tiruchirappalli 620015 India

Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis electrocardiography (ECG) plays an important role. The Advancement of Medical Instrumentation (AAMI) suggests, all the heartbeat of MIT-BIH dataset into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed. Then, features computed from ECG signals are RR (separation between two successive heartbeat pulses), local binary pattern(LBP), morphological changes, wavelet, high order statistics, and several amplitude values; Pre-RR, Post-RR, Local RR and Global RR. The main reason is to computing many morphological features to comparison analysis with the model when trained and tested on single features vs combining all features together. Model gives highest performance which is 90.8% accuracy using LBP as single feature compared with other features. Combined all important features together and optimal features (total 104 ) are given as an input to Single SVM classifier as well as ensembles of SVM which maps the component feature vectors to the particular class name. Principal Component Analysis (PCA) technique is also applied to the model which reduces the total number of features from 104 to 30 features. The Single SVM system can accurately classify four beat types and has an overall classification rate of 94.05% without PCA and using PCA 92.84% while the Ensemble SVM classifies and with an accuracy of 92.96%

关键词： Electrocardiogram (ECG) signal MIT-BIH dataset Wavelet Transform Support Vector machines OVO

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Supervised learning for Yaw Orientation Estimation

Supervised Learning for Yaw Orientation Estimation

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Tobias Feigl Christopher Mutschler Michael Philippsen Machine Learning and Information Fusion Group Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Programming Systems Group

With free movement and multi-user capabilities, there is demand to open up Virtual Reality (VR) for large spaces. However, the cost of accurate camera-based tracking grows with the size of the space and the number of users. No-pose (NP) tracking is cheaper, but so far it cannot accurately and stably estimate the yaw orientation of the user's head in the long-run. Our novel yaw orientation estimation combines a single inertial sensor located at the human's head with inaccurate positional tracking. We exploit that humans tend to walk in their viewing direction and that they also tolerate some orientation drift. We classify head and body motion and estimate heading drift to enable low-cost long-time stable head orientation in NP tracking on 100 m×100 m. Our evaluation shows that we estimate heading reasonably well.

关键词： Magnetic heads Feature extraction Acceleration Tracking Head Accelerometers Estimation

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Digitizing handwriting with a sensor pen: A writer-independent recognizer

arXiv

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arXiv 2021年

作者： Wehbi, Mohamad Hamann, Tim Barth, Jens Eskofier, Bjoern Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany STABILO International GmbH Heroldsberg Germany

Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a realtime handwriting recognition system to be used for writing on normal paper. © 2021, CC BY.

关键词： Character recognition

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ConvLoRA and AdaBN Based Domain Adaptation via Self-Training

ConvLoRA and AdaBN Based Domain Adaptation via Self-Training

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IEEE International Symposium on Biomedical Imaging

作者： Sidra Aleem Julia Dietlmeier Eric Arazo Suzanne Little SFI Research Centre for Machine Learning Dublin City University Ireland Insight SFI Research Centre for Data Analytics Dublin City University Ireland

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated/separate fine-tuned network for each target domain, that retain all the pre-trained model parameters, is prohibitively expensive. To address this limitation, we propose Convolutional Low-Rank Adaptation (ConvLoRA). ConvLoRA freezes pre-trained model weights, adds trainable low-rank decomposition matrices to convolutional layers, and backpropagates the gradient through these matrices thus greatly reducing the number of trainable parameters. To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA. Our method has fewer trainable parameters and performs better or on-par with large independent fine-tuned networks (with less than 0.9% trainable parameters of the total base model) when tested on the segmentation of Calgary-Campinas dataset containing brain MRI images. Our approach is simple, yet effective and can be applied to any deep learning-based architecture which uses convolutional and batch normalization layers. Code is available at: https://***/aleemsidra/ConvLoRA.

关键词： Convolutional codes Adaptation models Image segmentation Computational modeling Magnetic resonance imaging Matrix decomposition Batch normalization

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Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

arXiv

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arXiv 2018年

作者： Sheng, Leyuan Pavlovskiy, Evgeniy N. Department of Mathematics and Mechanics Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia

Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-to-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer

Digitizing Handwriting with a Sensor Pen: A Writer-Independe...

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International Workshop on Frontiers in Handwriting Recognition

作者： Mohamad Wehbi Tim Hamann Jens Barth Bjoern Eskofier Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany STABILO International GmbH Heroldsberg Germany

ISBN: (数字)9781728199665

ISBN: (纸本)9781728199672

Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a real-time handwriting recognition system to be used for writing on normal paper.

关键词： Writing Character recognition Handwriting recognition Optical character recognition software data models Training Real-time systems

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