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Identifying and planning for individualized change: Patient-provider collaboration using lightweight food diaries in healthy eating and irritable bowel syndrome

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第1期3卷 1–27页

作者： Chung, Chia-Fang Wang, Qiaosi Schroeder, Jessica Cole, Allison Zia, Jasmine Fogarty, James Munson, Sean A. Informatics Indiana University Bloomington BloomingtonIN47405 United States School of Interactive Computing Georgia Institute of Technology AtlantaGA30332 United States Paul G. Allen School of Computer Science and Engineering DUB Group University of Washington SeattleWA98195 United States Family Medicine University of Washington SeattleWA98195 United States Division of Gastroenterology University of Washington SeattleWA98195 United States Human Centered Design and Engineering DUB Group University of Washington SeattleWA98195 United States

Identifying and planning strategies that support a healthy lifestyle or manage a chronic disease often require patient-provider collaboration. For example, people with healthy eating goals often share everyday food, exercise, or sleep data with health coaches or nutritionists to find opportunities for change, and patients with irritable bowel syndrome (IBS) often gather food and symptom data as part of working with providers to diagnose and manage symptoms. However, a lack of effective support often prevents health experts from reviewing large amounts of data in time-constrained visits, prevents focusing on individual goals, and prevents generating correct, individualized, and actionable recommendations. To examine how to design photo-based diaries to help people and health experts exchange knowledge and focus on collaboration goals when reviewing the data together, we designed and developed Foodprint, a photo-based food diary. Foodprint includes three components: (1) A mobile app supporting lightweight data collection, (2) a web app with photo-based visualization and quantitative visualizations supporting collaborative reflection, and (3) a pre-visit note communicating an individual's expectations and questions to experts. We deployed Foodprint in two studies: (1) with 17 people with healthy eating goals and 7 health experts, and (2) with 16 IBS patients and 8 health experts. Building upon the lens of boundary negotiating artifacts and findings from two field studies, our research contributes design principles to (1) prepare individuals to collect data relevant to their health goals and for collaboration, (2) help health experts focus on an individual's eating context, experiences, and goals in collaborative review, and (3) support individuals and experts to develop individualized, actionable plans and strategies. © 2019 Association for computing Machinery.

关键词： Data acquisition

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Comparative Study of Masking and Mapping Based on Hierarchical Extreme Learning Machine for Speech Enhancement

Comparative Study of Masking and Mapping Based on Hierarchic...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Ryandhimas E. Zezario Join W. C. Sigalingging Tassadaq Hussain Jia-Ching Wang Yu Tsao Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Central University Taoyuan Taiwan Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan

In this study, we compare the speech enhancement performance based on hierarchical extreme learning machine (HELM) with two distinct strategies: masking and mapping. Experimental results of the perceptual evaluation of speech quality (PESQ) show that for both of limited and sufficient amounts of training data, mapping-based HELM tends to be more effective to improve the performance of speech enhancement.

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Industrial topics in urban labor system

arXiv

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arXiv 2020年

作者： Park, Jaehyuk Frank, Morgan R. Sun, Lijun Youn, Hyejin Kellogg School of Management Northwestern University EvanstonIL United States Northwestern Institute on Complex Systems EvanstonIL United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PittsburgePA United States Connection Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Digital Economy Lab Institute for Human-Centered AI Stanford University StanfordCA United States Department of Civil Engineering McGill University MontrealQC Canada Department of Civil and Environmental Engineering Northwestern University EvanstonIL60208 United States

Categorization is an essential component for us to understand the world for ourselves and to communicate it collectively. It is therefore important to recognize that classification systems is not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented. Out-of-date classification systems would potentially limit further understanding of the current economy because things constantly change. Here, we develop an occupation-based classification system for the US labor economy, called industrial topics, that satisfy adaptability and representability. By leveraging the distributions of occupations across the US urban areas, we identify industrial topics — clusters of occupations based on their co-existence pattern. Industrial topics indicate the mechanisms under the systematic allocation of different occupations. Considering the densely connected occupations as an industrial topic, our approach characterizes regional economies by their topical composition. Unlike the existing survey-based top-down approach, our method provides timely information about the underlying structure of the regional economy, which is critical for policymakers and business leaders especially in our fast-changing economy. Copyright © 2020, The Authors. All rights reserved.

关键词： Employment

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P 011—Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements

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Gait and Posture 2018年 65卷 249-250页

作者： Slijepcevic, D. Zeppelzauer, M. Schwab, C. Raberger, A.-M. Dumphart, B. Baca, A. Breiteneder, C. Horsak, B. St. Pölten University of Applied Sciences Institute of Creative Media Technologies St. Pölten Austria St. Pölten University of Applied Sciences Institute of Health Sciences St. Pölten Austria University of Vienna Department of Biomechanics Kinesiology and Computer Science in Sport Vienna Austria TU Wien Institute of Visual Computing and Human-Centered Technology Vienna Austria

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On the understanding and interpretation of machine learning predictions in clinical gait analysis using explainable artificial intelligence

arXiv

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arXiv 2019年

作者： Horst, F. Slijepcevic, D. Lapuschkin, S. Raberger, A.-M. Zeppelzauer, M. Samek, W. Breiteneder, C. Schollhorn, W.I. Horsak, B. Department of Training and Movement Science Institute of Sport Science Johannes Gutenberg-University Mainz Mainz Germany Department of Media and Digital Technologies Institute of Creative Media Technologies St. POlten University of Applied Sciences St. POlten Austria Department of Video Coding and Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Department of Health Sciences Institute of Health Sciences St. POlten University of Applied Sciences St. Polten Austria Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria

Systems incorporating Artificial Intelligence (AI) and machine learning (ML) techniques are increasingly used to guide decision-making in the healthcare sector. While AI-based systems provide powerful and promising results with regard to their classification and prediction accuracy (e.g., in differentiating between different disorders in human gait), most share a central limitation, namely their black-box character. Understanding which features classification models learn, whether they are meaningful and consequently whether their decisions are trustworthy is difficult and often impossible to comprehend. This severely hampers their applicability as decisionsupport systems in clinical practice. There is a strong need for AI-based systems to provide transparency and justification of predictions, which are necessary also for ethical and legal compliance. As a consequence, in recent years the field of explainable AI (XAI) has gained increasing importance. XAI focuses on the development of methods that enhance transparency and interpretability of complex ML models, such as Deep (Convolutional) Neural Networks. The primary aim of this article is to investigate whether XAI methods can enhance transparency, explainability and interpretability of predictions in automated clinical gait classification. We utilize a dataset comprising bilateral three-dimensional ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls. In our experiments, we included several gait classification tasks, employed a representative set of classification methods, and a well-established XAI method-Layer-wise Relevance Propagation (LRP)-to explain decisions at the signal (input) level. The classification results are analyzed, compared and interpreted in terms of classification accuracy and relevance of input values for specific decisions. The decomposed input relevance information are evaluated from a statistical (using Statistical Parameter

关键词： Transparency

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Improving Communication in Risk Management of Health Information technology Systems by means of Medical Text Simplification

Improving Communication in Risk Management of Health Informa...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Silvana Togneri MacMahon Marco Alfano Biagio Lenzitti Giosuè Lo Bosco Fergal McCaffery Davide Taibi Markus Helfert Department of Visual and Human Centred Computing Dundalk Institute of Technology Dublin Road Dundalk Ireland School of Computing Dublin City University Glasnevin Dublin 9 Ireland Dipartimento di Matematica e Informatica Università di Palermo Italy Istituto per le Tecnologie Didattiche Consiglio Nazionale delle Ricerche Palermo Italy

Health Information technology Systems (HITS) are increasingly used to improve the quality of patient care while reducing costs. These systems have been developed in response to the changing models of care to an ongoing relationship between patient and care team, supported by the use of technology due to the increased instance of chronic disease. However, the use of HITS may increase the risk to patient safety and security. While standards can be used to address and manage these risks, significant communication problems exist between experts working in different departments. These departments operate in silos often leading to communication breakdowns. For example, risk management stakeholders who are not clinicians may struggle to understand, define and manage risks associated with these systems when talking to medical professionals as they do not understand medical terminology or the associated care processes. In order to overcome this communication problem, we propose the use of the “Three Amigos” approach together with the use of the SIMPLE tool that has been developed to assist patients in understanding medical terms. This paper examines how the “Three Amigos” approach and the SIMPLE tool can be used to improve estimation of severity of risk by non-clinical risk management stakeholders and provides a practical example of their use in a ten step risk management process.

关键词： Risk management Stakeholders IEC Standards Safety Tools Medical devices

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In-Silico Ear Model Based on Episcopic Images for Percutaneous Auricular Vagus Nerve Stimulation

In-Silico Ear Model Based on Episcopic Images for Percutaneo...

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EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields

作者： Babak Dabiri Razlighi Stefan Kampusch Stefan H. Geyer Van Hoang Le Florian Thurk Simon Brenner Jozsef Constantin Szeles Wolfgang J. Weninger Eugenijus Kaniusas Institute of Electrodynamics Microwave and Circuit Engineering Vienna University of Technology Division of Anatomy MIC Medical University of Vienna Institute of Visual Computing & Human-Centered Technology Computer Vision Lab Vienna University of Technology Department for Vascular Surgery University Clinic for Surgery Medical University of Vienna

ISBN: (纸本)9781538659229

Auricular branch of vagus nerve stimulation is continuously evolving as a minimal-invasive treatment option in patients suffering from chronic pain. For optimization of stimulation mechanisms, we conducted insilico modelling of the electrode-tissue-nerve interface by a thorough literature review of the anatomical structures combined with nerve and vessel structures from episcopic images of a human ear. Four nerves and two vessels in the ear were implemented in a 3D CAD model for future optimization of stimulation regions and patterns. Mean diameters for nerves were 30 μm, and for vessels 100 μm. Mean distance between nerves and vessels was about 100 μm.

关键词： Auricular Vagus Nerve Stimulation In-Silico Modelling Episcopic Imaging

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Handwritten Music Object Detection: Open Issues and Baseline Results

Handwritten Music Object Detection: Open Issues and Baseline...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Alexander Pacha Kwon-Young Choi Bertrand Coüasnon Yann Ricquebourg Richard Zanibbi Horst Eidenberger Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Yann Ricquebourg Univ Rennes CNRS IRISA Rennes France IRISA Univ. Rennes Rennes France Rochester Institute of Technology Rochester USA Technische Universitat Wien Wien Wien AT

Optical Music Recognition (OMR) is the challenge of understanding the content of musical scores. Accurate detection of individual music objects is a critical step in processing musical documents because a failure at this stage corrupts any further processing. So far, all proposed methods were either limited to typeset music scores or were built to detect only a subset of the available classes of music symbols. In this work, we propose an end-to-end trainable object detector for music symbols that is capable of detecting almost the full vocabulary of modern music notation in handwritten music scores. By training deep convolutional neural networks on the recently released MUSCIMA++ dataset which has symbol-level annotations, we show that a machine learning approach can be used to accurately detect music objects with a mean average precision of over 80%.

关键词： Detectors Semantics Object detection Music Training Feature extraction Vocabulary

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Service learning implementation: Empowering engineering students via drone tech for EDU-4IR program

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AIP Conference Proceedings 2022年第1期2433卷

作者： Jaysuman Pusppanathan Izwyn Zulkapri Nina Diana Nawi Fatin Aliah Phang Nur Amira Zulkiflli Adam Wong Yoon Khang Jamil Abedalrahim Jamil Alsayaydeh Tee Kian Sek Fauzan Khairi Che Harun 1Sports Innovation Technology Centre Institute of Human Centered Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia 2School of Education Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia 3Centre for Engineering Education Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia 4School of Biomedical Engineering & Health Sciences Faculty of Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia 5Faculty of Engineering Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia 6Center for Advanced Computing Technology Faculty of Engineering Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia 7Instrumentation and Sensing Technology Faculty of Electronic & Electrical Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia 8School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

With the advancement of technology, there is an urgency to produce students equipped with the 21st Century skills to keep up with the Fourth Industrial Revolution. This is in line with the aspiration of the Malaysian government to develop holistic graduates and transform education as in the Malaysia Education Blueprint 2015-2025. Service learning pedagogy was explored in this paper by integrating the course content of UKQA2232-01 Drone technology with the real context application. Thirty-one engineering undergraduates enrolled in the Drone technology course participated in the Drone Tech for Edu 4IR project. The project consists of seminars, workshops, and racing activities. Document analysis of the course information, project reports, and students’ reflections were employed in this study to identify the learning process and skills gained by the students. Results show that the students went through 6 phases of service learning implementation. The service learning has enhanced the students’ learning, sense of responsibility, accountability, adaptability, management and international exposure. These are essential to develop good engineers in the future.

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