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Correction for: Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

JMIR research protocols 2018年第1期7卷 e10页

作者： Floor Sieverink Saskia Kelders Mannes Poel Lisette van Gemert-Pijnen Centre for eHealth and Wellbeing Research Department of Psychology Health and Technology University of Twente Enschede Netherlands. Optentia Research Focus Area North-West University Vanderbijlpark South Africa. Human Media Interaction Group Department of Computer Science University of Twente Enschede Netherlands.

[This corrects the article DOI: .].

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Opening the black box of electronic health: Collecting, analyzing, and interpreting log data

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JMIR Research Protocols 2017年第8期6卷 e156页

作者： Sieverink, Floor Kelders, Saskia Poel, Mannes van Gemert-Pijnen, Lisette Centre for eHealth and Wellbeing Research Department of Psychology Health and Technology University of Twente Enschede Netherlands Optentia Research Focus Area North-West University Vanderbijlpark South Africa Human Media Interaction Group Department of Computer Science University of Twente Enschede Netherlands

In electronic health (eHealth) research, limited insight has been obtained on process outcomes or how the use of technology has contributed to the users' ability to have a healthier life, improved well-being, or activate new attitudes in their daily tasks. As a result, eHealth is often perceived as a black box. To open this black box of eHealth, methodologies must extend beyond the classic effect evaluations. The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology. However, the possibilities of log data in eHealth research have not been exploited to their fullest extent. The aim of this paper is to describe how log data can be used to improve the evaluation and understand the use of eHealth technology with a broader approach than only descriptive statistics. This paper serves as a starting point for using log data analysis in eHealth research. Here, we describe what log data is and provide an overview of research questions to evaluate the system, the context, the users of a technology, as well as the underpinning theoretical constructs. We also explain the requirements for log data, the starting points for the data preparation, and methods for data collection. Finally, we describe methods for data analysis and draw a conclusion regarding the importance of the results for both scientific and practical applications. The analysis of log data can be of great value for opening the black box of eHealth. A deliberate log data analysis can give new insights into how the usage of the technology contributes to found effects and can thereby help to improve the persuasiveness and effectiveness of eHealth technology and the underpinning behavioral models. © Floor Sieverink, Saskia Kelders, Mannes Poel, Lisette van Gemert-Pijnen. Originally published in JMIR Research Protocols (http://***), 07.08.2017. This is an open-access article distribu

关键词： Black box EHealth Evaluation Log data analysis

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Guo, Dejun Wang, Hesheng Chen, Weidong Liu, Ming Xia, Zeyang Leang, Kam K. Lab University of Utah Robotics Center Salt Lake CityUT84112 United States Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute of Biomedical and Health Engineering Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781509046331

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： Mobile robots

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Exploring Interactive Teaching of a Multi-Modal Emotional Expression of a humanoid Robot

Exploring Interactive Teaching of a Multi-Modal Emotional Ex...

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Future Technologies Conference

作者： Myriam Anis Ahmed Elnaggar Dirk Reichardt School of Computer Science and Engineering German University in Cairo Intelligent Interaction Lab Computer Science Department DHBW Stuttgart

ISBN: (纸本)9781509041725

One of the most popular domains in the current research is getting robots to replace humans in the difficult and dangerous tasks. To do that a robot must act as intelligently as humans towards different problems. Giving life to a robot is a dream that is coming true. One Of the hottest fields in this domain is teaching the robot how to understand and express emotions. The main target of this paper is to present an approach to teach a humanoid how to understand and then to express emotions. The robot should be able to understand emotions from plain text, voice and facial expressions then express them using gestures and voices. To validate the results, the robot should be able to read a text, understand the emotions in this text and then express them. Text synthesis is implemented through machine learning while expressing emotions is done through analyzing and then imitating people's voices and postures while they experience different emotions. This research only focuses on the basic 3 emotions: happiness, sadness and anger. For the results an accuracy of 73.9% was reached in text synthesis, and survey was done to test the clarity of emotions expressed by the robot. An accuracy of 83.6%, 87.6% and 86.3% was reached in expressing happiness, sadness and anger respectively.

关键词： Robotics Text synthesis Emotions expressions Machine learning

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Author Correction: A robust benchmark for detection of germline large deletions and insertions

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Nature biotechnology 2020年第11期38卷 1357页

作者： Justin M Zook Nancy F Hansen Nathan D Olson Lesley Chapman James C Mullikin Chunlin Xiao Stephen Sherry Sergey Koren Adam M Phillippy Paul C Boutros Sayed Mohammad E Sahraeian Vincent Huang Alexandre Rouette Noah Alexander Christopher E Mason Iman Hajirasouliha Camir Ricketts Joyce Lee Rick Tearle Ian T Fiddes Alvaro Martinez Barrio Jeremiah Wala Andrew Carroll Noushin Ghaffari Oscar L Rodriguez Ali Bashir Shaun Jackman John J Farrell Aaron M Wenger Can Alkan Arda Soylev Michael C Schatz Shilpa Garg George Church Tobias Marschall Ken Chen Xian Fan Adam C English Jeffrey A Rosenfeld Weichen Zhou Ryan E Mills Jay M Sage Jennifer R Davis Michael D Kaiser John S Oliver Anthony P Catalano Mark J P Chaisson Noah Spies Fritz J Sedlazeck Marc Salit Material Measurement Laboratory National Institute of Standards and Technology Gaithersburg MD USA. jzook@nist.gov. National Human Genome Research Institute National Institutes of Health Rockville MD USA. Material Measurement Laboratory National Institute of Standards and Technology Gaithersburg MD USA. National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD USA. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Roche Sequencing Solutions Belmont CA USA. Ontario Institute for Cancer Research Toronto Ontario Canada. Charles-Bruneau Cancer Centre Division of Hematology-Oncology CHU Sainte-Justine Montreal Quebec Canada. Molecular Biology Institute University of California Los Angeles Los Angeles CA USA. Department of Physiology and Biophysics Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA. The WorldQuant Initiative for Quantitative Prediction Weill Cornell Medicine New York NY USA. The Feil Family Brain and Mind Research Institute Weill Cornell Medicine New York NY USA. Bionano Genomics Inc. San Diego CA USA. Davies Research Centre School of Animal and Veterinary Sciences University of Adelaide Roseworthy SA Australia. 10× Genomics Pleasanton CA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Google Mountain View CA USA. Department of Computer Science Roy G. Perry College of Engineering Prairie View A&M University Prairie View TX USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. BC Cancer Genome Sciences Centre Vancouver British Columbia Canada. Biomedical Genetics Department of Medicine Boston University Medical School Boston MA USA. Pacific Biosciences Menlo Park CA USA. Department of Computer Engin

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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7.3: Compare Variation of User's EEG Owing to Parallax Cue

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Haichen Hu Danli Wang Kang Yue Xinpan Yang Steve C. Chiu Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello America

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further.

关键词： EEG Parallax RDS 3D

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A report on the first international workshop on research methods in animal-computer interaction

A report on the first international workshop on research met...

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2017 ACM SIGCHI Conference on human Factors in Computing Systems, CHI EA 2017

作者： Zamansky, Anna Roshier, Amanda Mancini, Clara Collins, Emily C. Hall, Carol Grillaert, Katie Morrison, Ann North, Steve Wirman, Hanna Information Systems Department University of Haifa Israel School of Veterinary Medicine and Science University of Nottingham United Kingdom Animal-Computer Interaction Lab Open University Milton Keynes United Kingdom Sheffield Robotics University of Sheffield United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University United Kingdom Messerli Research Institute University of Veterinary Medicine Medical University of Vienna University of Vienna Austria Department of Architecture Design and Media Technology Aalborg University Denmark Mixed Reality Laboratory University of Nottingham United Kingdom School of Design Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450346566

Animal-computer interaction (ACI) is a new and quickly developing discipline, which is closely related to HCI and is making reference to some of its theoretical frameworks and research methodologies. The first edition of the Workshop on Research Methods in ACI (RM4ACI) was co-located with the Third International Conference on Animal-computer interaction, which took place in Milton-Keynes, UK in November 2016. This paper presents an overview of the workshop, including insights from discussions on some of the challenges faced by the ACI community as it works to develop ACI as a discipline, and on important opportunities for cross-fertilization between HCI and ACI that the HCI community could consider. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Animals

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Toward integrating Cognitive Tutor interaction data with human tutoring text dialogue data in LearnSphere

Toward integrating Cognitive Tutor interaction data with hum...

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2016 EDM Workshops and Tutorials, WT-EDM 2016

作者： Fancsali, Stephen E. Ritter, Steven Berman, Susan R. Yudelson, Michael Rus, Vasile Morrison, Donald M. Carnegie Learning Inc. 437 Grant Street Suite 1906 PittsburghPA15219 United States Human-Computer Interaction Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Institute for Intelligent Systems Department of Computer Science University of Memphis MemphisTN United States

We present details of a large, novel dataset that includes both Carnegie Learning Cognitive Tutor interaction data and various data related to learner interactions with human tutors via an online chat while using the Cognitive Tutor. We discuss integrating these two data modalities within LearnSphere and propose workflows (and corresponding analyses) germane to our U.S. department of Defense Advanced Distributed Learning Initiative-funded Integrating human and Automated Tutoring Systems (IHATS) project, demonstrating various aspects of the potential of the LearnSphere framework. © 2016, CEUR-WS. All rights reserved.

关键词： Cognitive systems

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Interconnecting objects, visitors, sites and (Hi)stories across cultural and historical concepts: The crosscult project 6th

Interconnecting objects, visitors, sites and (Hi)stories acr...

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6th International Euro-Mediterranean Conference on Digital Heritage, EuroMed 2016

作者： Vassilakis, Costas Antoniou, Angeliki Lepouras, George Wallace, Manolis Lykourentzou, Ioanna Naudet, Yannick Human-Computer Interaction and Virtual Reality Lab Department of Informatics and Telecommunications University of the Peloponnese Tripolis22 131 Greece Knowledge and Uncertainty Research Laboratory Department of Informatics and Telecommunications University of the Peloponnese Tripolis22 131 Greece Luxembourg Institute of Science and Technology 5 avenue des Hauts-Fourneaux Esch/Alzette4363 Luxembourg

ISBN: (纸本)9783319484952

human History, is a huge mesh of interrelated facts and concepts, spanning beyond borders, encompassing global aspects and finally constituting a shared, global experience. This is especially the case regarding European history, which is highly interconnected by nature;however, most History-related experiences that are today offered to the greater public, from schools to museums, are siloed. The CrossCult project aims to provide the means for offering citizens and cultural venue visitors a more holistic view of history, in the light of cross-border interconnections among pieces of cultural heritage, other citizens viewpoints and physical venues. To this end, the CrossCult project will built a comprehensive knowledge base encompassing information and semantic relationships across cultural information elements, and will provide the technological means for delivering the contents of this knowledge base to citizens and venue visitors in a highly personalized manner, creating narratives for the interactive experiences that maximise situational curiosity and serendipitous learning. The CrossCult platform will also exploit the cognitive/emotional profiles of the participants as well as temporal, spatial and miscellaneous features of context, including holidays and anniversaries, social media trending topics and so forth. © Springer International Publishing AG 2016.

关键词： Semantics

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Personalizing EEG-based affective models with transfer learning 25

Personalizing EEG-based affective models with transfer learn...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zheng, Wei-Long Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective braincomputer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subjectspecific labeled data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average.

关键词： Electroencephalography

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