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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Young-Hoon Nho Jong Gwan Lim Dae-Eon Kim Dong-Soo Kwon Robotics Program and Human-Robot Interaction research center Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Information Engineering Computer Science and University of Trento Italy KAIST Human-Robot Interaction Research Center Daejeon Republic of Korea Department of Mechanical Engineering Human-Robot Interaction research center Daejeon Republic of Korea

Fall detection systems have been proposed to prevent additional injuries following fall accidents. This paper introduces an easily learnable fall detection system based on the data of an individual patient in a hospital room. The improvement of low performance using a single accelerometer at wrists and the inconvenience of sensor attached to a waist in the conventional approach was concentrated on by integrating heart rate signals to the conventional acceleration approach and changing the sensor location from a waist to wrists. As for the optimal heart rate feature selection, we proposed a four-feature vector combination (root mean square of successive differences, standard deviation of successive differences, normal to normal 50, normal to normal 20) with correlation and mutual information analysis in addition to mean absolute deviation selected as an accelerometer feature. To easily acquire and train the patients' fall data, our system was based on unsupervised learning approaches using Gaussian mixture models for optimal classifiers with the optimal cluster number decided by cluster validation index of square error sum. A 10-fold cross validation was applied for a final performance evaluation where each threshold for separating fall state from non-fall state was automatically decided in several comparison groups, which were created on the basis of fusion timing and used sensors. As a result, despite sensors attached to the wrist, the wearable inconvenience of the conventional is overcome using the feature-level fused approach between heart rates and accelerations with the accuracy up to 98.39 %, which is closest to 99.34 % of the case using a single accelerometer located at the waist.

关键词： Accelerometers Heart rate Wrist Training data Hospitals Robot sensing systems Unsupervised learning

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Using social media to stimulate history reflection in cultural heritage

Using social media to stimulate history reflection in cultur...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Stavroula Bampatzia Angeliki Antoniou George Lepouras Costas Vasilakis Manolis Wallace Department of Informatics and Telecommunications Human-Computer Interaction and Virtual Reality Lab Tripolis Greece Department of Informatics and Telecommunications Knowledge and Uncertainty Research Laboratory Tripolis Greece

CrossCult H2020 is a European project, the aim of which is the reflection of history in a cultural setting. In this paper, we describe how social media can be linked to cultural heritage and in particular how we can incorporate games, social networks, history reflection and culture. The paper presents the case study of one of the project pilots, to show how history reflection can be enhanced with the use of social networks.

关键词： Reflection Cultural differences History Games Facebook Europe

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Evaluation of Motor Training Performance in 3D Virtual Environment via Combining Brain-computer Interface and Haptic Feedback

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Procedia computer science 2017年 107卷 256-261页

作者： Hong Wu Shuang Liang Wenlong Hang Xiaolu Liu Qiong Wang Kup-Sze Choi Jing Qin School of Information Engineering Wuhan University of Technology Wuhan 430070 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Digital Media Jiangnan University Wuxi 214122 China School of Biomedical Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Hung Hom Hong Kong

Brain-computer interfaces (BCIs) based on virtual reality (VR) mostly integrate visual and/or auditory feedback. Haptic feedbackwhich has the potential on improving the feasibility and operability of VR-based BCI systems is rarely explored in previous studies. In this article, we present a novel framework of BCI system based on both visual and haptic feedback, in which users can learn to manipulate the haptic device's stylus for motor training. The effects of motor training with and without haptic feedback are evaluated by detecting and analysing the changes of electroencephalogram (EEG). The preliminary experimental results indicate that haptic feedback may influence the modulation of the beta rhythms over left and right sensorimotor cortex during hand movements. This study can be easily replicated to evaluate the existing systems with haptic feedback and used to develop new applications for motor training.

关键词： Haptic feedback brain-computer interface (BCI) Electroencephalogram (EEG) event-related spectral perturbation (ERSP) virtual reality (VR)

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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P-34: Compare and Model Multi-level Stereoscopic 3D Visual Fatigue Based on EEG

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SID Symposium Digest of Technical Papers 2017年第1期48卷

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

In this paper, we designed an experiment to observe changes for different fatigue level in human brain when watching random dot stereogram (RDS) using the method of EEG. The subjects were asked to watch 3D content for 30 minutes and the disparity of RDS changed every 10 seconds. During this watching task, subjects should rate their extent of fatigue during the experiment on the scale of one to five only when they felt worse or better. 30 channels electroencephalogram (EEG) signals were recorded through the experiment. Finally, the relationship between various fatigue levels and disparities which revealed by EEG signals were investigated. Our results show that the power of waveband, namely alpha increased with the fatigue level increasing from one to four during watching RDS, but decreased in level five. We have employed logistic regression to model fatigue level and achieved classification accuracy of 94.18%. Our research has important implications for the continuous monitoring of user's fatigue in the future.

关键词： Visual fatigue modeling EEG 3DTV Random Dot Stereogram

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XDSession: Integrated development and testing of cross-device applications 15

XDSession: Integrated development and testing of cross-devic...

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7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS 2015

作者： Nebeling, Michael Husmann, Maria Zimmerli, Christoph Valente, Giulio Norrie, Moira C. Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9781450336468

Despite the recent proliferation of new cross-device application frameworks, there is still a lack of sophisticated tools for testing new applications during their development. This paper presents XDSession - a framework for cross-device application development based on a concept of cross-device sessions, not only useful for managing distribution and synchronisation, but also for logging and debugging. Integrated with XDSession are two new tools specifically designed for cross-device testing. First, the session controller supports management and testing of cross-device sessions with connected or simulated devices at run-time. Second, the session inspector enables inspection and analysis of multi-device/multi-user sessions with support for deterministic record/replay of cross-device sessions. We show the utility of XDSession based on a case study of a semester-long course project in which our tools were used by students to reimplement an existing application and extend it with cross-device capabilities. © Copyright 2015 ACM.

关键词： session concept multi-user/multi-device applications cross-device development platform

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Kinect analysis: A system for recording, analysing and sharing multimodal interaction elicitation studies 15

Kinect analysis: A system for recording, analysing and shari...

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7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS 2015

作者： Nebeling, Michael Ott, David Norrie, Moira C. Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9781450336468

Recently, guessability studies have become a popular means among researchers to elicit user-defined interaction sets involving gesture, speech and multimodal input. However, tool support for capturing and analysing interaction proposals is lacking and the method itself is still evolving. This paper presents Kinect Analysis - a system designed for interaction elicitation studies with support for record-and-replay, visualisation and analysis based on Kinect's depth, audio and video streams. Kinect Analysis enables post-hoc analysis during playback and live analysis with real-time feedback while recording. In particular, new visualisations such as skeletal joint traces and heatmaps can be superimposed for analysis and comparison of multiple recordings. It also introduces KinectScript - a simple scripting language to query recordings and automate analysis tasks based on skeleton, distance, audio and gesture scripts. The paper discusses Kinect Analysis both as a tool and a method that could enable researchers to more easily collect, study and share interaction proposals. Using data from a previous guessability study with 25 users, we show that Kinect Analysis in combination with Kinect-Script is useful and effective for a range of analysis tasks. Copyright 2015 ACM.

关键词： tools for guessability studies visualisation and analysis kinect analysis multimodal interaction recording

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DISCRIMINATIVELY TRAINED JOINT SPEAKER AND ENVIRONMENT REPRESENTATIONS FOR ADAPTATION OF DEEP NEURAL NETWORK ACOUSTIC MODELS

DISCRIMINATIVELY TRAINED JOINT SPEAKER AND ENVIRONMENT REPRE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Maofan Yin Sunil Sivadas Kai Yu Bin Ma Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab. Department of Computer Science and Engineering Shanghai Jiao Tong University China Human Language Technology Lab. Institute for Infocomm Research Singapore

ISBN: (纸本)9781479999897

A recent trend in normalization of factors extraneous to a speech recognition task has been to explicitly introduce features related to the unwanted variability in the training of Deep Neural Networks (DNN). Typically, this is done by either perturbing the training set with models of these extraneous factors such as vocal tract length and environmental noise or augmenting the conventional spectral features with auxiliary information such as i-vector, noise spectrum, etc. Another emerging approach is to derive low dimensional representations of the factors from the hidden layers of DNN and use it for normalization of the acoustic model. Almost all of these approaches focus on either speaker or environment normalization. In this paper we propose a novel approach for estimating a compact joint representation of speakers and environment by training a DNN, with a bottleneck layer, to classify the i-vector features into speaker and environment labels by Multi-Task Learning (MTL). Another novelty is to learn this compact representation while learning to map the i-vector of a noisy utterance into its corresponding clean speaker ivector and noise-only i-vector. Experiments were conducted on an artificially noise-corrupted version of the WSJ corpus. The proposed compact joint speaker-environment representations show promising gains.

关键词： i-vector deep neural network speaker adaptation multi-task learning noise robustness Loudspeakers speaker adaptation Noise robustness Environments normalization Joints Neural network representations Speech recognition PARALLEL PROCESSING (computerS) Acoustic models Ambient Noise Vocal tract

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Landscape of somatic mutations in 560 breast cancer whole-genome sequences (vol 534, pg 47, 2016)

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NATURE 2019年第7742期566卷 E1-E1页

作者： Nik-Zainal, Serena Davies, Helen Staaf, Johan Ramakrishna, Manasa Glodzik, Dominik Zou, Xueqing Martincorena, Inigo Alexandrov, Ludmil B. Martin, Sancha Wedge, David C. Van Loo, Peter Ju, Young Seok Smid, Marcel Brinkman, Arie B. Morganella, Sandro Aure, Miriam R. Lingjaerde, Ole Christian Langerod, Anita Ringner, Markus Ahn, Sung-Min Boyault, Sandrine Brock, Jane E. Broeks, Annegien Butler, Adam Desmedt, Christine Dirix, Luc Dronov, Serge Fatima, Aquila Foekens, John A. Gerstung, Moritz Hooijer, Gerrit K. J. Jang, Se Jin Jones, David R. Kim, Hyung-Yong King, Tari A. Krishnamurthy, Savitri Lee, Hee Jin Lee, Jeong-Yeon Li, Yilong McLaren, Stuart Menzies, Andrew Mustonen, Ville O'Meara, Sarah Pauporte, Iris Pivot, Xavier Purdie, Colin A. Raine, Keiran Ramakrishnan, Kamna Rodriguez-Gonzalez, F. German Romieu, Gilles Sieuwerts, Anieta M. Simpson, Peter T. Shepherd, Rebecca Stebbings, Lucy Stefansson, Olafur A. Teague, Jon Tommasi, Stefania Treilleux, Isabelle Van den Eynden, Gert G. Vermeulen, Peter Vincent-Salomon, Anne Yates, Lucy Caldas, Carlos van't Veer, Laura Tutt, Andrew Knappskog, Stian Tan, Benita Kiat Tee Jonkers, Jos Borg, Ake Ueno, Naoto T. Sotiriou, Christos Viari, Alain Futreal, P. Andrew Campbell, Peter J. Span, Paul N. Van Laere, Steven Lakhani, Sunil R. Eyfjord, Jorunn E. Thompson, Alastair M. Birney, Ewan Stunnenberg, Hendrik G. de Vijver, Marc J. van Martens, John W. M. Borresen-Dale, Anne-Lise Richardson, Andrea L. Kong, Gu Thomas, Gilles Stratton, Michael R. Wellcome Trust Sanger Institute Hinxton Cambridge UK East Anglian Medical Genetics Service Cambridge University Hospitals NHS Foundation Trust Cambridge UK Division of Oncology and Pathology Department of Clinical Sciences Lund Lund University Lund Sweden Theoretical Biology and Biophysics (T-6) Los Alamos National Laboratory Los Alamos USA Center for Nonlinear Studies Los Alamos National Laboratory Los Alamos USA Department of Human Genetics University of Leuven Leuven Belgium Department of Medical Oncology Erasmus MC Cancer Institute and Cancer Genomics Netherlands Erasmus University Medical Center Rotterdam The Netherlands Radboud University Department of Molecular Biology Faculty of Science Nijmegen The Netherlands European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton UK Department of Cancer Genetics Institute for Cancer Research Oslo University Hospital The Norwegian Radium Hospital Oslo Norway K. G. Jebsen Centre for Breast Cancer Research Institute for Clinical Medicine University of Oslo Oslo Norway Department of Computer Science University of Oslo Oslo Norway Gachon Institute of Genome Medicine and Science Gachon University Gil Medical Center Incheon South Korea Translational Research Lab Centre Léon Bérard Lyon Cedex 08 France Department of Pathology Brigham and Women’s Hospital Boston USA The Netherlands Cancer Institute Amsterdam The Netherlands Breast Cancer Translational Research Laboratory Université Libre de Bruxelles Institut Jules Bordet Brussels Belgium Translational Cancer Research Unit Center for Oncological Research Faculty of Medicine and Health Sciences University of Antwerp Antwerp Belgium Dana-Farber Cancer Institute Boston Massachusetts USA Department of Pathology Academic Medical Center Amsterdam The Netherlands Department of Pathology Asan Medical Center College of Medicine Ulsan University Ulsan South Korea Department of Pathology College of Medicine Hany

In the Methods section of this Article, 'greater than' should have been 'less than' in the sentence 'Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance that was at least 10 times greater than the whole-genome average for the individual sample. '. The Article has not been corrected.

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Phone-Aware LSTM-RNN for Voice Conversion

Phone-Aware LSTM-RNN for Voice Conversion

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Jiahao Lai Bo Chen Tian Tan Sibo Tong Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Speech Lab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks（LSTM-RNNs）. Most existing voice conversion methods, including Joint Density Gaussian Mixture Models（JDGMMs）, Deep Neural Networks（DNNs）and Bidirectional Long Short-Term Memory Recurrent Neural Networks（BLSTM-RNNs）, only take acoustic information of speech as features to train models. We propose to incorporate linguistic information to build voice conversion system by using monophones generated by a speech recognizer as linguistic features. The monophones and spectral features are combined together to train LSTM-RNN based voice conversion models,reinforcing the context-dependency modelling of *** results of the 1st voice conversion challenge shows our system achieves significantly higher performance than baseline（GMM method） and was found among the most competitive scores in similarity test. Meanwhile, the experimental results show phone-aware LSTM-RNN method obtains lower Melcepstral distortion and higher MOS scores than the baseline LSTM-RNNs.

关键词： voice conversion voice conversion challenge long short-term memory recurrent neural networks phone

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