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[POSTER] Walking in Augmented Reality: An Experimental Evaluation by Playing with a Virtual Hopscotch

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[POSTER] Walking in Augmented Reality: An Experimental Evalu...

IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Manuela Chessa Fabio Solari Department of Informatics Bioengineering Robotics and Systems Engineering Italy

ISBN: (纸本)9781538614549

A crucial aspect when interacting in augmented reality (AR) scenarios is having a stable and coherent perception of the virtual contents overlaid on the real world. This is particularly challenging when the task in the AR environments involves walking onto virtual paths on the floor. In this paper, we present two experiments, conducted in both real-world and cluttered conditions and in a laboratory setup, in which non-trained people were asked to play with a virtual hopscotch. We perform both a qualitative evaluation, by means of a self-assessment questionnaire, and quantitative measures, by capturing the 3D positions of the observers, which show errors in the perception of the hopscotchs layout. The obtained results suggest us that besides the known issues of pose estimation, also the mismatch between the intrinsic parameters of the mobile camera and the ones of the observer eyes could be investigated and taken into account to design effective AR systems.

关键词： Legged locomotion Three-dimensional displays Augmented reality Games Human computer interaction Smart phones

Support Vector Slant Binary Tree Architecture for Facial Stress Recognition Based on Gabor and HOG Feature

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Support Vector Slant Binary Tree Architecture for Facial Str...

International Workshop on Big Data and Information Security (IWBIS)

作者： Barlian Henryranu Prasetio Hiroki Tamura Koichi Tanno Department of Materials and Informatics University of Miyazaki >Japan Department of Environmental Robotics University of Miyazaki Japan Department of Electrical and Systems Engineering University of Miyazaki Japan

The facial stress recognition with histogram information is discussed in this paper. The patterns of facial stress are recognized into three stages, these include a registered image, feature extraction, and classification. The registered image process takes three important parts of the face (the pair of eyes, the nose, and the mouth). While the histogram method such as Gabor filter and HOG feature is used as the feature extraction. Furthermore, the proposed method is to combine SVM and Slant Binary Tree algorithm. Each part of face processed on a different pipeline. Based on the experiment result, the proposed system has accuracy about 86.7{\% and it is outperforming the previous methods. The results of the tree structure show that the nose is a part of the face that most indicates stress.

关键词： Stress Feature extraction Face Support vector machines Mouth Decision trees Gabor filters

Forecasting intracranial hypertension using multi-scale waveform metrics

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arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

Remote health monitoring in the time of COVID-19

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arXiv 2020年

作者： Behar, Joachim A. Liu, Chengyu Tsutsui, Kenta Corino, Valentina D.A. Singh, Janmajay Pimentel, Marco A.F. Karlen, Walter Warrick, Philip Zaunseder, Sebastian Andreotti, Fernando Osipov1, Maxim McSharry, Patrick E. Kotzen, Kevin Karmakar, Chandan Clifford, Gari D. Faculty of Biomedical Engineering Technion-IIT Haifa Israel State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing Department of Cardiovascular Medicine Saitama International Medical Center Saitama Department of Electronics Information and Bioengineering Milano Italy Independent Researcher Department of Engineering Science University of Oxford Oxford United Kingdom Mobile Health Systems Lab Department of Health Sciences and Technology ETH Zurich Zurich Switzerland PeriGen CaryNC United States Faculty of Information Technology FH Dortmund Dortmund Germany Sensyne Health Oxford United Kingdom Babylon Health London United Kingdom Carnegie Mellon University Africa Kigali Rwanda African Centre of Excellence in Data Science University of Rwanda Kigali Rwanda Oxford Man Institute of Quantitative Finance Oxford University Oxford United Kingdom School of Information Technology Deakin University Geelong Australia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia ranges from mild to critically ill cases and requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g., hotels). Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper we present a short review of remote health monitoring initiatives taken in 19 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Self-localization of parking robots using square-like landmarks

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arXiv 2018年

作者： Ye, Canbo Chen, Guang Qu, Sanqing Yang, Qianyi Chen, Kai Du, Jiatong Hu, Ruien College of Automotive Engineering Tongji University Chair of Robotics Artificial Intelligence and Real-time Systems TUM Department of Informatics Technical University of Munich

In this paper, we present a framework for self-localization of parking robots in a parking lot innovatively using square-like landmarks, aiming to provide a positioning solution with low cost but high accuracy. It utilizes square structures common in parking lots such as pillars, corners or charging piles as robust landmarks and deduces the global pose of the robot in conjunction with an off-line map. The localization is performed in real-time via Particle Filter using a single line scanning LiDAR as main sensor, an odometry as secondary information sources. The system has been tested in a simulation environment built in V-REP, the result of which demonstrates its positioning accuracy below 0.20 m and a corresponding heading error below 1◦. Copyright © 2018, The Authors. All rights reserved.

关键词： Robots

An efficient l-shape fitting method for vehicle pose detection with 2d lidar

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arXiv 2018年

作者： Qu, Sanqing Chen, Guang Ye, Canbo Lu, Fan Wang, Fa Xu, Zhongcong Ge, Yixin College of Automotive Engineering Tongji University Chair of Robotics Artificial Intelligence and Real-time Systems TUM Department of Informatics Technical University of Munich

Detecting vehicles with strong robustness and high efficiency has become one of the key capabilities of fully autonomous driving cars. This topic has already been widely studied by GPU-accelerated deep learning approaches using image sensors and 3D LiDAR, however, few studies seek to address it with a horizontally mounted 2D laser scanner. 2D laser scanner is equipped on almost every autonomous vehicle for its superiorities in the field of view, lighting invariance, high accuracy and relatively low price. In this paper, we propose a highly efficient search-based L-Shape fitting algorithm for detecting positions and orientations of vehicles with a 2D laser scanner. Differing from the approach to formulating L-Shape fitting as a complex optimization problem, our method decomposes the L-Shape fitting into two steps: L-Shape vertexes searching and L-Shape corner localization. Our approach is computationally efficient due to its minimized complexity. In on-road experiments, our approach is capable of adapting to various circumstances with high efficiency and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

Correction: Variability of temperature measurements recorded by a wearable device by biological sex

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Biology of sex differences 2023年第1期14卷 82页

作者： Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun K Viswanath Wendy Hartogensis Stephan Dilchert Frederick M Hecht Anoushka Chowdhary Claudine Anglo Leena Pandya Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E Mason Benjamin L Smarr Department of Biomedical Informatics UC San Diego Health University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. Bioinformatics and Systems Biology University of California San Diego San Diego CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Osher Center for Integrative Health University of California San Francisco San Francisco CA USA. Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA. San Diego Supercomputer Center University of California San Diego San Diego CA USA. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. bsmarr@eng.ucsd.edu. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. bsmarr@eng.ucsd.edu.

Ensemble Support Vector Machine and Neural Network Method for Speech Stress Recognition

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Ensemble Support Vector Machine and Neural Network Method fo...

International Workshop on Big Data and Information Security (IWBIS)

作者： Barlian Henryranu Prasetio Hiroki Tamura Koichi Tanno Department of Materials and Informatics Interdisciplinary Graduate School of Agriculture and Engineering University of Miyazaki Japan Department of Environmental Robotics Faculty of Engineering University of Miyazaki Japan Department of Electrical and Systems Engineering Faculty of Engineering University of Miyazaki Japan

This paper has proposed a system that can be analyzed of speech stress recognition. The proposed method (ensemble SVM and NN) is analyzed comparatively proven to have high accuracy. The ensemble method has been applied to improve machine learning ability in identifying with a small number of datasets. It is caused; due to stress is one of the unconscious emotions. Stress can be recognized by speech however it is not robust. It was caused by small datasets. In this work, we use the sample of SUSAS dataset. The dataset is divided into 10 groups by the combinational method. Each group of data trained using SVM then combined it with trained NN. The experimental results show that with a small dataset, the proposed method outperformed the previous methods.

关键词： Stress Speech recognition Machine learning Neural networks Support vector machines Feature extraction Kernel

Multi-manned Assembly Line Balancing Problem with Skilled Workers: A New Mathematical Formulation

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IFAC-PapersOnLine 2017年第1期50卷 1211-1216页

作者： Giglio D. Paolucci M. Roshani A. Tonelli F. Department of Mechanical Energy Management and Transportation Engineering (DIME) University of Genova Via Opera Pia 15 Genova 16145 Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Via Opera Pia 13 Genova 16145 Italy

Multi-manned assembly lines are usually found in plants producing large-sized high-volume products such as automotive sector. In this paper, the balancing problem of this kind of assembly lines with skilled workers is addressed. A new mixed integer programming formulation is presented to solve this problem optimally with the objective of minimizing the total operating cost of the assembly line. The main advantage of the proposed model is to allow the workers in each multi-manned workstation to perform the different assembly tasks of same product simultaneously. The proposed formulation has been used to solve some experimental problems found in the literature. The comparison between the results obtained with the proposed model and those obtained with the model proposed by Moon, Logendran, and Lee (2009) shows that the proposed model can improve the operating cost of the system by reducing the number of workers and workstations. © 2017

关键词： Assembly line balancing problem Mathematical programming Multi-manned workstations Multi-skilled workers