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arXiv 2018年

作者： Wu, Dongrui Lawhern, Vernon J. Gordon, Stephen Lance, Brent J. Lin, Chin-Teng DataNova NY United States Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen Proving GroundMD United States Department of Computer Science University of Texas at San Antonio San AntonioTX United States DCS Corp AlexandriaVA United States Brain Research Center National Chiao-Tung University Hsinchu Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Australia

There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain computer interface

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Agreement rate initialized maximum likelihood estimator for ensemble classifier aggregation and its application in brain-computer interface

arXiv

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arXiv 2018年

Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class that most base classifiers vote for. However, improved performance can be obtained by assigning weights to the base classifiers according to their accuracy. This paper proposes an agreement rate initialized maximum likelihood estimator (ARIMLE) to optimally fuse the base classifiers. ARIMLE first uses a simplified agreement rate method to estimate the classification accuracy of each base classifier from the unlabeled samples, then employs the accuracies to initialize a maximum likelihood estimator (MLE), and finally uses the expectation-maximization algorithm to refine the MLE. Extensive experiments on visually evoked potential classification in a brain-computer interface application show that ARIMLE outperforms majority voting, and also achieves better or comparable performance with several other state-of-the-art classifier combination approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain computer interface

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Towards automatic semantic segmentation in volumetric ultrasound 20th

Towards automatic semantic segmentation in volumetric ultra...

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Yang, Xin Yu, Lequan Li, Shengli Wang, Xu Wang, Na Qin, Jing Ni, Dong Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Department of Ultrasound Affliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University Shenzhen China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China School of Nursing Centre for Smart Health The Hong Kong Polytechnic University Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319661810

3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this paper, we are looking at the problem of volumetric segmentation in ultrasound to promote the volume-based, precise maternal and fetal health monitoring. Our contribution is threefold. First, we propose the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge. Second, based on our customized 3D Fully Convolutional Network, we propose to inject a Recurrent Neural Network (RNN) to flexibly explore 3D semantic knowledge from a novel, sequential perspective, and therefore significantly refine the local segmentation result which is initially corrupted by the ubiquitous boundary uncertainty in ultrasound volumes. Third, considering sequence hierarchy, we introduce a hierarchical deep supervision mechanism to effectively boost the information flow within RNN and further improve the semantic segmentation results. Extensively validated on our in-house large datasets, our approach achieves superior performance and presents to be promising in boosting the interpretation of prenatal ultrasound volumes. Our framework is general and can be easily extended to other volumetric ultrasound segmentation tasks. © 2017, Springer International Publishing AG.

关键词： Ultrasonics

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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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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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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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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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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Kinematic study with and without ski boots using ski simulator

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science and Sports 2017年第1期32卷 e9-e14页

作者： Lee, H.T. Kim, Y.J. Kim, Y.S. Department of Computer Science and Engineering Korea University of Technology and Education Chunjeol-ro 330-708 Cheonan South Korea Human-centered Interaction Laboratory Korea University of Technology and Education Chunjeol-ro 330-708 Cheonan South Korea Department of Marine Sports Pukyong National University Yongso-ro 608-737 Nam-gu Busan South Korea

Objectives We performed a comparative analysis of ski simulators, which remove seasonal and regional constraints, to effectively improve training methods. Equipment and methods We compared the kinematic characteristics with and without ski boots on the ski simulator and found significant differences in lower limb joint angles, run-time, muscle activity, and heart rate. Results Based on these results, we suggest the following exercise guidelines for ski simulators: (i) simulator skiing without boots results in a wider range of motion (ROM) and can be utilized as low-intensity exercise to improve muscle endurance and balance and (ii) simulator skiing with boots enables faster movements within a limited range and can help improve muscle strength. These results are not limited to ski training;they can also be applied to specialized exercise improvement and strengthening programs as well as physical training. © 2016 Elsevier Masson SAS

关键词： Lower limb joint angle Muscle activity Ski simulator exercise Skiing analysis

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