Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement
暂无分享,去创建一个
Yanjun Qi | John Lach | Jiaqi Gong | Myla D. Goldman | J. Lach | Yanjun Qi | M. Goldman | Jiaqi Gong
[1] G. Johansson. Visual perception of biological motion and a model for its analysis , 1973 .
[2] David C. Burr,et al. Seeing biological motion , 1998, Nature.
[3] Suzanne G. Leveille,et al. Upper and lower limb muscle power relationships in mobility-limited older adults. , 2005, The journals of gerontology. Series A, Biological sciences and medical sciences.
[4] Jeffrey A. Cohen,et al. Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls , 2008, Multiple sclerosis.
[5] K. Müller,et al. Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.
[6] Kenneth Meijer,et al. Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.
[7] John Lach,et al. TEMPO 3.1: A Body Area Sensor Network Platform for Continuous Movement Assessment , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.
[8] Toyoaki Nishida,et al. Mining Causal Relationships in Multidimensional Time Series , 2010, Smart Information and Knowledge Management.
[9] S. Beer,et al. Assessment of gait parameters and fatigue in MS patients during inpatient rehabilitation: a pilot trial , 2011, Journal of Neurology.
[10] Andreas Ziehe,et al. Comparison of Granger Causality and Phase Slope Index , 2008, NIPS Causality: Objectives and Assessment.
[11] Taeyoung Kim,et al. Characterizing and minimizing synchronization and calibration errors in inertial body sensor networks , 2010, BODYNETS.
[12] Isabelle Guyon,et al. Causality : Objectives and Assessment , 2010 .
[13] Aapo Hyvärinen,et al. Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity , 2010, J. Mach. Learn. Res..
[14] Jan Rueterbories,et al. Methods for gait event detection and analysis in ambulatory systems. , 2010, Medical engineering & physics.
[15] Maik C. Stüttgen,et al. Computation of measures of effect size for neuroscience data sets , 2011, The European journal of neuroscience.
[16] F. Horak,et al. Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. , 2012, Gait & posture.
[17] L. Craighero,et al. Leadership in Orchestra Emerges from the Causal Relationships of Movement Kinematics , 2012, PloS one.
[18] Vladimir Pavlovic,et al. Sparse Granger causality graphs for human action classification , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[19] Tao Liu,et al. Gait Analysis Using Wearable Sensors , 2012, Sensors.
[20] Shanshan Chen,et al. Aiding diagnosis of normal pressure hydrocephalus with enhanced gait feature separability , 2012, Wireless Health.
[21] Chong-Wah Ngo,et al. Trajectory-Based Modeling of Human Actions with Motion Reference Points , 2012, ECCV.
[22] Gracián Triviño,et al. Linguistic description of the human gait quality , 2013, Eng. Appl. Artif. Intell..
[23] Ilaria Carpinella,et al. Quantitative assessment of upper limb motor function in Multiple Sclerosis using an instrumented Action Research Arm Test , 2014, Journal of NeuroEngineering and Rehabilitation.
[24] Manuela Galli,et al. Summary measures for clinical gait analysis: a literature review. , 2014, Gait & posture.
[25] Begonya Garcia-Zapirain,et al. Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications , 2014, Sensors.
[26] K. R. Ramakrishnan,et al. A Cause and Effect Analysis of Motion Trajectories for Modeling Actions , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[27] F. Horak,et al. Body-worn sensors capture variability, but not decline, of gait and balance measures in multiple sclerosis over 18 months. , 2014, Gait & posture.
[28] Hermie Hermens,et al. Optimal Sensor Placement for Measuring Physical Activity with a 3D Accelerometer , 2014, Sensors.
[29] Bernt Schiele,et al. A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.
[30] Yanjun Qi,et al. Piecewise Linear Dynamical Model for Actions Clustering from Inertial Body Sensors with Considerations of Human Factors , 2014, BODYNETS.
[31] Nicholas D. Lane,et al. Can Deep Learning Revolutionize Mobile Sensing? , 2015, HotMobile.
[32] Yanjun Qi,et al. Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
[33] Barry R. Greene,et al. Assessment and Classification of Early-Stage Multiple Sclerosis With Inertial Sensors: Comparison Against Clinical Measures of Disease State , 2015, IEEE Journal of Biomedical and Health Informatics.