PRAXIS: Towards automatic cognitive assessment using gesture recognition

[1]  J. Warren.  Apraxia , 2018, Canadian Medical Association Journal.

[2]  Clayton R. Pereira,et al.  A new computer vision-based approach to aid the diagnosis of Parkinson's disease , 2016, Comput. Methods Programs Biomed..

[3]  Georgios Tzimiropoulos,et al.  Human Pose Estimation via Convolutional Part Heatmap Regression , 2016, ECCV.

[4]  Hazim Kemal Ekenel,et al.  How Transferable Are CNN-Based Features for Age and Gender Classification? , 2016, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG).

[5]  Ling Shao,et al.  Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Daniel Thalmann,et al.  Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Hermann Ney,et al.  Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data is Continuous and Weakly Labelled , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Debi Prosad Dogra,et al.  Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning , 2016, IEEE Transactions on Biomedical Engineering.

[9]  Peter V. Gehler,et al.  DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Serhan Cosar,et al.  Generating unsupervised models for online long-term daily living activity recognition , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[11]  James M. Keller,et al.  Recognizing complex instrumental activities of daily living using scene information and fuzzy logic , 2015, Comput. Vis. Image Underst..

[12]  Cordelia Schmid,et al.  P-CNN: Pose-Based CNN Features for Action Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Silvio Savarese,et al.  Watch-n-patch: Unsupervised understanding of actions and relations , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  P. Robert,et al.  Ecological Assessment of Autonomy in Instrumental Activities of Daily Living in Dementia Patients by the Means of an Automatic Video Monitoring System , 2015, Front. Aging Neurosci..

[15]  C. Schmid,et al.  A Robust and Efficient Video Representation for Action Recognition , 2015, International Journal of Computer Vision.

[16]  Nicu Sebe,et al.  Video classification with Densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off , 2015, International Journal of Multimedia Information Retrieval.

[17]  Vincent Lepetit,et al.  Hands Deep in Deep Learning for Hand Pose Estimation , 2015, ArXiv.

[18]  T. Issac,et al.  Apraxias in Neurodegenerative Dementias , 2015, Indian journal of psychological medicine.

[19]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[20]  Ling Shao,et al.  Realistic action recognition via sparsely-constructed Gaussian processes , 2014, Pattern Recognit..

[21]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Ken Perlin,et al.  Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..

[23]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Benjamin Schrauwen,et al.  Sign Language Recognition Using Convolutional Neural Networks , 2014, ECCV Workshops.

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Gérard G. Medioni,et al.  Structured Time Series Analysis for Human Action Segmentation and Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Ling Shao,et al.  Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[29]  Ling Shao,et al.  Spatio-Temporal Laplacian Pyramid Coding for Action Recognition , 2014, IEEE Transactions on Cybernetics.

[30]  Rama Chellappa,et al.  Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Cristina V. Lopes,et al.  Free-hand interaction with leap motion controller for stroke rehabilitation , 2014, CHI Extended Abstracts.

[32]  Dario Farina,et al.  Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control , 2014, IEEE Transactions on Biomedical Engineering.

[33]  Alon Wolf,et al.  An Adaptive Home-Use Robotic Rehabilitation System for the Upper Body , 2014, IEEE Journal of Translational Engineering in Health and Medicine.

[34]  Sergio Escalera,et al.  Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments , 2014, IEEE Transactions on Cybernetics.

[35]  Sergio Escalera,et al.  ChaLearn multi-modal gesture recognition 2013: grand challenge and workshop summary , 2013, ICMI '13.

[36]  Hairong Qi,et al.  Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps , 2013, 2013 IEEE International Conference on Computer Vision.

[37]  Cristian Sminchisescu,et al.  The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[38]  Siew Wen Chin,et al.  Game-based human computer interaction using gesture recognition for rehabilitation , 2013, 2013 IEEE International Conference on Control System, Computing and Engineering.

[39]  Yiannis Kompatsiaris,et al.  Recognition of Activities of Daily Living for Smart Home Environments , 2013, 2013 9th International Conference on Intelligent Environments.

[40]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[41]  Aytül Erçil,et al.  A decision forest based feature selection framework for action recognition from RGB-depth cameras , 2013, 2013 21st Signal Processing and Communications Applications Conference (SIU).

[42]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.

[43]  Ling Shao,et al.  Silhouette Analysis-Based Action Recognition Via Exploiting Human Poses , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[44]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[45]  Christian Wolf,et al.  Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification , 2012, BMVC.

[46]  Yannick Benezeth,et al.  Posture Recognition Based on Fuzzy Logic for Home Monitoring of the Elderly , 2012, IEEE Transactions on Information Technology in Biomedicine.

[47]  Deva Ramanan,et al.  Detecting activities of daily living in first-person camera views , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Min Sun,et al.  Conditional regression forests for human pose estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Noel E. O'Connor,et al.  Evaluating a dancer's performance using kinect-based skeleton tracking , 2011, ACM Multimedia.

[50]  Christian Wolf,et al.  Sequential Deep Learning for Human Action Recognition , 2011, HBU.

[51]  Thomas B. Moeslund,et al.  A selective spatio-temporal interest point detector for human action recognition in complex scenes , 2011, 2011 International Conference on Computer Vision.

[52]  John D. Steeves,et al.  Computer vision-based classification of hand grip variations in neurorehabilitation , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[53]  Darko Kirovski,et al.  Real-time classification of dance gestures from skeleton animation , 2011, SCA '11.

[54]  Miriam Vollenbroek-Hutten,et al.  Chronic pain rehabilitation with a serious game using multimodal input , 2011, 2011 International Conference on Virtual Rehabilitation.

[55]  Quoc V. Le,et al.  Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis , 2011, CVPR 2011.

[56]  Qingxiang Wang,et al.  Design of the workstation for hand rehabilitation based on data glove , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

[57]  L. Enrique Sucar,et al.  Gesture therapy: A vision-based system for upper extremity stroke rehabilitation , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[58]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[59]  Wanqing Li,et al.  Action recognition based on a bag of 3D points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[60]  Florent Perronnin,et al.  Large-scale image retrieval with compressed Fisher vectors , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[61]  Hiroshi Yokoi,et al.  Development of hand rehabilitation system for paralysis patient – universal design using wire-driven mechanism – , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[62]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[63]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[64]  Luc Van Gool,et al.  An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector , 2008, ECCV.

[65]  C. Schmid,et al.  A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.

[66]  E. Patchick,et al.  The treatment of phantom limb pain using immersive virtual reality: Three case studies , 2007, Disability and rehabilitation.

[67]  Ramakant Nevatia,et al.  Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost , 2006, ECCV.

[68]  M. Catani,et al.  The rises and falls of disconnection syndromes. , 2005, Brain : a journal of neurology.

[69]  Ivan Laptev.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

[70]  F. Gordin,et al.  Bacterial Contamination of Computer Keyboards in a Teaching Hospital , 2003, Infection Control & Hospital Epidemiology.

[71]  C. Bell.  Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision: DSM-IV-TR Quick Reference to the Diagnostic Criteria from DSM-IV-TR , 2001 .

[72]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[73]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[74]  Xiaodong Yang,et al.  Effective 3D action recognition using EigenJoints , 2014, J. Vis. Commun. Image Represent..

[75]  Guang Li,et al.  Sign Language Recognition and Translation with Kinect , 2013 .

[76]  D. L. Gall,et al.  Evaluation des apraxies gestuelles , 2003 .

[77]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[78]  N. Otsu.  A Threshold Selection Method from Gray-Level Histograms , 1979, IEEE Trans. Syst. Man Cybern..

[79]  Heng Wang,et al.  Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.

[80]  Ming Yang,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 3d Convolutional Neural Networks for Human Action Recognition , 2022 .