On the Agreement of Commodity 3D Cameras

The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitio ...

[1]  Ying Wu,et al.  Mining actionlet ensemble for action recognition with depth cameras , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Choong Yeon Kim,et al.  Validation of feasibility of two depth sensor-based Microsoft Kinect cameras for human abduction-adduction motion analysis , 2016 .

[3]  Franck Multon,et al.  Validation of an ergonomic assessment method using Kinect data in real workplace conditions. , 2017, Applied ergonomics.

[4]  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.

[5]  Majid Mirmehdi,et al.  A comparative study of pose representation and dynamics modelling for online motion quality assessment , 2016, Comput. Vis. Image Underst..

[6]  Marjorie Skubic,et al.  Validation of the Microsoft Kinect as a Portable and Inexpensive Screening Tool for Identifying ACL Injury Risk , 2014, Orthopaedic Journal of Sports Medicine.

[7]  Jonathan Tompson,et al.  Towards Accurate Multi-person Pose Estimation in the Wild , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Martin Masek,et al.  Joint movement similarities for robust 3D action recognition using skeletal data , 2015, J. Vis. Commun. Image Represent..

[9]  Gérard G. Medioni,et al.  Home Monitoring Musculo-skeletal Disorders with a Single 3D Sensor , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Luc Van Gool,et al.  Coupled Action Recognition and Pose Estimation from Multiple Views , 2012, International Journal of Computer Vision.

[11]  Jonathan Tompson,et al.  PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model , 2018, ECCV.

[12]  Winfried Ilg,et al.  Validation of enhanced kinect sensor based motion capturing for gait assessment , 2017, PloS one.

[13]  Jun Kong,et al.  Informative joints based human action recognition using skeleton contexts , 2015, Signal Process. Image Commun..

[14]  Bert Coolen,et al.  Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor , 2017, Sensors.

[15]  Majid Mirmehdi,et al.  Online quality assessment of human motion from skeleton data , 2014, BMVC.

[16]  Welf Löwe,et al.  Data-Driven Human Movement Assessment , 2019, KES-IDT.

[17]  Douglas G. Altman,et al.  Measurement in Medicine: The Analysis of Method Comparison Studies , 1983 .

[18]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[19]  Stephen Lin,et al.  A Visual Evaluation Framework for In-Home Physical Rehabilitation , 2014, 2014 IEEE International Symposium on Multimedia.

[20]  Anuj Srivastava,et al.  Accurate 3D action recognition using learning on the Grassmann manifold , 2015, Pattern Recognit..

[21]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[22]  Federica Verdini,et al.  Validation of an optimized algorithm to use Kinect in a non-structured environment for Sit-to-Stand analysis , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[23]  John Hansen,et al.  Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm , 2019, IEEE Sensors Journal.

[24]  Bart Vanrumste,et al.  Validation of the kinect for gait analysis using the GAITRite walkway , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Wenbing Zhao,et al.  A Validation Study of Rehabilitation Exercise Monitoring Using Kinect , 2019, Advanced Methodologies and Technologies in Medicine and Healthcare.

[26]  Welf Löwe,et al.  Towards an Automated Assessment of Musculoskeletal Insufficiencies , 2019, KES-IDT.

[27]  Marjorie Skubic,et al.  Validation of a Kinect V2 based rehabilitation game , 2018, PloS one.

[28]  Jake K. Aggarwal,et al.  View invariant human action recognition using histograms of 3D joints , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[29]  Moataz A. Eltoukhy,et al.  Validation of Static and Dynamic Balance Assessment Using Microsoft Kinect for Young and Elderly Populations , 2018, IEEE Journal of Biomedical and Health Informatics.

[30]  Max E Valentinuzzi,et al.  Statistical Validation for Clinical Measures: Repeatability and Agreement of Kinect™-Based Software , 2018, BioMed research international.

[31]  Jing Wang,et al.  View-robust action recognition based on temporal self-similarities and dynamic time warping , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).

[32]  Adso Fernández-Baena,et al.  Biomechanical Validation of Upper-Body and Lower-Body Joint Movements of Kinect Motion Capture Data for Rehabilitation Treatments , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.