Measuring human movement for biomechanical applications using markerless motion capture

Modern biomechanical and clinical applications require the accurate capture of normal and pathological human movement without the artifacts associated with standard marker-based motion capture techniques such as soft tissue artifacts and the risk of artificial stimulus of taped-on or strapped-on markers. In this study, the need for new markerless human motion capture methods is discussed in view of biomechanical applications. Three different approaches for estimating human movement from multiple image sequences were explored. The first two approaches tracked a 3D articulated model in 3D representations constructed from the image sequences, while the third approach tracked a 3D articulated model in multiple 2D image planes. The three methods are systematically evaluated and results for real data are presented. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice.

[1]  A. Laurentini,et al.  The Visual Hull Concept for Silhouette-Based Image Understanding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Takeo Kanade,et al.  Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  José M. F. Moura,et al.  Capture and Representation of Human Walking in Live Video Sequences , 1999, IEEE Trans. Multim..

[4]  Jessica K. Hodgins,et al.  Interactive control of avatars animated with human motion data , 2002, SIGGRAPH.

[5]  Takeo Kanade,et al.  Shape-From-Silhouette Across Time Part II: Applications to Human Modeling and Markerless Motion Tracking , 2005, International Journal of Computer Vision.

[6]  T. Andriacchi Dynamics of pathological motion: applied to the anterior cruciate deficient knee. , 1990, Journal of biomechanics.

[7]  David J. Fleet,et al.  Learning parameterized models of image motion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  T. Andriacchi,et al.  Knee adduction moment, serum hyaluronan level, and disease severity in medial tibiofemoral osteoarthritis. , 1998, Arthritis and rheumatism.

[9]  Ioannis A. Kakadiaris,et al.  3D human body model acquisition from multiple views , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  Stefano Corazza,et al.  Most favorable camera configuration for a shape-from-silhouette markerless motion capture system for biomechanical analysis , 2005 .

[11]  Jake K. Aggarwal,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2008 .

[12]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[13]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  C. Cobelli,et al.  A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach , 2006, Annals of Biomedical Engineering.

[15]  Takeo Kanade,et al.  Advances in Cooperative Multi-Sensor Video Surveillance , 1999 .

[16]  T. Andriacchi,et al.  A relationship between gait and clinical changes following high tibial osteotomy. , 1985, The Journal of bone and joint surgery. American volume.

[17]  G. Johansson Visual perception of biological motion and a model for its analysis , 1973 .

[18]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[19]  S H Holzreiter,et al.  Assessment of gait patterns using neural networks. , 1993, Journal of biomechanics.

[20]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[23]  Mubarak Shah,et al.  Motion-based recognition a survey , 1995, Image Vis. Comput..

[24]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[25]  Andrea Bottino,et al.  A Silhouette Based Technique for the Reconstruction of Human Movement , 2001, Comput. Vis. Image Underst..

[26]  R A Olshen,et al.  Statistical analysis of gait patterns of persons with cerebral palsy. , 2015, Statistics in medicine.

[27]  Wallace S. Rutkowski,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2022 .

[28]  Dragomir Anguelov,et al.  VALIDATION OF A MARKERLESS MOTION CAPTURE SYSTEM FOR THE CALCULATION OF LOWER EXTREMITY KINEMATICS , 2005 .

[29]  Rama Chellappa,et al.  Multi-camera Tracking of Articulated Human Motion Using Motion and Shape Cues , 2006, ACCV.

[30]  M. Drenth San Juan, Puerto Rico , 2001 .

[31]  M. P. Murray,et al.  Walking patterns of normal women. , 1970, Archives of physical medicine and rehabilitation.

[32]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Azriel Rosenfeld,et al.  3D object tracking using shape-encoded particle propagation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[34]  Takeo Kanade,et al.  Shape-From-Silhouette Across Time Part I: Theory and Algorithms , 2005, International Journal of Computer Vision.

[35]  Jitendra Malik,et al.  Twist Based Acquisition and Tracking of Animal and Human Kinematics , 2004, International Journal of Computer Vision.

[36]  J. Cutting,et al.  Recognizing friends by their walk: Gait perception without familiarity cues , 1977 .

[37]  Rama Chellappa,et al.  Multiple view tracking of humans modelled by kinematic chains , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[38]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[39]  W. Weber,et al.  Mechanik der menschlichen Gehwerkzeuge , 1894 .

[40]  É. Marey,et al.  Animal mechanism : a treatise on terrestrial and aerial locomotion , 2022 .

[41]  Larry S. Davis,et al.  3-D model-based tracking of humans in action: a multi-view approach , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[42]  A. Cappozzo,et al.  Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. , 2004, Gait & posture.

[43]  D. Winter Biomechanical motor patterns in normal walking. , 1983, Journal of motor behavior.

[44]  Annegret Mündermann,et al.  Conditions that influence the accuracy of anthropometric parameter estimation for human body segments using shape-from-silhouette , 2005 .

[45]  T. Andriacchi,et al.  Basic science of the knee and total knee arthroplasty , 1992 .

[46]  A. Garrod Animal Locomotion , 1874, Nature.

[47]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[48]  A. B. Drought,et al.  WALKING PATTERNS OF NORMAL MEN. , 1964, The Journal of bone and joint surgery. American volume.

[49]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..