Generating physically realistic kinematic and dynamic models from small data sets: An application for sit-to-stand actions

Kinematic and dynamic models are used to create simplified, yet accurate representations of reality. In application to biological systems, there is often a choice on what level of complexity is appropriate for the model. This paper introduces a structured method for obtaining an accurate model that can represent the sit-to-stand motion and reproduce the associated contact forces in the standing phase. These models are generated from small datasets, just five measured sit-to-stand actions, and result in simple, physically realisable dynamic models. The assumptions made apriori on the model are minimal, with the number of segments, axes of rotation, marker allocation and location, and dynamic model all determined from this small dataset. From this initial analysis, the use of a triple pendulum with a simple point mass at the centre of the torso was found to be representative. Through the generation of these simple, repeatable models, this work aims to develop a modelling framework that is suitable for the study of biological systems and clinical use.

[1]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[2]  Ruzena Bajcsy,et al.  Development and Application of Stereo Camera-Based Upper Extremity Workspace Evaluation in Patients with Neuromuscular Diseases , 2012, PloS one.

[3]  Oussama Khatib,et al.  Using haptics to probe human contact control strategies for six degree-of-freedom tasks , 2014, 2014 IEEE Haptics Symposium (HAPTICS).

[4]  Wolfram Burgard,et al.  Automatic bone parameter estimation for skeleton tracking in optical motion capture , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Rachid Aissaoui,et al.  Biomechanical analysis and modelling of sit to stand task: a literature review , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[6]  Robb J Muirhead,et al.  The five times sit to stand test: responsiveness to change and concurrent validity in adults undergoing vestibular rehabilitation. , 2006, Journal of vestibular research : equilibrium & orientation.

[7]  Ruzena Bajcsy,et al.  Calculating Reachable Workspace Volume for Use in Quantitative Medicine , 2014, ECCV Workshops.

[8]  Michael Damsgaard,et al.  Analysis of musculoskeletal systems in the AnyBody Modeling System , 2006, Simul. Model. Pract. Theory.

[9]  Gentiane Venture,et al.  Motion capture based identification of the human body inertial parameters , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Roman Kamnik,et al.  Model based inertial sensing of human body motion kinematics in sit-to-stand movement , 2008, Simul. Model. Pract. Theory.

[11]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[12]  Gentiane Venture,et al.  A numerical method for choosing motions with optimal excitation properties for identification of biped dynamics - An application to human , 2009, 2009 IEEE International Conference on Robotics and Automation.

[13]  B. E. Maki,et al.  Measuring balance in the elderly: validation of an instrument. , 1992, Canadian journal of public health = Revue canadienne de sante publique.

[14]  M A Hughes,et al.  Chair rise strategies in the elderly. , 1994, Clinical biomechanics.

[15]  Gentiane Venture,et al.  Optimal estimation of human body segments dynamics using realtime visual feedback , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Gentiane Venture,et al.  Real-time implementation of physically consistent identification of human body segments , 2011, 2011 IEEE International Conference on Robotics and Automation.

[17]  John Rasmussen,et al.  Evaluation of lower extremity musculo-skeletal model using sit-to-stand movement. , 2003 .

[18]  R J Full,et al.  Templates and anchors: neuromechanical hypotheses of legged locomotion on land. , 1999, The Journal of experimental biology.

[19]  A Cappozzo,et al.  A telescopic inverted-pendulum model of the musculo-skeletal system and its use for the analysis of the sit-to-stand motor task. , 1999, Journal of biomechanics.

[20]  Stepán Obdrzálek,et al.  Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Philippe Lemoine,et al.  OpenSYMORO: An open-source software package for symbolic modelling of robots , 2014, 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[22]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[23]  Wolfram Burgard,et al.  Automatic initialization for skeleton tracking in optical motion capture , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[25]  R Riener,et al.  Biomechanical analysis of sit-to-stand transfer in healthy and paraplegic subjects. , 2000, Clinical biomechanics.