Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patient's home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution.

[1]  T. Schmitz-Rode,et al.  Introducing a feedback training system for guided home rehabilitation , 2010, Journal of NeuroEngineering and Rehabilitation.

[2]  C. A. Dairaghi,et al.  Concurrent neuromechanical and functional gains following upper-extremity power training post-stroke , 2013, Journal of NeuroEngineering and Rehabilitation.

[3]  Gentiane Venture,et al.  Real-time identification and visualization of human segment parameters , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Marcus J Fuhrer,et al.  Rehabilitation medicine summit: building research capacity Executive Summary , 2006, Journal of NeuroEngineering and Rehabilitation.

[5]  Gentiane Venture,et al.  Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems , 2014, Int. J. Robotics Res..

[6]  Philippe Fraisse,et al.  Online identification and visualization of the statically equivalent serial chain via constrained Kalman filter , 2013, 2013 IEEE International Conference on Robotics and Automation.

[7]  Idsart Kingma,et al.  Comparison of a laboratory grade force platform with a Nintendo Wii Balance Board on measurement of postural control in single-leg stance balance tasks. , 2013, Journal of biomechanics.

[8]  Marjorie Skubic,et al.  Evaluation of an inexpensive depth camera for passive in-home fall risk assessment , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[9]  Philippe Fraisse,et al.  Center of Mass Estimation for Rehabilitation in a Multi-contact Environment: A Simulation Study , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[10]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[11]  T. M. Owings,et al.  Body segment inertial parameter estimation for the general population of older adults. , 2002, Journal of biomechanics.

[12]  Zvi S. Roth,et al.  Fundamentals of Manipulator Calibration , 1991 .

[13]  Philippe Fraisse,et al.  Estimation of the center of mass with Kinect and Wii balance board , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  John H Challis,et al.  A simple method to determine body segment masses in vivo: reliability, accuracy and sensitivity analysis. , 2003, Clinical biomechanics.

[15]  F. Prince,et al.  Comparison of three methods to estimate the center of mass during balance assessment. , 2004, Journal of biomechanics.

[16]  David A. Winter,et al.  Human balance and posture control during standing and walking , 1995 .

[17]  Andrew P. Murray,et al.  Estimation of the centre of mass from motion capture and force plate recordings: A study on the elderly , 2011 .

[18]  Jan Stegenga,et al.  Exergaming for balance training of elderly: state of the art and future developments , 2013, Journal of NeuroEngineering and Rehabilitation.

[19]  S. Cotton,et al.  Estimation of the Center of Mass: From Humanoid Robots to Human Beings , 2009, IEEE/ASME Transactions on Mechatronics.

[20]  Yoshiyuki Sankai,et al.  Application of Robot Suit HAL to Gait Rehabilitation of Stroke Patients: A Case Study , 2012, ICCHP.

[21]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[22]  Lena H Ting,et al.  Accuracy of force and center of pressure measures of the Wii Balance Board. , 2014, Gait & posture.

[23]  Paul McCrory,et al.  Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance. , 2010, Gait & posture.

[24]  Zhongying Zhao,et al.  Boolean genetic network model for the control of C. elegans early embryonic cell cycles , 2013, BioMedical Engineering OnLine.

[25]  P. Leva Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters. , 1996 .

[26]  E. van Lunteren,et al.  Improvement of diaphragm and limb muscle isotonic contractile performance by K+ channel blockade , 2010, Journal of NeuroEngineering and Rehabilitation.

[27]  Yang Yang,et al.  Reliability and Validity of Kinect RGB-D Sensor for Assessing Standing Balance , 2014, IEEE Sensors Journal.

[28]  R. Baker Gait analysis methods in rehabilitation , 2006, Journal of NeuroEngineering and Rehabilitation.

[29]  Timothy Bretl,et al.  Testing Static Equilibrium for Legged Robots , 2008, IEEE Transactions on Robotics.

[30]  Ragou Ady,et al.  A PERSONAL ROBOT INTEGRATING A PHYSICALLY-BASED HUMAN MOTION TRACKING AND ANALYSIS , 2013 .

[31]  David A. Winter,et al.  Biomechanics and Motor Control of Human Movement , 1990 .

[32]  Mark Andrew Jaffrey,et al.  Estimating centre of mass trajectory and subject-specific body segment parameters using optimisation approaches , 2008 .

[33]  Wisama Khalil,et al.  Modeling, Identification & Control of Robots , 2002 .

[34]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[35]  Ronan Boulic,et al.  On the Computation and Control of the Mass Center of Articulated Chains , 1998 .

[36]  Linda Denehy,et al.  Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.

[37]  V. Dietz,et al.  Clinical assessments performed during robotic rehabilitation by the gait training robot Lokomat , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[38]  Torsten Bumgarner,et al.  Biomechanics and Motor Control of Human Movement , 2013 .

[39]  Elena Oggero,et al.  Biomedical instruments versus toys:a preliminary comparison of force platforms and the nintendo wii balance board - biomed 2011. , 2011, Biomedical sciences instrumentation.