An individual and dynamic Body Segment Inertial Parameter validation method using ground reaction forces
Introduction
Calculating Body Segment Inertial Parameters (BSIP) has been shown to be of critical importance for clinical and biomechanical research (Andrews and Mish, 1996, Kingma et al., 1995, Silva and Ambrósio, 2004, Rao et al., 2006, Pai, 2010). The measurement of the inertia and the position of the COM of each body part allows us to monitor variations in muscle-mass during hospitalization, rehabilitation or neurological examination. Consequently the knowledge of the individual inertial parameters is of crucial importance to support personalized healthcare. The better the inertial estimation of those segments is, the better are the resulting joint loads (force and moment) obtained by inverse dynamics (Pearsall and Costigan, 1999, Rao et al., 2006, Pàmies-Vilàa et al., 2012). Previous research has been conducted to improve the BSIP estimation using geometric models based on numerous anthropometric measurements (Hanavan Jr., 1964) or taking results from cadavers׳ studies (Dempster, 1955, Chandler et al., 1975) as well as in vivo body scanning methods (Zatsiorsky and Seluyanov, 1983, Zatsiorsky et al., 1990, de Leva, 1996, Ma et al., 2011). Even though the estimations have been improved (Dumas et al., 2007) there are still regression methods based on earlier collected databases e.g. (McConville et al., 1980, Young et al., 1983). Geometrical methods are precise and based on complex acquisition systems such as 3D scanner, IRM or X-ray absorptiometry which are expensive and may expose subjects to radiations. Recently, identification methods used in Mechanical Engineering have been applied to the estimation of human BSIP (Atchonouglo et al., 2008, Venture et al., 2009b, Venture et al., 2009c, Ayusawa et al., 2011). These methods are based on human body mechanical models whose parameters are expected to match kinematic and dynamic recorded data. Therefore, they allow evaluating BSIP on a subject-by-subject basis using an optoelectronic motion capture system and a force platform.
In this framework, this paper proposes to evaluate an identification method (IM) to assess the inertial parameters of humans without considering joint torques. It is based on the fact that the dynamics of such systems can be written using the Newton–Euler formalism for the base-link (chosen arbitrarily) and the Lagrangian formalism for the rest of the kinematic chains (Venture et al., 2009b, Venture et al., 2009c, Ayusawa et al., 2011). It can be thus, demonstrated that to identify the dynamics of the whole system only the six equations obtained for the base-link are necessary. Since no ground truth value of the BSIP is available, the IM is validated twofold. First the recalculated contact forces, using inverse dynamics, are compared with the ground reaction forces (GRF) measured from a force platform. Secondly the results are compared with the GRF computed using a regression based model (RM) proposed by (Dumas et al., 2007). Additionally, the results are cross validated with a high velocity overarm throwing movement.
The paper is structured as follows: in the Methods section the obtained identification model from the base link equation is briefly described and the experimental identification of the whole-body parameters is presented. In the Results section, the experimental results obtained from both methods are presented and discussed.
Section snippets
Methods
To obtain accurate identification results it is important to define the kinematic model used to describe the human body, and to obtain its characteristic geometric parameters.
Results
In this section we present the results of the comparison between IM and RM. The RMSE, the Pearson Correlation Coefficient, the phase dependent (P) and magnitude dependent (M) and combined error (C) computations are shown in Table 2. The correlation between the IM and the force plate is very high (>0.99) and the RMSE and P, M and C are smaller compared to the obtained results by RM (Table 3).
Further investigations of each force plate output show that the highest correlations for both BSIP
Discussion
The present study was aimed to validate and compare an identification method of subject specific BSIP estimation with results from a regression based model (Dumas et al., 2007). The BSIP estimation strongly depends on the segment׳s length, mass and geometric shape. During gait, errors in BSIP values have significant effects on kinetic parameters e.g. variations of the hip flexion/extension torque up to 20% depending on the chosen model (Rao et al., 2006). These differences seem to be more
Conclusion
This research was conducted to validate a non-invasive and not radiation based method to obtain individual BSIP for each subject. In comparison with the measured ground reaction forces the IM approach has shown its advantages compared to models proposed in previous research (Dumas et al., 2007). Our work does not contradict previous research but simply provides a new approach to compare BSIPs. The technique is not time consuming and DOF and segments can easily be added or removed upon needs.
Conflict of interest statement
No conflict of interest.
Acknowledgments
This study was supported by the Japan Society for the Promotion of Science (JSPS/FF1/391 ID no. PE 12509). Special thanks to Prof. Yoshihiko Nakamura and Dr. Ko Ayusawa from the University of Tokyo for technical discussions and to Yusuke Ogawa for his kind help during the data acquisition.
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