The Influence of BSP Estimate Errors on Dynamics Analyses

quantifying the effect of BSP estimate errors on dynamics quantities derived from kinematic data.

Several researchers have performed such sensitivity analyses in recent years, for various dynamics applications. Some have claimed that BSP errors had only a small effect on IDA-calculated net joint moments at the ankle during dynamic jumping activities (Arampatzis et al., 1997) and the net joint forces and moments at the hip, knee and ankle joints during walking (Challis, 1996; Davis, 1992; Pearsall and Costigan, 1999). Krabbe et al. (1997) even suggested that the contribution of segmental inertial components to IDA calculations of net ankle joint forces and moments during the stance phase of running at 5 ms-1 were so

2.2.3

The Influence of BSP Estimate Errors on Dynamics

Analyses

Kwon by up to 3.5% of body eight and mean airborne angular momentum varied by up to 10.4% between insignificant that they could be ignored. Challis (1996) reported that segmental moment of inertia errors of up to 8% only had small effects on net joint moments on the specific activities of walking, vertical jumping and rapid elbow extensions8. For walking and vertical jumping, he reported %RMS differences between the resultant joint moments calculated with perturbed and non-perturbed BSPs of less than 2%. For the rapid elbow extensions, 5% perturbations to forearm moments of inertia produced 4.1% RMS differences. He did not assess the error propagation caused by errors in segmental mass and centre of mass BSPs; however, these types of BSP errors may have a greater effect on net joint forces and moments for activities involving greater segmental accelerations and, in some cases, lower external loads, such as the swing phase of gait or kicking motions (Ganley and Powers, 2004b). Challis (1996) pointed out that segmental moment of inertia errors might have a greater effect on other biomechanical analysis approaches than they did on his IDA calculations. This was the case for the ten BSP estimation techniques assessed by Kwon (1996). CM calculated by a SK analysis varied between the methods assessed by

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methods. For simulated sitting and standing activities, Lenzi et al. (2003) found that combined errors of up to 10% in shank-foot, thigh and head-arms-trunk BSPs produced RMS errors in CM displacement of up to 20% of the total CM displacement range exhibited during such activities.

imal differences were observed during the second ull phase, the phase during which the shank and thigh accelerations are at their Arampatzis et al. (1997) claimed that using BSPs derived by either the method of Zatsiorsky or Hanavan9 did not make “any great difference” to calculated net joint moments at the hip, knee and ankle joints during dynamic jumping activities. Considering net joint moment differences ranged between ±8%, ±5% and ±3%, respectively, their claim is questionable, particularly for the hip results. Likewise, the claim of Ganley and Powers (2004a) is questionable. They stated, “based on gait analysis of three children, it is likely that the differences between DXA- derived and cadaver-based estimates would have a negligible effect on the calculation of net joint moments during gait in 7-13 year-old children.” However, conservative interpretation of their reported results indicates that the maximum difference in net hip joint moment calculations for the 7 year-old subject was at least 12%. Chiu and Salem (2005) calculated the BSPs of an elite male weight-lifter using a DEXA scanner and by using the regression equations of Dempster (1955). During a snatch pull exercise, they found the two sets of BSPs produced differences in calculated net knee and hip joint moments of up to 5% and 10%, respectively. The max

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greatest. Andrews and Mish (1996) demonstrated that, for a simulated rigid shank-foot segment oscillating through 45 degrees with a period of one second, a 5% perturbation of the segment’s three BSPs elicited up to a 12% error in net knee joint moment calculations.

9 _{The specific references for Zatsiorsky and Hanavan were not cited by Arampatzis et al. (1997),}

Pearsall and Reid 994) argued that as biomechanical models become more complex, the need for

Kerwin, 1996) such as throwing (Pearsall and Costigan, 1999), icking (Ganley and Powers, 2004b) and the swing phase of gait (Chester and Many of the aforementioned researchers have asserted or implied that small differences in calculated dynamics quantities caused by anticipated BSP errors are of no practical significance. Conversely, in their review paper,

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accurate, individual-specific BSPs becomes more critical in order to prevent errors in calculated dynamics quantities “arising from BSP lacking the sensitivity equivalent to the model’s goal.” Nigg (1999) stated that BSP accuracy is less important for within-subjects study designs. However, following this argument, small differences in calculated dynamics quantities caused by anticipated BSP errors are clearly more important for between-subjects designs, which are more common than within-subject designs in biomechanics research.

Regardless of experimental design, the sensitivity of various dynamics quantities to BSP errors depends upon the movement activity under investigation (Challis and Kerwin, 1996; Challis, 1999; Kingma et al., 1996b). For example, BSP errors would have a relatively more significant effect on net joint force and moment calculations for certain open-loop movements. These movements would include activities during which limb segments undergo relatively large accelerations, such as running (Pearsall and Costigan, 1999) and activities with low external loads (Challis and

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hniques and to assess the effects of BSP estimate rrors on many practical applications. BSP-dependent dynamics quantities appear

e, defendable and widely accepted means of evaluating living-subject BSP estimation methods. Hence, combined dynamics and optimisation BSP estimation techniques warrant further investigation, because such techniques are based on the same underpinning principles. The results of aforementioned sensitivity analyses also help to identify movement patterns, dynamics quantities and objective function formulations that might be effective for estimating various BSPs.

2.3

Summary

or measurement chnique has been demonstrated to be accurate and reliable for all applications. Gagnon, 1999a; Larivière and Gagnon, 1999b; Plamondon et al., 1996). The top-down approach was demonstrated to be more sensitive to perturbations in trunk BSPs.

BSP-dependent dynamics calculations have been used frequently to compare different BSP estimation tec

e

to provide the most objectiv

In section 2.1 of this review, some promising IA methods for determining CM trajectory for quiet stance and more dynamic activities that require only force platform data were identified. These methods need to be improved by eradicating sources of drift error. Accounting for force platform measurement errors and accurately estimating initial CM conditions are crucial to this process. In section 2.2, it was argued that various dynamics calculations require accurate BSP estimates, and that no existing subject-specific BSP estimation

n methods for subject-specific BSP estimation and improved dynamics solutions warrant further investigation,

thods of CM trajectory determination and bined dynamics and optimisation methods of subject-specific BSP

following chapters. Combined dynamics and optimisatio

recognising that BSP-dependent dynamics quantities provide the most objective means of evaluating living-subject BSP estimation methods. Potential methodological improvements to IA me

to com

i

ove eneral

p

3.1

y prevent the IA from being appropriate for many movement studies. SK approaches, and other dynamics calculations, require accurate kinematics data and BSP estimates. BSP estimates are often of dubious accuracy for the specific subject and movement activity under investigation. A safe, accurate and inexpensive method for subject-specific BSP estimation that can be applied routinely in biomechanics laboratories has not been demonstrated in the literature. Optimisation techniques may provide means of overcoming some of the limitations of both IA and SK approaches.

3.2

Aims

The overall objective of this research is to explore different ways to improve the representation of sagittal plane whole body human dynamics using nonlinear optimisation techniques. Fig. 4 outlines the two broad aims of the research and

3.

RESEARCH AIMS

Th s chapter outlines the rationale for the research described hereafter. The rall objective and broad aims of this work are declared, as are the g

ap roaches employed to achieve them.