Abstract
The biomechanical models used to refine and stabilize motion capture processes are almost invariably driven by joint center estimates, and any errors in joint center calculation carry over and can be compounded when calculating joint kinematics. Unfortunately, accurate determination of joint centers is a complex task, primarily due to measurements being contaminated by soft-tissue artifact (STA). This paper proposes a novel approach to joint center estimation implemented via sequential application of single-frame optimization (SFO). First, the method minimizes the variance of individual time frames’ joint center estimations via the developed variance minimization method to obtain accurate overall initial conditions. These initial conditions are used to stabilize an optimization-based linearization of human motion that determines a time-varying joint center estimation. In this manner, the complex and nonlinear behavior of human motion contaminated by STA can be captured as a continuous series of unique rigid-body realizations without requiring a complex analytical model to describe the behavior of STA. This article intends to offer proof of concept, and the presented method must be further developed before it can be reasonably applied to human motion. Numerical simulations were introduced to verify and substantiate the efficacy of the proposed methodology. When directly compared with a state-of-the-art inertial method, SFO reduced the error due to soft-tissue artifact in all cases by more than 45%. Instead of producing a single vector value to describe the joint center location during a motion capture trial as existing methods often do, the proposed method produced time-varying solutions that were highly correlated (r > 0.82) with the true, time-varying joint center solution.
Highlights
In the 140-plus years since its inception, motion capture has grown from a novelty used to win bets into a ubiquitous tool for use in fields such as animation, healthcare, industry, sports, and the military [1,2,3,4,5]
It is obvious from the time scale that the soft-tissue artifact (STA) simulated in Cases 1–4 is not realistic
The results suggest that single-frame optimization (SFO) can produce viable results for a more realistically simulated STA, and that higher frequencies are more difficult to capture
Summary
In the 140-plus years since its inception, motion capture has grown from a novelty used to win bets into a ubiquitous tool for use in fields such as animation, healthcare, industry, sports, and the military [1,2,3,4,5]. Optical motion capture is currently considered the field’s gold standard, and it works by using cameras to visually deduce the position and posture of a subject who is covered in markers [6,7]. The paramount importance of accurate calculation of joint axes and joint centers in determining accurate joint kinematics has spawned much research into methods of using optical markers to determine them [8,9,10,11,12]. Determination of joint center locations can be accomplished via predictive methods, such as the Harrington Equations, where regression equations are applied to a subject’s anthropometric measurements [13]. Marker-based methods for direct computation of joint centers can be categorized as sphere-fitting or transformation approaches, or collectively as functional methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
