Joint Center Estimation Using Single-Frame Optimization: Part 2: Experimentation

Abstract

Human motion capture is driven by joint center location estimates, and error in their estimation can be compounded by subsequent kinematic calculations. Soft tissue artifact (STA), the motion of tissue relative to the underlying bones, is a primary cause of error in joint center calculations. A method for mitigating the effects of STA, single-frame optimization (SFO), was introduced and numerically verified in Part 1 of this work, and the purpose of this article (Part 2) is to experimentally compare the results of SFO with a marker-based solution. The experimentation herein employed a single-degree-of-freedom pendulum to simulate human joint motion, and the effects of STA were simulated by affixing the inertial measurement unit to the pendulum indirectly through raw, vacuum-sealed meat. The inertial sensor was outfitted with an optical marker adapter so that its location could be optically determined by a camera-based motion-capture system. During the motion, inertial effects and non-rigid attachment of the inertial sensor caused the simulated STA to manifest via unrestricted motion (six degrees of freedom) relative to the rigid pendulum. The redundant inertial and optical instrumentation allowed a time-varying joint center solution to be determined both by optical markers and by SFO, allowing for comparison. The experimental results suggest that SFO can achieve accuracy comparable to that of state-of-the-art joint center determination methods that use optical skin markers (root mean square error of 7.87–37.86 mm), and that the time variances of the SFO solutions are correlated (r = 0.58–0.99) with the true, time-varying joint center solutions. This suggests that SFO could potentially help to fill a gap in the existing literature by improving the characterization and mitigation of STA in human motion capture.

Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review

“…Such joint center position vectors implicitly assume that segments are rigid and connected at one common fixed point. However, possible small joint-translational movements and soft tissue artifacts will violate this model [58,59]. In reality, soft tissue artifacts are present when patients move [60].…”

“…In reality, soft tissue artifacts are present when patients move [60]. Frick et al [58,59] recently proposed a method that identifies the time variations of a joint center position vector due to soft skin movement, but lacks a proper validation. Ideally, prior knowledge is estimated from sensor measurements [20,46,56] rather than measured in a movement laboratory or obtained from anthropometric tables.…”

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

“…STA occurs from unequal movement of soft tissue layers (muscle, tendon, and dermis) between the bone and the skin surface [90]. Typically, relative translation and relative rotation are assumed to show the majority of STA, in which yields to be the components targeted for mitigation [91]. Another way to mitigate STA is by processing the translational acceleration and rotational velocity measured by an IMU [92].…”

Inertial-Based Human Motion Capture: A Technical Summary of Current Processing Methodologies for Spatiotemporal and Kinematic Measures

“…Inertial-based motion capture is also not exempt from the error caused by STA. Where OMC methodologies often use rigid clusters of markers [ 83 ] and/or anatomical modelling assumptions [ 84 ] to reduce the effects of STA, research into the reduction of STA effects on IMC is limited [ 85 , 86 ]. Frick et al presented a two-part study using numerical methods to reduce the effect of STA on inertial-based joint centre estimations.…”

“…Although the method showed good agreeance with state-of-the-art OMC joint centre estimations on a mechanical rig, the SFO cost function assumes the joint centre to be undergoing negligible acceleration, which may be violated for many applications. The method proposed by Frick and Rahmatalla [ 85 ] demonstrates the potential in the reduction of STA when using IMC methods; however, further development is required before the SFO method is considered a practical solution for more complex applications [ 85 , 86 ].…”