A Simple Algorithm for Assimilating Marker-Based Motion Capture Data During Periodic Human Movement Into Models of Multi-Rigid-Body Systems

Abstract

Human movement analysis is often performed with a model of multi-rigid-body system, whereby reflective-marker-based motion capture data are assimilated into the model for characterizing kinematics and kinetics of the movements quantitatively. Accuracy of such analysis is limited, due to motions of the markers on the skin relative to the underlying skeletal system, referred to as the soft tissue artifact (STA). Here we propose a simple algorithm for assimilating motion capture data during periodic human movements, such as bipedal walking, into models of multi-rigid-body systems in a way that the assimilated motions are not affected by STA. The proposed algorithm assumes that STA time-profiles during periodic movements are also periodic. We then express unknown STA profiles using Fourier series, and show that the Fourier coefficients can be determined optimally based solely on the periodicity assumption for the STA and kinematic constraints requiring that any two adjacent rigid-links are connected by a rotary joint, leading to the STA-free assimilated motion that is consistent with the multi-rigid-link model. To assess the efficiency of the algorithm, we performed a numerical experiment using a dynamic model of human gait composed of seven rigid links, on which we placed STA-affected markers, and showed that the algorithm can estimate the STA accurately and retrieve the non-STA-affected true motion of the model. We also confirmed that our STA-removal processing improves accuracy of the inverse dynamics analysis, suggesting the usability of the proposed algorithm for gait analysis.

A novel method for in vivo measurement of dynamic ischiofemoral space based on MRI and motion capture

“…Human motion analysis usually employs a multi-rigid system model that assimilates reflection marker-based motion capture data into the model to quantitatively describe the kinematic (such as the hip joint center) and dynamical (such as the joint force) features of motion ( Suzuki et al, 2018 ). One commonly used in vivo method, a combined motion capture system, and reverse engineering software (such as Opensim), is suitable for measuring the large joint force and muscle strength ( Li et al, 2021 ).…”

Accuracy measurement of different marker based motion analysis systems for biomechanical applications: A round robin study

“…Because the skin with the attached markers is moving relative to the anatomical landmarks during motion and a model to determine the joint centers always contain errors, resulting measurement errors of a few millimeters and a few degrees are unavoidable [ 2 , 4 , 5 ]. Algorithms were developed to limit errors due to soft tissue motion and increase the measurement accuracy [ 6 ]. However, in the mentioned research fields, typically the measurement accuracy does not need to be beyond one millimeter or one degree.…”

Towards Motion Capture with Minimal Sensing

“…Such cases can be solved using interpolation (over typically short periods of time) with information from the remaining markers. Such interpolations can effectively be achieved using linear motion modeling with a Kalman Filter [11] or by exploiting knowledge of rigid body segments of the human body [80,190].…”