Open AccessA marker registration method to improve joint angles computed by constrained inverse kinematics
Author(s) -
James J. Dunne,
Thomas K. Uchida,
Thor F. Besier,
Scott L. Delp,
Ajay Seth
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0252425
Subject(s) - orientation (vector space) , computer vision , artificial intelligence , computer science , inverse kinematics , kinematics , joint (building) , image registration , graphical user interface , pattern recognition (psychology) , mathematics , robot , physics , geometry , image (mathematics) , engineering , architectural engineering , classical mechanics , programming language
Accurate computation of joint angles from optical marker data using inverse kinematics methods requires that the locations of markers on a model match the locations of experimental markers on participants. Marker registration is the process of positioning the model markers so that they match the locations of the experimental markers. Markers are typically registered using a graphical user interface (GUI), but this method is subjective and may introduce errors and uncertainty to the calculated joint angles and moments. In this investigation, we use OpenSim to isolate and quantify marker registration–based error from other sources of error by analyzing the gait of a bipedal humanoid robot for which segment geometry, mass properties, and joint angles are known. We then propose a marker registration method that is informed by the orientation of anatomical reference frames derived from surface-mounted optical markers as an alternative to user registration using a GUI. The proposed orientation registration method reduced the average root-mean-square error in both joint angles and joint moments by 67% compared to the user registration method, and eliminated variability among users. Our results show that a systematic method for marker registration that reduces subjective user input can make marker registration more accurate and repeatable.