Verification of the blobby quaternion model of human joint limits. (January 2018)

• Record Type: Journal Article
• Authors: Szczęsna, Agnieszka
• Is Part Of: Biomedical signal processing and control. Volume 39(2018)
• Page Start: 130
• Page End: 138
• Publication Date: 2018-01
• Subjects: Quaternion -- Isosurface -- Quaternion volume -- Joint limit -- Motion capture; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.07.029
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The quaternion blobby model for constraining the joint range of motions is proposed. The model connects implicit surface modeling and quaternion volume. The algorithm to reprojection any orientation to the nearest valid ones is proposed. The model is based on captured motion data. The model is flexible and can be fitted to individual joint data. The extensive tests based on captured shoulder joint data are discussed. Using of two metrics to generate the model is discussed Abstract: The quaternion blobby model for constraining the joint range of motions based on real captured data has been proposed. The boundary of the feasible region is modeled using a geometric approach. The proposed method aims at generating an implicit representation of quaternion volume field boundaries which represent the space of all possible and permitted orientations in the joint. The implicit surface is generated as an isosurface of quaternion volume. This approximation volume is determined based on data captured by the optical motion capture system and transformed to unit quaternions. The isosurface is generated from the blobby model which is popular as a solid object modeling tool in computer graphics. The obtained quaternion orientation space represents valid orientations and allows to reproject any orientation to the nearest valid ones. The model was verified based on motion captured shoulder joint data.