Posture similarity index: A method to compare hand postures in synergy space
- Published
- Accepted
- Subject Areas
- Bioengineering, Neuroscience, Kinesiology, Computational Science
- Keywords
- Hand, Posture, Synergy, Posture similarity index, Representative postures
- Copyright
- © 2018 Bhatt et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Posture similarity index: A method to compare hand postures in synergy space. PeerJ Preprints 6:e3080v2 https://doi.org/10.7287/peerj.preprints.3080v2
Abstract
Background
The Human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer. Central Nervous System (CNS) uses different strategies in different manipulation tasks based on task requirements. Several attempts to compare postures of the hand have been made. Some of these have been developed for use in Robotics and animation industries. In this study, we develop an index to quantify the similarity between two human hand postures, the posture similarity index.
Methods
Twelve right-handed volunteers performed 70 postures and lifted and held 30 objects (total of 100 different postures, each performed 5 times). Kinematics of individual finger phalanges (segments) were captured by using a 16-sensor electromagnetic tracking sensor system. The hand was modelled as a 21-DoF system and the corresponding joint angles were computed. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We developed a posture similarity index (PSI), that represents similarity between posture in the synergy (Principal component) space. First, performance of this index was tested using a synthetic dataset. After confirming that it performs well with synthetic dataset, we used it to analyse experimental data. Further, we used PSI to identify postures that are representative in the sense that they have a greater overlap (in synergy space) with a large number of postures.
Results
Using synthetic data and real experimental data, it was found that PSI was a relatively accurate index of similarity in synergy space. Also, it was found that more special postures than common postures were found among “representative” postures.
Conclusion
An index for comparing posture similarity in synergy space has been developed and its use has been demonstrated using synthetic dataset and experimental dataset. In addition, we found that special postures are actually special in the sense that there are more of them in the “representative” postures as identified by our posture similarity index.
Author Comment
We got one round of peer review from PeerJ. This is the final revised version.