Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction Activities

Xochicale Miguel and Baber Chris

Abstract

Human movement variability arises from the process of mastering redundant (bio)mechanical degrees of freedom to successfully accomplish any given motor task where flexibility and stability of many possible joint combinations helps to adapt to environment conditions. While the analysis of movement of variability is becoming increasingly popular as a diagnostic tool or skill performance evaluation, there are remain challenges on applying the most appropriate methods. We therefore investigate nonlinear methods such as reconstructed state space (RSSs), uniform time-delay embedding, recurrence plots (RPs) and recurrence quantification analysis (RQAs) with real-world time-series data of wearable inertial sensors. That said, twenty healthy participants imitated vertical and horizontal arm movements in normal and faster velocity from an humanoid robot. We applied nonlinear methods to the collected data to found visual differences in the patterns of RSSs and RPs and statistical differences with RQAs. We conclude that Shannon Entropy with RQA is a robust method that helps to quantify activities, types of sensors, windows lengths and level of smoothness. Hence this work might enhance the development of better diagnostic tools for applications in rehabilitation and sport science for skill performance or new forms of human-humanoid interaction for quantification of movement adaptations and motor pathologies.

Manuscript*

$GitHub Actions Status$
*Manuscript is 100% reproducible work meaning that code, data, tex project and CI-github action with free-cortex framework) is open accessible.

Code and data

Guidelines and instructions to reproduce the results of manuscript are available in the following paths: code and data.

Pre-print in arXiv and papers with code

Licence and Citation

The work of this manuscript is under Creative Commons Attribution-ShareAlike 4.0 International License . Hence, you are free to reuse it and modify it as much as you want and as long as you cite this manuscript as original reference and you re-share your work under the same terms. CC-BY-SA.md was downloaded from Creative Commons Markdown.

BibTeX for pre-print in arXiv

@misc{xochicale2021,
      title={	Nonlinear methods to quantify Movement Variability 
		in Human-Humanoid Interaction Activities}, 
      author={Miguel Xochicale and Chirs Baber},
      year={2021},
      eprint={1810.09249},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}

BibTex for code and data in Zenodo

@software{miguel_xochicale_2021_4661227,
  author       = {Miguel Xochicale},
  title        = {{Code and data for "Nonlinear methods to quantify 
                   Movement Variability in Human-Humanoid Interaction
                   Activities"}},
  month        = apr,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.4661227},
  url          = {https://doi.org/10.5281/zenodo.4661227}
}

Contact

For specific questions about the content of this repository, please contact Miguel Xochicale. If your question might be relevant to other people, please instead open an issue.

mxochicale / srep2021