Validation of Automated Mobility Assessment Using a Single 3D Sensor
Abstract
Reliable mobility assessment is essential to diagnose or optimize treatment in persons affected by mobility disorders, e.g., for musculo-skeletal disorders. In this work, we present a system that is able to automatically assess mobility using a single 3D sensor. We validate the system ability to assess mobility and predict the medication state of Parkinson’s disease patients while using a relatively small number of motion tasks. One key component of our system is a graph-based feature extraction technique that can capture the dynamic coordination between parts of the body while providing results that are easier to interpret than those obtained with other data-driven approaches. We further discuss the system and the study design, highlighting aspects that provide insights for developing mobility assessment applications in other contexts.
Cite
Text
Kao et al. "Validation of Automated Mobility Assessment Using a Single 3D Sensor." European Conference on Computer Vision Workshops, 2016. doi:10.1007/978-3-319-48881-3_12Markdown
[Kao et al. "Validation of Automated Mobility Assessment Using a Single 3D Sensor." European Conference on Computer Vision Workshops, 2016.](https://mlanthology.org/eccvw/2016/kao2016eccvw-validation/) doi:10.1007/978-3-319-48881-3_12BibTeX
@inproceedings{kao2016eccvw-validation,
title = {{Validation of Automated Mobility Assessment Using a Single 3D Sensor}},
author = {Kao, Jiun-Yu and Nguyen, Minh and Nocera, Luciano and Shahabi, Cyrus and Ortega, Antonio and Winstein, Carolee J. and Sorkhoh, Ibrahim and Chung, Yu-Chen and Chen, Yi-An and Bacon, Helen},
booktitle = {European Conference on Computer Vision Workshops},
year = {2016},
pages = {162-177},
doi = {10.1007/978-3-319-48881-3_12},
url = {https://mlanthology.org/eccvw/2016/kao2016eccvw-validation/}
}