Approximation of Inertial Measurement Unit Data to Time Series Kinematic Data Through Correlation Analysis and Machine Learning

William Fröhlich, Rafael Bittencourt, Sandro Rigo, Rafael Baptista, César Marcon

2025

Abstract

Accurate results are traditionally obtained in gait analysis using gold-standard methods such as motion capture with kinematic cameras and force platforms in biomechanics labs. However, these techniques are expensive, time-consuming, and require controlled environments, limiting their accessibility for more clinical and research applications. This study explores the potential of inertial measurement units as a cost-effective alternative. We focused on extracting features from Inertial Measurement Unit (IMU) data, such as acceleration and angular velocity, and derived metrics like speed and angular acceleration to approximate the accuracy of kinematic camera data. Following extensive preprocessing of inertial and kinematic datasets, we applied analytical methods, including Pearson correlation and cross-correlation, to identify significant relationships between the two data sources. We employed the most strongly correlated features to train Machine Learning models, Clustering techniques to assess the consistency and reliability of the results, and the Random Forest algorithm to train and evaluate the models’ capacity for time series prediction. Our findings suggest that certain aspects of IMU data strongly correlate with kinematic outcomes. This indicates that IMUs can replicate results traditionally obtained through more complex and costly methods under specific conditions.

Paper Citation

in Harvard Style

Fröhlich W., Bittencourt R., Rigo S., Baptista R. and Marcon C. (2025). Approximation of Inertial Measurement Unit Data to Time Series Kinematic Data Through Correlation Analysis and Machine Learning. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 75-86. DOI: 10.5220/0013115800003911

in Bibtex Style

@conference{healthinf25,
author={William Fröhlich and Rafael Bittencourt and Sandro Rigo and Rafael Baptista and César Marcon},
title={Approximation of Inertial Measurement Unit Data to Time Series Kinematic Data Through Correlation Analysis and Machine Learning},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={75-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013115800003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Approximation of Inertial Measurement Unit Data to Time Series Kinematic Data Through Correlation Analysis and Machine Learning
SN - 978-989-758-731-3
AU - Fröhlich W.
AU - Bittencourt R.
AU - Rigo S.
AU - Baptista R.
AU - Marcon C.
PY - 2025
SP - 75
EP - 86
DO - 10.5220/0013115800003911
PB - SciTePress