Investigating Correlation and Similarity Between Inertial Measurement Unit and Kinematic Data in Gait Analysis

William da Rosa Fröhlich, Rafael Bittencourt, Sandro José Rigo, Rafael Baptista, César A. M. Marcon

Abstract: In gait analysis, precise results typically depend on gold-standard techniques such as motion capture with kinematics cameras and force platforms in biomechanics labs. However, these methods are costly, time-consuming, and require controlled environments, limiting their accessibility for clinical and research use. This study investigates inertial measurement units as a costeffective wearable circuit implementation alternative. We focused on extracting features from inertial data, such as acceleration and angular velocity. We derived metrics like speed and angular acceleration to approximate the accuracy of camera kinematic data. After extensive preprocessing of inertial data and kinematic datasets, we explored alternatives, including Pearson correlation and cross-correlation analyses, to identify significant relationships between the data sources. The highest correlated features were used to train machine learning models, which were then analyzed using clustering techniques to evaluate the consistency and reliability of the results. The findings show that certain inertial data aspects strongly correlate with kinematic outcomes, indicating that inertial data can replicate results traditionally obtained through more complex methods under specific conditions.

External IDs:dblp:conf/lascas/FrohlichBRBM25