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

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

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 technique