Gesture-Context Adaptive Kalman Filtering using ANN for Enhanced Motion Tracking with IMUs

Abstract

Accurate motion tracking is critical in robotics, augmented reality, and assistive technologies. However, low-cost Inertial Measurement Units (IMUs) often suffer from drift and noise. While standard Kalman filters are commonly used to address these issues, their fixed noise parameters limit their effectiveness across varying gesture dynamics. This paper presents a novel gesture-context adaptive Kalman filter that uses a Random Forest classifier for gesture recognition and a neural network to adjust the measurement noise covariance dynamically. Results show significant improvements over standard Kalman filtering, reducing pitch Mean Absolute Error (MAE) from 4.22° to 1.70° and increasing Signal-to-Noise Ratio (SNR) from 2.44 dB to 5.16 dB, with similar gains for roll. This approach advances adaptive tracking performance in wearable, human-interactive systems.