Inertial Sensor

Inertial sensors, measuring acceleration and angular velocity, are crucial for motion tracking and navigation across diverse applications, from robotics and autonomous vehicles to human activity recognition and healthcare. Current research emphasizes improving accuracy and robustness through sensor fusion techniques (combining data from multiple inertial units or with other sensor modalities like GPS, cameras, or UWB), advanced filtering algorithms (e.g., Kalman filters, extended Kalman filters, and deep learning-based approaches), and novel data-driven methods (including deep neural networks for tasks like noise reduction, calibration, and pose estimation). These advancements are driving significant improvements in the precision and reliability of inertial-based systems, impacting fields ranging from precise localization in GPS-denied environments to more accurate gait analysis for medical diagnostics.