Artificial intelligencedriven optimization of MEMS navigation sensors for enhanced user experience

This review delves into the key area of artificial intelligence (AI)-driven optimization applied to Microelectromechanical Systems (MEMS) navigation sensors, with the primary objective of enhancing the user experience. Employing a comprehensive research methodology, it extensively explores AI-powered techniques, encompassing sensor fusion, adaptive filtering, calibration, compensation, predictive modeling, and energy efficiency. Through rigorous case studies and empirical evidence, this research provides substantial achievements, including enhanced accuracy, reduced power consumption, heightened reliability, and amplified user satisfaction, across diverse applications such as autonomous vehicles, indoor localization, wearable devices, and unmanned systems. In conclusion, this review highlights the transformative potential of AI-driven optimization in MEMS navigation sensors while acknowledging persistent challenges in computational complexity, data availability, and real-time processing. It advocates for future research focusing on innovative AI methodologies, integration with emerging technologies, adherence to human-centric design principles, and the establishment of rigorous evaluation standards. Such research promises to unlock the full potential of AI-driven optimization, ushering in advanced and user-centric navigation systems, and ultimately improving user experience across diverse areas.