Pedometer Using Acceleration Sensor with Kalman Filter Implementation

In my research, I focused on designing and developing a Kalman filter-based pedometer that utilizes acceleration sensors to detect human motion. The primary objective was to create a reliable step-counting system capable of accurately calculating step counts and distances with enhanced precision and reliability. The implementation involves processing raw accelerometer data through a Kalman filter algorithm to reduce noise and improve motion detection accuracy. I conducted extensive testing to validate the pedometer's performance, using two different sensor placement locations: waist and arm positions. Through analysis of the test data, I concluded that this pedometer provides accurate measurements across different body positions while maintaining high precision and reliability. This is particularly valuable for individuals requiring accurate step and distance calculations, such as athletes, fitness enthusiasts, and people monitoring their health conditions. The system's core functionality includes real-time data acquisition, signal filtering using Kalman gain optimization, peak detection algorithms for step identification, and distance computation based on stride estimation. Overall, my research delivers a robust pedometer solution that offers accurate measurements across various wearing positions, demonstrating superior precision and reliability that makes it highly useful for numerous applications.