Signal Processing Techniques for Step Counting, Activity Classification, and Distance Measurement Using a Single IMU

Abstract

The aim of this thesis was to develop and validate an offline method for monitoring human walking and running using a single, low-cost Inertial Measurement Unit (IMU). We designed signal-processing algorithms to count steps, estimate distance and speed, and classify activity level. All from the 3-axis accelerometer and gyroscope data. Raw signals were filtered with low-pass Finite Impulse Response (FIR) filter, zero-velocity updates were applied at each detected gait event and dominant stride frequencies were extracted via Fast Fourier Transform (FFT) over sliding windows. In test with five subject on a 75 m straight path, step-count accuracy averaged 98% for walking and 94% for running. The distance estimates reached 96% accuracy in walking and 91% in running. Activity classification achieved 100% accuracy in controlled trials and 89% in mixed scenarios.