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

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Examensarbete på kandidatnivå
Bachelor Thesis

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Modellbyggare

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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.

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Inertial Measurement Unit (IMU), signal processing, gait analysis, distance estimation, activity classification, step counting, Zero-Velocity Update (ZUPT)

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