Signal Processing Techniques for Step Counting, Activity Classification, and Distance Measurement Using a Single IMU
Publicerad
Typ
Examensarbete på kandidatnivå
Bachelor Thesis
Bachelor Thesis
Program
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Inertial Measurement Unit (IMU), signal processing, gait analysis, distance estimation, activity classification, step counting, Zero-Velocity Update (ZUPT)