Music Assisted Running Trainer Based on Runner’s Step Frequencies
Date Issued
2015
Date
2015
Author(s)
Tung, Yen-Ju
Abstract
This thesis describes a smartphone app we developed called MART (Music Assisted Run Trainer). The goal is to assist users to jog easily and pleasantly through controlling the tempo of the music. MART estimates the runner’s SPM (steps-per-minute) in real-time via the tri-accelerometer and gyroscope in the smartphone. The music whose tempo is the same as the runner’s SPM is then synthesized via Waveform Similarity Based Overlap-Add (WSOLA). Most of the existing research in step detection uses threshold-based methods, while MART adopts a method which is based on a beat tracking algorithm. We propose four methods for finding the step period. Three of them are based on autocorrelation function in the time domain, while the other one is based on cepstrum in the frequency domain. We design two experiments to evaluate the performance of MART in detecting steps and estimating step frequency. Experimental result shows that the accuracy of the time-domain methods is better than the frequency-domain method.
Subjects
step-per-minute analysis
step detection
tri-accelerometer
gyroscope
Type
thesis
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