Post-Processing Method for Determining Peaks in Noisy Strain Gauge Data with a Low Sampling Frequency
Hill, Peter Lee
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The Virginia Tech Transportation Institute is recognized for being a pioneer in naturalistic driving studies. These studies determine driving behavior, and its correlation to safety critical events, by equipping participant�[BULLET]s vehicles with data acquisition systems and recording them for a period of time. The driver�[BULLET]s habits and responses to certain scenarios and events are analyzed to determine trends and opportunities to improve overall driver safety. One of these studies installed strain gauges on the front and rear brake levers of motorcycles to record the frequency and magnitude of brake presses. The recorded data was sampled at 10 hertz and had a significant amount of noise introduced from temperature and electromagnetic interference. This thesis proposes a peak detection algorithm, written in MATLAB, that can parallel process the 40,000 trips recorded in this naturalistic driving study. This algorithm uses an iterative LOWESS regression to eliminate the offset from zero when the strain gauge is not stressed, as well as a cumulative sum and statistical concepts to separate brake activations from the rest of the noisy signal. This algorithm was verified by comparing its brake activation detection to brake activations that were manually identified through video reduction. The algorithm had difficulty in accurately identifying activations in files where the amplitude of the noise was close to the amplitude of the brake activations, but this only described 2% of the sampled data. For the rest of the files, the peak detection algorithm had an accuracy of over 90%.
- Masters Theses