Abstract
Sensor data annotation involves automated marking of a time series of readings taken from the sensor, which highlights various activities specified by the specified series. Activity marking has a wide range of practical applications: predictive maintenance, intelligent management of life support systems, climate modeling, etc. Previously, we developed a parallel PSF algorithm for annotating sensor data using a GPU based on the concept of snippets. Snippet is a subsequence that many other subsequences of a given series resemble in the sense of a specialized similarity measure based on Euclidean distance. This article describes two case studies performed using the PSF algorithm: annotation of the readings of a wearable vibration accelerometer mounted on a person and a stationary vibration accelerometer mounted on a small crusher. As part of the research, computational experiments were conducted to evaluate the speed and accuracy of the developed algorithm. Also there was the research on the dependence of the efficiency of the algorithm on the values of the input parameters: the number of the desired snippets and the length of the subsequence.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Bulletin of the South Ural State University. Series "Computational Mathematics and Software Engineering"
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
