Abstract
The issue of time-series segmentation refers to the division of continuous sensor data into discrete windows, each of which are processed individually and assigned a class label. Unlike offline data processing scenarios, segmentation for low-power wearable devices must balance the juxtaposed aims of accuracy and computational efficiency. In this paper, we propose a novel scheme for segmentation of sparse sensor data using an adaptive window size approach. Our results are benchmarked on an audio-based nutrition monitoring dataset, and show a reduction in processing overhead of 68% compared to the baseline with fixed window sizes.
Sign-in/Register to access full text options 
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: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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.
