title: A Hierarchical Classification and Segmentation Scheme for Processing Sensor Data.
abstract: Detecting short-duration events from continuous sensor signals is a significant challenge in the domain of wearable devices and health monitoring systems. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which can be individually processed using statistical classifiers or other algorithms. In this paper, we propose an algorithm for segmenting time-series signals and detecting short-duration data in the domain of lightweight embedded systems with real-time constraints. First, we demonstrate an approach for signal segmentation using a simple binary classifier. Next, we show how a novel two-stage classification algorithm can reduce computational overhead compared to a single-stage approach. Our proposed scheme is benchmarked using an audio-based nutrition-monitoring case study.
authors.forename: Haik
authors.initials: H
authors.lastname: Kalantarian
authors.forename: Costas
authors.initials: C
authors.lastname: Sideris
authors.forename: Majid
authors.initials: M
authors.lastname: Sarrafzadeh
mesh_terms: D000328:Adult; D000465:Algorithms; D003020:Clothing; D004435:Eating / Q000502:physiology; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008962:Models, Theoretical; D018670:Monitoring, Ambulatory / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult