Title
Probabilistic segmentation of time-series audio signals using Support Vector Machines.
Abstract
To allow health tracking, patient monitoring, and provide timely user interventions, sensor signals from body sensor networks need to be processed in real-time. Time subdivisions of the sensor signals are extracted and fed into a supervised learning algorithm, such as Support Vector Machines (SVM), to learn a model capable of distinguishing different class labels. However, selecting a short-duration window from the continuous data stream is a significant challenge, and the window may not be properly centered around the activity of interest. In this work, we address the issue of window selection from a continuous data stream, using an optimized SVM-based probability model. To evaluate the effectiveness of our approach, we apply our algorithm to audio signals acquired from a wearable nutrition-monitoring necklace. Our optimized algorithm is capable of correctly classifying 86.1% of instances, compared to a baseline of 73% which segments the time-series data with fixed-size non-overlapping windows, and an exhaustive-search approach with an accuracy of 92.6%.11This work is funded by the National Science Foundation AIR Option 1: Award Number 1312310.
Year
DOI
Venue
2016
10.1016/j.micpro.2016.04.011
Microprocessors and Microsystems - Embedded Hardware Design
Keywords
Field
DocType
Segmentation,Time-series,Support vector machines,Wearable devices
Audio signal,Data mining,Segmentation,Remote patient monitoring,Computer science,Data stream,Wearable computer,Support vector machine,Wearable technology,Wireless sensor network
Journal
Volume
Issue
ISSN
46
PA
0141-9331
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Haik Kalantarian17812.54
Bobak Mortazavi2458.38
Mohammad Pourhomayoun38715.23
Nabil Alshurafa413419.65
Majid Sarrafzadeh53103317.63