Title | ||
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Probabilistic segmentation of time-series audio signals using Support Vector Machines. |
Abstract | ||
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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 |
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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 Kalantarian | 1 | 78 | 12.54 |
Bobak Mortazavi | 2 | 45 | 8.38 |
Mohammad Pourhomayoun | 3 | 87 | 15.23 |
Nabil Alshurafa | 4 | 134 | 19.65 |
Majid Sarrafzadeh | 5 | 3103 | 317.63 |