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- W2386728016 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%.1" @default.
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- W2386728016 date "2016-10-01" @default.
- W2386728016 modified "2023-10-16" @default.
- W2386728016 title "Probabilistic segmentation of time-series audio signals using Support Vector Machines" @default.
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- W2386728016 doi "https://doi.org/10.1016/j.micpro.2016.04.011" @default.
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