Probabilistic segmentation of time-series audio signals using Support Vector Machines

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