Gesture spotting using wrist worn microphone and 3-axis accelerometer

We perform continuous activity recognition using only two wrist-worn sensors - a 3-axis accelerometer and a microphone. We build on the intuitive notion that two very different sensors are unlikely to agree in classification of a false activity. By comparing imperfect, jumping window classifications from each of these sensors, we are able discern activities of interest from null or uninteresting activities. Where one sensor alone is unable to perform such partitioning, using comparison we are able to report good overall system performance of up to 70% accuracy. In presenting these results, we attempt to give a more-in depth visualization of the errors than can be gathered from confusion matrices alone.