  The lack of large-scale, labeled data sets impedes progress in developing
robust and generalized predictive models for on-body sensor-based human
activity recognition (HAR). Labeled data in human activity recognition is
scarce and hard to come by, as sensor data collection is expensive, and the
annotation is time-consuming and error-prone. To address this problem, we
introduce IMUTube, an automated processing pipeline that integrates existing
computer vision and signal processing techniques to convert videos of human
activity into virtual streams of IMU data. These virtual IMU streams represent
accelerometry at a wide variety of locations on the human body. We show how the
virtually-generated IMU data improves the performance of a variety of models on
known HAR datasets. Our initial results are very promising, but the greater
promise of this work lies in a collective approach by the computer vision,
signal processing, and activity recognition communities to extend this work in
ways that we outline. This should lead to on-body, sensor-based HAR becoming
yet another success story in large-dataset breakthroughs in recognition.
