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High Five: Improving Gesture Recognition by Embracing Uncertainty by Tootaghaj, Diman Zad; Sampson, Adrian; Mytkowicz, Todd; McKinley, Kathryn S is a scholarly article available to read on EtoBox.

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Sensors on mobile devices---accelerometers, gyroscopes, pressure meters, and GPS---invite new applications in gesture recognition, gaming, and fitness tracking. However, programming them remains challenging because human gestures captured by sensors are noisy. This paper illustrates that noisy gestures degrade training and classification accuracy for gesture recognition in state-of-the-art deterministic Hidden Markov Models (HMM). We introduce a new statistical quantization approach that mitigates these problems by (1) during training, producing gesture-specific codebooks, HMMs, and error models for gesture sequences; and (2) during classification, exploiting the error model to explore multiple feasible HMM state sequences. We implement classification in Uncertain<t>, a probabilistic programming system that encapsulates HMMs and error models and then automates sampling and inference in the runtime. Uncertain<T> developers directly express a choice of application-specific trade-off between recall and precision at gesture recognition time, rather than at training time. We demonstrate benefits in configurability, precision, recall, and recognition on two data sets with 25 gestures fro

Author

Tootaghaj, Diman Zad; Sampson, Adrian; Mytkowicz, Todd; McKinley, Kathryn S

Published

2017

Language

EN