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Simon Rogers


Publications by Simon Rogers (bibliography)

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Weir, Daryl, Rogers, Simon, Murray-Smith, Roderick and Lochtefeld, Markus (2012): A user-specific machine learning approach for improving touch accuracy on mobile devices. In: Proceedings of the 2012 ACM Symposium on User Interface Software and Technology 2012. pp. 465-476. http://dx.doi.org/10.1145/2380116.2380175

We present a flexible Machine Learning approach for learning user-specific touch input models to increase touch accuracy on mobile devices. The model is based on flexible, non-parametric Gaussian Process regression and is learned using recorded touch inputs. We demonstrate that significant touch accuracy improvements can be obtained when either raw sensor data is used as an input or when the device's reported touch location is used as an input, with the latter marginally outperforming the former. We show that learned offset functions are highly nonlinear and user-specific and that user-specific models outperform models trained on data pooled from several users. Crucially, significant performance improvements can be obtained with a small (≈200) number of training examples, easily obtained for a particular user through a calibration game or from keyboard entry data.

© All rights reserved Weir et al. and/or ACM Press

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Rogers, Simon, Williamson, John, Stewart, Craig and Murray-Smith, Roderick (2011): AnglePose: robust, precise capacitive touch tracking via 3d orientation estimation. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 2575-2584. http://dx.doi.org/10.1145/1978942.1979318

We present a finger-tracking system for touch-based interaction which can track 3D finger angle in addition to position, using low-resolution conventional capacitive sensors, therefore compensating for the inaccuracy due to pose variation in conventional touch systems. Probabilistic inference about the pose of the finger is carried out in real-time using a particle filter; this results in an efficient and robust pose estimator which also gives appropriate uncertainty estimates. We show empirically that tracking the full pose of the finger results in greater accuracy in pointing tasks with small targets than competitive techniques. Our model can detect and cope with different finger sizes and the use of either fingers or thumbs, bringing a significant potential for improvement in one-handed interaction with touch devices. In addition to the gain in accuracy we also give examples of how this technique could open up the space of novel interactions.

© All rights reserved Rogers et al. and/or their publisher

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Rogers, Simon, Williamson, John, Stewart, Craig and Murray-Smith, Roderick (2010): FingerCloud: uncertainty and autonomy handover incapacitive sensing. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 577-580. http://doi.acm.org/10.1145/1753326.1753412

We describe a particle filtering approach to inferring finger movements on capacitive sensing arrays. This technique allows the efficient combination of human movement models with accurate sensing models, and gives high-fidelity results with low-resolution sensor grids and tracks finger height. Our model provides uncertainty estimates, which can be linked to the interaction to provide appropriately smoothed responses as sensing performance degrades; system autonomy is increased as estimates of user behaviour become less certain. We demonstrate the particle filter approach with a map browser running with a very small sensor board, where finger position uncertainty is linked to autonomy handover.

© All rights reserved Rogers et al. and/or their publisher

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