Intent Inference for Hand Pointing Gesture Based Interactions in Vehicles
IEEE Transactions on Cybernetics
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Ahmad, B., Murphy, J. K., Langdon, P., Godsill, S., Hardy, R., & Skrypchuk, L. (2015). Intent Inference for Hand Pointing Gesture Based Interactions in Vehicles. IEEE Transactions on Cybernetics, 46 878-889. https://doi.org/10.1109/TCYB.2015.2417053
Using interactive displays, such as a touchscreen, in vehicles typically requires dedicating a considerable amount of visual as well as cognitive capacity and undertaking a hand pointing gesture to select the intended item on the interface. This can act as a distractor from the primary task of driving and consequently can have serious safety implications. Due to road and driving conditions, the user input can also be highly perturbed resulting in erroneous selections compromising the system usability. In this paper, we propose intent-aware displays that utilise a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can be achieved with high confidence remarkably early in the pointing gesture. This can drastically reduce the time and effort required to successfully complete an in-vehicle selection task. In the proposed probabilistic inference framework, the likelihood of all the nominal destinations are sequentially calculated by modelling the hand pointing gesture movements as a destination-reverting process. This leads to a Kalman filter-type implementation of the prediction routine that requires minimal parameter training and has low computational burden; it is also amenable to parallelisation. The substantial gains obtained using an intent-aware display are demonstrated using data collected in an instrumented vehicle driven under various road conditions.
interactive displays, finger tracking, human computer interactions, Bayesian inference, Kalman filtering
The authors would like to thank Jaguar Land Rover for funding this research under the CAPE agreement and facilitating the data collection.
External DOI: https://doi.org/10.1109/TCYB.2015.2417053
This record's URL: https://www.repository.cam.ac.uk/handle/1810/247765