Abstract
Dialogue control for health-oriented smartwatch apps is a multi-dimensional task. In our application scenario, the intended purpose of the smartwatch app is the prevention and detection of health hazards jeopardizing the smartwatch wearer (e.g. exsiccosis because of insufficient drinking); the designated target group of the app are elderly people. The dimension of a potential simultaneity of health hazards and ethical considerations how to position the wearer always in control of the app have been presented before. In this paper we focus on the third dimension of the mandatory acceptance conditions of the app. The intended assistance functionality of the app can be only realized, if the interventions of the app occur only in daily life situations, when the wearer will accept such interventions. We present a machine learning approach, by which the app will learn from the wearer over time, when such interventions are appropriate and accepted - and when the app will be expected to remain silent. Of course, this decision has to take into account also the urgency of the intervention with respect to the severity of the threating health hazard.
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Notes
- 1.
These gestures include: drinking, eating, hand washing, run_away, sleeping/snoozing, steering (a vehicle/bicycle), teeth brushing, tumbling, and, of course, the »unclassified« gesture [2].
- 2.
It should be noted that the critical dialogue section concept proposed in [4] is asymmetric in its nature: by definition, a critical dialogue section will be always executed completely by the smartwatch app, as soon as it has started. But, the smartwatch wearer, user, is free to interrupt the execution of the section by application of the “shut up” gesture or command at any time.
- 3.
Currently described via an extended notion of UML finite state machines.
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Lutze, R., Waldhör, K. (2020). Improving Dialogue Design and Control for Smartwatches by Reinforcement Learning Based Behavioral Acceptance Patterns. In: Kurosu, M. (eds) Human-Computer Interaction. Human Values and Quality of Life. HCII 2020. Lecture Notes in Computer Science(), vol 12183. Springer, Cham. https://doi.org/10.1007/978-3-030-49065-2_6
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