Abstract
The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.
Highlights
In recent decades, demographic and societal changes are causing ever more people to live alone at an older age
Following the construction of the instances, we run all the instances through the predictive model, i.e., we provide them as input to the model, and collect predictions for the user feedback
This paper describes the processes for assessment and integration of user preferences in a coaching system
Summary
Demographic and societal changes are causing ever more people to live alone at an older age. The SAAM coaching system, which was developed in scope of the collaborative research project of the same name (Supporting Active Aging through Multimodal coaching, https://saam2020.eu/, accessed on 14 January 2021) is one such solution It features an array of sensor-based monitoring technologies, a situation awareness reasoning engine and automatically triggered coaching actions, as is common to many other systems of this kind, the SAAM coaching system involves people from the user’s social circles in the coaching loop. Besides involving the user’s social circle in coaching, the system has a friendly user interface, attempts to unobtrusively (as possible) sense and actuate coaching, and, crucially for this paper, attempts to adapt some of the parameters of the coaching process according to the user’s preferences. There are optional or even arbitrarily chosen parameter values related to rendering, i.e., the method of delivery and presentation of the coaching actions, that can be adjusted to suit the users and their preferences
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
