Research Article
Ontological Modeling of Motivational Messages for Physical Activity Coaching
@INPROCEEDINGS{10.1145/3154862.3154926, author={Claudia Villalonga and Harm op den Akker and Hermie Hermens and Luis Javier Herrera and Hector Pomares and Ignacio Rojas and Olga Valenzuela and Oresti Banos}, title={Ontological Modeling of Motivational Messages for Physical Activity Coaching}, proceedings={11th EAI International Conference on Pervasive Computing Technologies for Healthcare}, publisher={ACM}, proceedings_a={PERVASIVEHEALTH}, year={2018}, month={1}, keywords={ontology motivational messages smart coaching}, doi={10.1145/3154862.3154926} }
- Claudia Villalonga
Harm op den Akker
Hermie Hermens
Luis Javier Herrera
Hector Pomares
Ignacio Rojas
Olga Valenzuela
Oresti Banos
Year: 2018
Ontological Modeling of Motivational Messages for Physical Activity Coaching
PERVASIVEHEALTH
ACM
DOI: 10.1145/3154862.3154926
Abstract
Smart coaching systems are named to play a central role in both prevention and intervention strategies for behavioral change. While relevant progresses have been made in terms of automatic and continuous monitoring of behavioral aspects, e.g. amount and variety of physical activity, coaching and feedback techniques are still in an infancy stage. Current smart coaching strategies are mostly based on handcrafted messages which hardly personalize to the needs, context and preferences of each user. In order to make these recommendations more realistic, engaging and e ective more exible and sophisticated strategies are needed. This paper presents an ontology-based approach to model personalizable motivational messages for promoting healthy physical activity. The proposed ontology not only models the message intention and its components, e.g. argument, feedback or followup, but also its content, i.e. action, place, time or object required to perform the recommended activity. Through this ontology the messages can also be categorized into multiple classes, e.g. sedentary, mild or vigorous activities, and retrieved based on the preferences, needs and context of the user. Additional information not explicitly present on the messages can be inferred from the ontology by applying reasoning techniques and used to enhance the message retrieval process.