Physical activity (PA) is considered one of the most important factors for the prevention and management of non-communicable diseases (NCDs). Mobile technologies offer several opportunities for supporting PA, especially if combined with psychological aspects, model-based reasoning systems and personalized human computer interaction. This still on-going research aims at developing a scalable framework that targets PA promotion among both clinical and non-clinical population, exploiting Bayesian Networks and Expert Systems to characterize and predict qualitative variables like self-efficacy. The expected outcomes are the collection and management of real-time behavioral and psychological data to define a personalized strategy for increasing PA.

Baretta, D., Sartori, F., Greco, A., Melen, R., Stella, F., Bollini, L., et al. (2016). Wearable devices and AI techniques integration to promote physical activity. In MobileHCI '16 Adjunct - Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. September 6-9 2016 - Congress Palace, Florence (Tuscany), Italy (pp. 1105-1108). New York, NY : Association for Computing Machinery, Inc [10.1145/2957265.2965011].

Wearable devices and AI techniques integration to promote physical activity

BARETTA, DARIO
Primo
;
SARTORI, FABIO
Secondo
;
GRECO, ANDREA;MELEN, RICCARDO;STELLA, FABIO ANTONIO;BOLLINI, LETIZIA;D'ADDARIO, MARCO
Penultimo
;
STECA, PATRIZIA
Ultimo
2016

Abstract

Physical activity (PA) is considered one of the most important factors for the prevention and management of non-communicable diseases (NCDs). Mobile technologies offer several opportunities for supporting PA, especially if combined with psychological aspects, model-based reasoning systems and personalized human computer interaction. This still on-going research aims at developing a scalable framework that targets PA promotion among both clinical and non-clinical population, exploiting Bayesian Networks and Expert Systems to characterize and predict qualitative variables like self-efficacy. The expected outcomes are the collection and management of real-time behavioral and psychological data to define a personalized strategy for increasing PA.
Capitolo o saggio
Bayesian Networks; Expert Systems; Wearable Devices; Self-Efficacy; Behavior Change Techniques; Physical Activity
English
MobileHCI '16 Adjunct - Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. September 6-9 2016 - Congress Palace, Florence (Tuscany), Italy
2016
9781450344135
Association for Computing Machinery, Inc
1105
1108
Baretta, D., Sartori, F., Greco, A., Melen, R., Stella, F., Bollini, L., et al. (2016). Wearable devices and AI techniques integration to promote physical activity. In MobileHCI '16 Adjunct - Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. September 6-9 2016 - Congress Palace, Florence (Tuscany), Italy (pp. 1105-1108). New York, NY : Association for Computing Machinery, Inc [10.1145/2957265.2965011].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/130880
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