Skip to Main content Skip to Navigation
Conference papers

Probabilistic Activity Recognition For Serious Games With Applications In Medicine

Abstract : Human activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. Starting from an activity description enriched with event occurrence probabilities, we translate it into a corresponding formal model based on discrete-time Markov chains (DTMCs). We use the PRISM framework and its model checking facilities to express and check interesting temporal logic properties (PCTL) concerning the dynamic evolution of activities. We illustrate our approach on the model of a serious game used by clinicians to monitor Alzheimer patients. We expect that such a modeling approach could provide new indications for interpreting patient performances. This paper addresses only the model definition and its suitability to check behavioral properties of interest. Indeed, this is mandatory before envisioning any clinical study.
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : Thibaud l'Yvonnet Connect in order to contact the contributor
Submitted on : Thursday, October 31, 2019 - 2:38:45 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:52 PM
Long-term archiving on: : Saturday, February 1, 2020 - 7:29:15 PM


Files produced by the author(s)


  • HAL Id : hal-02341600, version 1



Elisabetta de Maria, Thibaud l'Yvonnet, Sabine Moisan, Jean-Paul Rigault. Probabilistic Activity Recognition For Serious Games With Applications In Medicine. ICFEM 2019 - FTSCS workshop, Nov 2019, Shenzhen, China. ⟨hal-02341600⟩



Record views


Files downloads