A. S. Ahouandjinou, C. Motamed, and E. C. Ezin, A temporal belief-based hidden Markov model for human action recognition in medical videos, Pattern Recognition and Image Analysis, 2015.

R. Alur and T. Henzinger, Reactive modules. Formal Methods in System Design, 1999.

S. D. Atkinson and V. L. Narasimhan, Design of an introductory medical gaming environment for diagnosis and management of parkinson's disease, Trendz in Information Sciences Computing(TISC), 2010.

F. Buttussi, T. Pellis, A. Cabas-vidani, D. Pausler, E. Carchietti et al., Evaluation of a 3D serious game for advanced life support retraining, Int. Journal of Medical Informatics, 2013.

F. F. Chamasemani and L. S. Affendey, Systematic review and classification on video surveillance systems, Int. Journal of Information Technology and Computer Science(IJITCS), 2013.

L. Chittaro and R. Sioni, Turning the classic snake mobile game into a location-based exergame that encourages walking, Persuasive Technology. Design for Health and Safety, 2012.

E. M. Clarke, O. Grumberg, and D. A. Peled, , 1999.

X. Du, M. El-khamy, J. Lee, and L. Davis, Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection, 2017 IEEE Winter Conf. on Applications of Computer Vision (WACV, 2017.

T. M. Fleming, L. Bavin, K. Stasiak, E. Hermansson-webb, S. N. Merry et al., Serious games and gamification for mental health: Current status and promising directions, Frontiers in Psychiatry, 2017.

H. Hansson and B. Jonsson, A logic for reasoning about time and reliability. Formal aspects of computing, 1994.

M. Hassan, A performance model of pedestrian dead reckoning with activity-based location updates, 18th IEEE Int. Conf. on Networks (ICON), p.2012, 2012.

A. Jalal, S. Kamal, and D. Kim, A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems. Int, Journal of Interactive Multimedia & Artificial Intelligence, 2017.

E. Kim, S. Helal, and D. Cook, Human activity recognition and pattern discovery, IEEE pervasive computing, 2009.

M. Kwiatkowska, G. Norman, and D. Parker, PRISM 4.0: Verification of probabilistic real-time systems, Proc. 23rd Int. Conf. on Computer Aided Verification (CAV'11, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00648035

M. Kwiatkowska, G. Norman, and D. Parker, Stochastic model checking, Int. School on Formal Methods for the Design of Computer, 2007.

T. Magherini, A. Fantechi, C. D. Nugent, and E. Vicario, Using temporal logic and model checking in automated recognition of human activities for ambient-assisted living, IEEE Transactions on Human-Machine Systems, 2013.

T. Magherini, G. Parente, C. D. Nugent, M. P. Donnelly, E. Vicario et al., Temporal logic bounded model-checking for recognition of activities of daily living, Proc. of the 10th IEEE Int. Conf. on Information Technology and Applications in Biomedicine, 2010.

M. Nyolt, K. Yordanova, and T. Kirste, Checking models for activity recognition, 2015.

C. Piciarelli, S. Canazza, C. Micheloni, and G. L. Foresti, A network of audio and video sensors for monitoring large environments, Handbook on Soft Computing for Video Surveillance, 2012.

D. Sadigh, K. Driggs-campbell, A. Puggelli, W. Li, V. Shia et al., Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior, Formal Verification and Modeling in Human-Machine Systems, AAAI Spring Symposium (FVHMS), 2014.

M. K. Tran, F. Brémond, and P. Robert, Assistance for older adults in serious game using an interactive system, 4th Int. Conf. on Games and Learning Alliance, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01275258

U. Ujjwal, A. Dziri, B. Leroy, and F. Bremond, Late Fusion of Multiple Convolutional Layers for Pedestrian Detection, 15th IEEE Int. Conf. on Advanced Video and Signal-based Surveillance, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01926073

M. Vrigkas, C. Nikou, and I. A. Kakadiaris, A review of human activity recognition methods, Frontiers in Robotics and AI, 2015.

S. Weerachai and M. Mizukawa, Human behavior recognition via top-view vision for intelligent space, Int. Conf. on Control, Automation and Systems (ICCAS), 2010.

H. B. Zhang, Y. X. Zhang, B. Zhong, Q. Lei, L. Yang et al., A comprehensive survey of vision-based human action recognition methods, Sensors, issue.5, 2019.