Skip to Main content Skip to Navigation

Modelling and Recognition of Human Activities of Daily Living in a Smart Home

Abstract : Most of the work done in the field of ambient assisted living (AAL) is based on the use of visual and audio sensors such as cameras. However, these sensors are often rejected by the patient because of their invasiveness. Alternative approaches require the use of sensors embedded on the person (GPS, electronic wristbands or RFID chips in clothing ...), and their relevance is therefore reduced to the assumption that people actually wear them, without rejecting nor forgetting them. For these reasons, in this thesis, we find more relevant the approaches based on the use of binary sensors integrated into the habitat only, such as motion detectors, sensory mats or optical barriers. In such a technological context, it becomes interesting to use paradigms, models and tools of Discrete Event Systems (DES), initially developed for modeling, analysis and control of complex industrial systems. In this thesis work, the goal is to build an activity of daily living modeling and monitoring approach, based on the models and the paradigm of the DES and answering a problem that is expressed as follows:The objective is to develop a global framework to discover and recognise activities of daily living of an inhabitant living alone in a smart home. This smart home have to be equipped with binary sensors only, expert labeling of activities should not be needed and activities can be represented by probabilistic models. The first method presented in this thesis allows to build a probabilistic finite-state automata (PFA) from a learning database and an expert description of the activities to be modeled given by the medical staff. The second method developed during this thesis estimates, according to the observations, the activity performed by the monitored inhabitant. The methods described in this thesis are applied on data generated using an apartment lent by ENS Paris-Saclay and equipped according the experimental needs of this thesis.
Document type :
Complete list of metadata

Cited literature [103 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Tuesday, September 4, 2018 - 2:40:07 PM
Last modification on : Monday, February 15, 2021 - 10:40:21 AM
Long-term archiving on: : Wednesday, December 5, 2018 - 4:28:53 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01867623, version 1



Kévin Viard. Modelling and Recognition of Human Activities of Daily Living in a Smart Home. Automatic Control Engineering. Université Paris-Saclay; Politecnico di Bari. Dipartimento di Ingegneria Elettrica e dell'Informazione (Italia), 2018. English. ⟨NNT : 2018SACLN022⟩. ⟨tel-01867623⟩



Record views


Files downloads