Sleep Activity Recognition using Binary Motion Sensors - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Sleep Activity Recognition using Binary Motion Sensors


Early detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect poten- tial sleep disturbances of the monitored senior res- idents. We use an unsupervised inference method based on actigraphy data generated by ambient mo- tion sensors scattered around the senior’s apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates.
Fichier principal
Vignette du fichier
sleep_wake_inference.pdf (613.26 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01943463 , version 1 (03-12-2018)


  • HAL Id : hal-01943463 , version 1


Yassine El Khadiri, Gabriel Corona, Cédric Rose, François Charpillet. Sleep Activity Recognition using Binary Motion Sensors. ICTAI 2018 - 30th International Conference on Tools with Artificial Intelligence, IEEE, Nov 2018, Volos, Greece. ⟨hal-01943463⟩
82 View
477 Download


Gmail Facebook X LinkedIn More