Various recent funded projects fuelled multi-disciplinary applied research in this direction.
We assess physical well-being by analysing patients over medium to long time periods, evaluating in particular their motility and the quality of Activity of Daily Living (how much they move, how active they are). To achieve this goal we employ RGB and RGB-D sensors and address the following main tasks: joint detection and tracking of people, apparent velocity estimation, pose transitions estimation (sit to stand), simple action recognition (sitting, standing, walking, bending, lying,…); human-object interaction and action recognition for ADL. For a more comprehensive analysis, the observations acquired with environmental visual sensors may be coupled with measures collected with wearable sensors. This allows us to build richer models able to capture interconnections between heterogeneous information, enabling the design of personalized healthcare.
The research is carried out in collaboration with Ospedale Galliera (Genova, It) within the MoDiPro facility (Modello di Dimissione Protetta, Protected Discharge Facility), a sensorized apartment within the hospital, an ideal test bed for research in Ambient Assisted Living.
Henry Medeiros - Marquette University
ReferencesData-driven Continuous Assessment of Frailty in Older People, Frontiers in Digit. Humanit., 17 April 2018
"Liguria 4P Health Predictive, Personalized, Preventive, Participatory Healthcare" (POR-FESR Liguria 2014-2021). In collaboration with MaLGa-MLDS
Social interaction assessment and emotional well-being
Emotional well-being is related to the sense of fulfilment; it includes satisfaction, optimism, having a purpose in life as well as being able to make the most of your abilities to cope with the normal challenges of life. An increasing body of research suggests that initiatives promoting physical wellbeing disregarding mental and social wellbeing may lead to failure. In this general framework we address the following main topics: human-human interaction for social signals assessment and evaluation of independence. Emotion analysis: emotion recognition, assessment of valence-arousal vs cognitive models approaches.
Raffaella Lanzarotti - UniMi
Giuliano Grossi - UniMi
Claudio De’ Sperati - San Raffaele
Andrea Gaggioli - Uni Cattolica
ReferencesG Grossi, R Lanzarotti, P Napoletano, N Noceti, F Odone “Positive technology for elderly well-being: a review” Pattern Recognition Letters 2019
CARIPLO “Stairway to elders: bridging space, time and emotions in their social environment for wellbeing”