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
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.
Also in collaboration with: Henry
Medeiros (Marquette University)
Funded by "Liguria 4P Health - Predictive, Personalized, Preventive,
Participatory Healthcare" (POR-FESR Liguria 2014-2021). In collaboration with MaLGa-MLDS
Reference paper: Data-driven Continuous
Assessment of Frailty in Older People, Frontiers in Digit. Humanit., 17 April 2018
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 indepencence. Emotion
analysis: emotion recognition, assessment of valence-arousal vs cognitive models approaches.
Giuliano Grossi (UNIMI),
Claudio de'Sperati (San Raffaele),
Andrea Gaggioli (Uni
Funded by CARIPLO "Stairway to elders: bridging space,
time and emotions in their social environment for wellbeing"
References: G Grossi, R Lanzarotti, P Napoletano, N Noceti, F Odone “Positive technology for
elderly well-being: a review” Pattern Recognition Letters 2019