Article
Understanding Social Behaviour in a Health-Care Facility from
Localization Data: A Case Study
Gloria Bellini
1
, Marco Cipriano
1
, Sara Comai
2,
* , Nicola De Angeli
1
, Jacopo Pio Gargano
1
,
Matteo Gianella
1
, Gianluca Goi
1
, Giovanni Ingrao
3
, Andrea Masciadri
2
, Gabriele Rossi
1
and Fabio Salice
2
Citation: Bellini, G.; Cipriano, M.;
Comai, S.; De Angeli, N.; Gargano,
J.P.; Gianella, M.; Goi, G.; Ingrao, G.;
Masciadri, A.; Rossi, G.; et al.
Understanding Social Behaviour in a
Health-Care Facility from
Localization Data: A Case Study.
Sensors 2021, 21, 2147.
https://doi.org/10.3390/s21062147
Academic Editor: Susanna Spinsante
Received: 2 February 2021
Accepted: 7 March 2021
Published: 18 March 2021
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4.0/).
1
Alta Scuola Politecnica (Politecnico di Milano and Politecnico di Torino), 20133 Milano, Italy;
gloria.bellini@asp-poli.it (G.B.); marco.cipriano@asp-poli.it (M.C.); nicola.deangeli@asp-poli.it (N.D.A.);
jacopopio.gargano@asp-poli.it (J.P.G.); matteo.gianella@asp-poli.it (M.G.); gianluca.goi@asp-poli.it (G.G.);
gabriele.rossi@asp-poli.it (G.R.)
2
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy;
andrea.masciadri@polimi.it (A.M.); fabio.salice@polimi.it (F.S.)
3
Cooperativa La Meridiana, 20900 Monza, Italy
* Correspondence: sara.comai@polimi.it
Abstract:
The most frequent form of dementia is Alzheimer’s Disease (AD), a severe progressive
neurological pathology in which the main cognitive functions of an individual are compromised.
Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in
the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients
is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and
providing a good quality of life. In this work, we analyze the localization data of AD patients living
in assisted care homes to gather insights about the social dynamics among them. We use localization
data collected by a system based on iBeacon technology comprising two components: a network
of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We
redefine the Relational Index to capture wandering and casual encounters, these being common
phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model,
visualize and understand the social behaviour of AD patients. We leverage the data analyses to build
predictive tools and applications to enhance social activities scheduling and sociability monitoring
and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions
and visualizations act as a support for caregivers in activity planning to maximize treatment effects
and, hence, slow down the progression of Alzheimer’s disease. We present the Community Behaviour
Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and
popularity of places within a facility. Finally, we show the potential of the system by analyzing
the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020
in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on
the behaviour of the residents, observing no particular impact on sociability even though social
distancing was put in place.
Keywords:
ambient assisted living; data-driven design; social behaviour prediction; social wellness
assessment
1. Introduction
In the last decades, the rapid advancements and new breakthroughs in the medical
field combined with the rising awareness towards healthy life practices have allowed
people in developed and developing countries to live longer than ever before. On the other
hand, declining fertility rates have been observed in many areas of the world, with highly
developed countries reporting the lowest numbers. As a consequence, many societies are
currently experiencing a steady increase in the age of their population. In 1990, only 6% of
the world population was aged 65 years or over, 10% in 2019 and by 2050 the number is
Sensors 2021, 21, 2147. https://doi.org/10.3390/s21062147 https://www.mdpi.com/journal/sensors
