=Paper=
{{Paper
|id=Vol-2559/paper5
|storemode=property
|title=Exploiting BLE beacons capabilities in the NESTORE monitoring system
|pdfUrl=https://ceur-ws.org/Vol-2559/paper5.pdf
|volume=Vol-2559
|authors=Filippo Palumbo,Paolo Baronti,Antonino Crivello,Francesco Furfari,Michele Girolami,Fabio Mavilia,Marta Civiello,Enrico Denna,Leonardo Angelini,Mira El Kamali,Omar Abou Khaled,Elena Mugellini,Giancarlo Pace
|dblpUrl=https://dblp.org/rec/conf/aiia/PalumboBCFGMCDA19
}}
==Exploiting BLE beacons capabilities in the NESTORE monitoring system==
https://ceur-ws.org/Vol-2559/paper5.pdf
Exploiting BLE beacons capabilities in the
NESTORE monitoring system
Filippo Palumbo?1[0000−0001−9778−7142] , Paolo Baronti1 , Antonino
Crivello1[0000−0001−7238−2181] , Francesco Furfari1 , Michele
Girolami1[0000−0002−3683−7158] , Fabio Mavilia1[0000−0002−6982−242X] , Marta
Civiello2 , Enrico Denna2 , Leonardo Angelini3[0000−0002−8802−5282] , Mira El
Kamali3 , Omar Abou Khaled3[0000−0002−0178−9037] , Elena
Mugellini3[0000−0002−0775−0862] , and Giancarlo Pace4
1
Institute of Information Science and Technologies “A. Faedo”
National Research Council, 56124 Pisa, Italy
{name.surname}@isti.cnr.it
2
FLEXTRONICS DESIGN SRL, Milan, Italy
3
HumanTech Institute
University of Applied Sciences Western Switzerland, 1700 Fribourg, Switzerland
{leonardo.angelini,mira.elkamali,omar.aboukhaled,elena.mugellini}@hes-so.ch
4
Neosperience S.p.a., 25125 Brescia, Italy
Abstract. Monitoring physiological and behavioural data related to the
five domains of well-being (i.e., physical, mental, cognitive, social, and
nutritional) is relevant for assessing the profile of people using assistive
technologies, in order to provide early detection and adaptive support
to his changing individual needs related to ageing. In this paper, we
present a system called NESTORE that aims at addressing such a chal-
lenge. In particular, we focus on the enabling technology that composes
the core set of devices of the so-called environmental monitoring system,
namely the NESTORE Bluetooth Low Energy beacons. The presented
system performs a range of services including data collection and analy-
sis of short- and long-term trends in social and behavioural parameters.
Furthermore, using the same set of devices the system provides insights
on the status of the user’s vital space in terms of thermal comfort. We
provide an overview of the NESTORE environmental monitoring sys-
tem and details and evaluation of the software modules built upon the
chosen technology: social interaction detection, indoor behavioural index
inference, and indoor thermal comfort detection.
Keywords: Bluetooth Low Energy · Social Interaction · Virtual Coach
· Active Ageing .
?
Corresponding author.
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 F. Palumbo et al.
1 Introduction
Extended life expectancy in developed countries is a sign of a global better
health, but it brings with it needs and challenges to be properly addressed.
To this end, Information and Communication Technologies (ICT) can provide
solutions for Active Ageing. However, the success of such solutions is strongly
affected by the perception of the users about their obtrusiveness and efficacy. In
this context, the NESTORE project aims at developing a suite of personalized
guidelines for supporting health and global wellness to be provided to the user by
means of multimodal tools: a virtual coach, a tangible interface, serious games,
and a set of personal and environmental sensors.
One of the main goal of NESTORE is to monitor physiological and be-
havioural data related to the five human domains of well-being (i.e., physical,
mental, cognitive, social, and nutritional), as identified by medical specialists [5].
In order to achieve this goal, we develop a multi-domain unobtrusive monitoring
system, also relevant for assessing the user profile [36] [28]. The NESTORE sys-
tem optimizes and integrates available technological solutions based on advanced
non-invasive monitoring systems, such as wearable and environmental sensors.
In this paper, we show the actual devices chosen as the set of environmental
sensors of the NESTORE ecosystem and, in particular, the tests performed on
custom devices, namely Bluetooth Low Energy (BLE) beacons, developed in the
project’s activities with the aim of detecting social interactions, indoor user’s
behaviour, and the environmental status of the user’s vital space (indoor air
quality, movements, detection of indoor/outdoor).
In recent years, we have witnessed a rapid surge in assisted living technologies
helping the ageing population [31]. Recent advancements in several technological
areas have helped the vision of Ambient Intelligence (AmI) and Ambient Assisted
Living (AAL) to become a reality. These technologies include smart homes, as-
sistive robotics, e-textile, and mobile and wearable sensors. Among the currently
available enabling technological solutions, BLE is becoming the most prominent
in allowing interoperability of wireless technologies in smart environments [24,
38] and it has been widely adopted in different typical AmI/AAL scenarios, from
indoor positioning [37, 29] to human activity recognition [13] and health status
monitoring [35].
In the NESTORE ecosystem, besides the unobtrusiveness granted by this
kind of devices (small in dimensions, ease of installation and maintenance, long
duration of battery), the choice of BLE allows us to provide different kind of
services to the user with the same devices (i.e., social interaction detection,
indoor behavioural index, and indoor thermal comfort) using the inherent ca-
pabilities offered by this technology. We exploit the characteristics of the BLE
technology in terms of type of customizable BLE messages (the BLE devices
embed different sensors like accelerometers, humidity and temperature sensors,
which information is transmitted in the advertisement’s payload) and possibil-
ity to infer proximity using the Received Signal Strength of the received BLE
advertisements.
Exploiting BLE beacons in NESTORE 3
The rest of the paper is structured as follows: Section 2 shows the main com-
ponents of the NESTORE indoor monitoring system with Section 3 describing
the actual implementation of the BLE solutions in the task of detecting social
interactions, inferring the indoor beahvioural index, and monitoring the indoor
thermal comfort of the user’s home.. Section 4 illustrates the main tool used
as feedback to the user, namely the NESTORE coaching app, and how it is
used to show the results of the algorithms presented, while Section 5 draws the
conclusions and future works.
2 The NESTORE monitoring system
The NESTORE environmental monitoring system is an ensemble of wireless sen-
sors able to sense the variables indicated by the domain experts. Furthermore,
it has the aim of detecting the interaction of the user with his circle of friends
and caregivers and the environment itself, while monitoring the status of the
environment, in terms of indoor thermal comfort. For this reasons, we call envi-
ronmental device any sensor deployed in the user’s vital space, while wearable the
device worn by the user during his daily activities. As further source of informa-
tion about the user’s status, we derive data as result of computation and fusion
strategy from a direct input of the user, as questionnaires, while interacting with
the NESTORE coach app. We call the latter soft data.
In order to build the integrated NESTORE environmental monitoring sys-
tem, we first performed a technology selection to satisfy not only the require-
ments coming from the domain experts related to the user profile (to cover as
much variables as possible), but also the ones coming from the co-design activ-
ities in terms of unobtrusiveness. The integrated system should be unobtrusive
under diverse perspectives: R1) user interaction - the user should not wear addi-
tional sensors or explicitly interact with the environmental device; R2) number
of devices - the user’s living environment should not be filled with lot of visi-
ble devices; R3) installation and maintenance - it should be easy to deploy and
maintain the device without additional effort from the user.
In this paper, we focus on the environmental technologies used to cover the
variable indicated by the medical experts of NESTORE, in terms of: sleep mon-
itoring [15, 2], weight monitoring [6], indoor user’s behaviour and environmental
status monitoring [10, 30, 11], and social interaction detection [25, 7, 14]. Figure
1 shows the ensemble of devices and technologies chosen to build the NESTORE
environmental monitoring system. In particular, we will describe the methods
that leverage the presence of BLE beacons as primary source of information,
being the most suitable solution to address the requirements of unobtrusiveness
as listed above.
The BLE beacons are emitters that periodically send beacons (called adver-
tisements in BLE) with a specific rate and power. They are received by other
BLE devices located nearby (e.g., the NESTORE wristband). When the receiver
hears a beacon, it estimates the Received Signal Strength Indicator (RSSI), a
4 F. Palumbo et al.
outdoor indoor
Internet
NESTORE
Cloud
Infrastructure
Fig. 1: The technologies used in the NESTORE monitoring system.
value expressed in a decibel scale (dbm), which can be related to the distance of
the receiver from the transmitter.
3 Exploiting BLE beacons
The hardware kit given to the NESTORE users during the piloting phase is
composed of all the environmental devices (e.g., a smart scale, a ballistocardio-
graphy sensor, five social BLE beacons, and five environmental BLE beacons)
plus the NESTORE wristband with its charger. The subset of devices based on
BLE connectivity is reported in Figure 2. It is composed by the wristband, a
charging station, and five social and five environmental beacons.
Fig. 2: Social and environmental BLE beacons with the NESTORE wristband and its
charging station.
Exploiting BLE beacons in NESTORE 5
The software modules that are going to be described in the following sub-
sections analyse data uploaded to the NESTORE cloud through the NESTORE
Connect agent, a service running in background of the user’s phone.
3.1 Detecting social interactions
This section describes the algorithm to detect the social interactions between
the NESTORE user and his/her circle of friends/caregivers, the algorithm is
referred to as Social Interaction Detector (SID). The basic idea is to analyse
the messages emitted by the social BLE beacons [8] with the goal of detecting
proximity among users. In turn, the proximity between a pair of users (a dyad )
is considered a sociological marker that express the willingness to establish a
social relationship, as also studied in [3]. Differently from [20], SID has been
designed as a Cloud-based service so that to elaborate the data collected remotely
and automatically at periodic intervals. Moreover, SID can analyse data stored
in different data providers such as a MongoDB collection, a plain CSV file or
the NESTORE Robofuse server. SID modifies also the way the interactions are
initially detected and maintained with respect the approach followed in [20].
Each NESTORE user wears the wristband acting as a data logger of the mes-
sages emitted by the social BLE beacons. Beacons are given to the NESTORE
user’s local circle: a set of friends/relatives/caregivers/neighbours etc. that the
NESTORE user identifies during the installation process.
The whole flow comprises the following steps:
1. Messages emitted by the social and environmental beacons are collected by
the wristband.
2. Messages are uploaded through the NESTORE Connect agent to RoboFuse5
in the NESTORE cloud.
3. SID periodically fetches and analyse the data from each NESTORE user.
4. The result of the analysis of SID is uploaded to ZivaCare6 through dedicated
APIs.
5. Results are available for a graphical representation with the NESTORE
coaching app.
SID is implemented in Java programming language; it runs on a dedicates
Virtual Machine (VM) hosted on the NESTORE cloud. SID performs two core
operations at periodical intervals: User profiling and Data retrieval and analysis.
User profiling During the user profiling operation, SID first retrieves the list
of NESTORE users and it checks if each of them has granted the consent for the
analysis of data. If the consent is not given or denied, then the analysis of the
specific NESTORE user is skipped, otherwise SID retrieves the user’s profile:
– The list of IDs of the social beacons assigned to each NESTORE user;
5
https://robofuse.com/
6
https://www.zivacare.com/
6 F. Palumbo et al.
– The list of IDs of the environmental beacons assigned to each NESTORE
user;
– The list of IDs of the friends joining to the NESTORE user’s local circle;
– Other meta-information for each NESTORE user.
Data retrieval and analysis In this phase, SID retrieves for each NESTORE
user the messages collected from its own wristband. Then, it performs the anal-
ysis of the messages collected from the wristbands of each NESTORE user. SID
retrieves messages for a time period (e.g. the last 24 hours) with the goal of iden-
tifying the number and the duration of the social interactions of the NESTORE
user with his/her local circle’s members.
The algorithm considers an interaction between X (NESTORE user) and Y
(NESTORE user’s friends) composed by 3 stages: i) Opening; ii) Keeping; iii)
Closing.
The opening stage lasts for ∆up seconds during which SID checks two con-
ditions:
– Number : to receive at least p % of the expected messages. The expected
messages depend on amount of messages a wristband can collects in a time
period;
– Quality: the RSSI of the messages received must exceed a specific threshold
t.
If both of the two conditions hold, then SID starts an interaction between X
and Y .
The keeping condition lasts for an undetermined period of time. During this
stage, SID checks the Quality condition. In particular, the RSSI of the messages
must always exceed a specific threshold t.
The closing condition checks that for at least ∆down seconds no messages
holding the Quality condition are received. If this happens, then SID closes the
interaction between X and Y .
Fig. 3: Stages detected with the SID algorithm.
A simplified representation of SID is reported in Figure 3. The figure shows
a time interval split in three segments: i) ∆up , during which SID expects at
least the p % of beacons with RSSI over a threshold (green dots); ii) the keeping
Exploiting BLE beacons in NESTORE 7
segment; and iii) the ∆down segment. SID allows to identify not only the number
of interaction for a dyad (X-Y ), but also its duration. In fact, all the messages
analysed are annotated with a timestamp which is used to keep track of the first
receives message (starting time) and the last receives message (ending time).
Data collected with SID can be used to estimate the location of an inter-
action. Such information is obtained by analysing messages coming from the
environmental beacons. Environmental beacons are similar to the social beacons
with the difference that they are deployed in specific locations of the house, e.g.
kitchen, bed room, rest room etc. The basic idea for detecting the location of the
interactions is similar to the algorithm for detecting interaction among people:
if SID detects proximity between NESTORE user X and environmental beacon
E1 deployed in the kitchen at time [t1 − t2 ] and if SID detects proximity between
NESTORE user X and user Y during [t2 − t3 ], then SID infers that NESTORE
user X interacts with Y in position E1 from time t2 to time t3 .
Performance evaluation In order to test and calibrate SID with a suitable
configuration for the NESTORE pilot studies, the following tests have been
conducted with the goal of: i) calibrating SID for detecting the social interactions
(parameters p and t); ii) measuring the performance obtained by SID.
The first test (T est1) reproduces a social interaction during which the friend
always brings the social beacon in proximity: the NESTORE user wears the
wristband and the local circle is composed by one person (the social beacon is
configured with a power of emission of −8dbm). The protocol for reproducing
the social interaction is the following (six runs with the same protocol):
1. The dyad moves 15+ meters away, in order to reproduce absence of interac-
tion:
(a) 4 minutes of non-interaction.
2. The dyad moves 1 to 1.5 meters in proximity, in order to reproduce interac-
tion:
(a) the friend brings the social beacon on the key-chain;
(b) 4 minutes of interaction.
The second test (T est2) reproduces a social interaction during which the
friend “forgets” the social beacon far from the place where the interaction ac-
tually takes place. This scenario reproduces a common situation in which the
a friend visits a NESTORE users and it leaves the bag at the entrance of the
house: the NESTORE user wears the wristband and the local circle is composed
by 1 person (the social beacon is configured with power of emission: -8dbm/
-4dbm / 0dbm. In this case, we tested different settings of the social beacons).
The protocol for reproducing the social interactions is the following (6 runs with
the same protocol):
1. The dyad moves 15+ meters away in order to reproduce absence of interac-
tion:
(a) 4 minute of non-interaction;
8 F. Palumbo et al.
2. The dyad moves 1 to 1.5 meters in proximity in order to reproduce interac-
tion:
(a) The friend leaves the social beacon on the clothes hangers placed about
8 meters away from the NESTORE user;
(b) 4 minute of interaction.
Fig. 4: RSSI fluctuation for T est1.
Figure 4 shows the RSSI fluctuations for T est1. The yellow line represents
the six intervals of four minutes during which the dyad is in proximity. We refer
to such yellow line as the ground truth, since the dyad is actually interacting.
Each of the blue dots wrapped inside an interval shows the RSSI value of the
messages received by the wristband and emitted by the social beacon of the
friend. The more dots, the more messages the wristband captures during the
interaction. The figure also shows the mean RSSI value (horizontal line) of all
the messages received during each of the six interactions.
Fig. 5: Perfect matching between SID and ground-truth.
Exploiting BLE beacons in NESTORE 9
Fig. 6: Partial match between SID and ground-truth.
We measure the performance of SID during the interaction and non-interaction
scenarios of T est1 and T est2. To this purpose, we compare the results of SID
with respect to the ground truth. Figures 5 and 6 report a representative ex-
ample of the performance of SID. The red bars represent the temporal intervals
during which a dyad is actually interacting; the blue bars represent the output of
SID. The more the bars overlap, the more SID performs well. In order to quan-
tify the matching between SID and the ground truth, we measure the following
metrics: True Positive (TP), True Negative (TN), False Positive (FP), and False
Negative (FN). These metrics assess the number of right/wrong answers of SID
with respect to the number of observations in the ground truth (i.e., interaction
or non-interaction for a specific dyad). Given such core metrics, we measure the
accuracy as:
TP + TN
accuracy =
TP + TN + FP + FN
which assesses the proportion of correct answers of out SID with respect to the
total number of observations. We also measure the F1 score, which combines
both precision P = T P/(T P + F P ) and recall R = T P/(T P + F N ), as follows:
P ×R
F1 = 2 ×
P +R
Figure 8 reports Accuracy and F1 score for T est1, respectively. We show a
set of lines of different colours, one for each of the values of selected p and
for different values of t (from -95dbm up to -75dbm). Accuracy and F1 score
decrease progressively with the increase of the t. In particular, the higher the
RSSI threshold used to infer proximity, the more errors are introduced by SID.
Differently from T est1, in which the social beacon is always close to the
interaction place, T est2 is more challenging. In particular, SID has to detect a
meeting even if the social beacon is far from the place of the interaction. We
tested the social beacon with different powers of emission. The tests done with
-8dbm (like in T est1) do not result with a positive outcome. In particular, we
observe that SID reports too many false negative answers: non-interaction rather
than interaction. This is caused by the low power of emission of the messages
that cause a high rate of errors. In order to increase the performance also in
T est2, we set social beacons to -4dbm and 0dbm. The results of Accuracy and
10 F. Palumbo et al.
Fig. 8: Accuracy and F1 score in T est1.
Fig. 10: Accuracy and F1 score in T est2.
Exploiting BLE beacons in NESTORE 11
F1 score are reported in Figure 10. In this case, the Accuracy and F1 score have
acceptable values up to t = −85dbm. After such threshold, Accuracy and F1
score decrease remarkably.
3.2 Indoor behavioural index
The objective of this module is unsupervised user habits detection starting from
the information coming from the environmental BLE beacons. By monitoring the
activation of the sensors embedded in the BLE beacons, indicating the open-
ing/closing of doors and room occupation of the user during his time spent
at home, it is possible to retrieve heterogeneous and multivariate time-series
over long periods. These time-series can be used to learn recurrent behaviors of
the user in her/his daily activities by analysing the time variations of several
parameters like the room occupied by the user and qualitative activity level.
In the literature, many applications, which serve mainly to support diagnosis,
effectively deal with temporal sequences, encouraging the development of the
related “time series mining” research field [34, 32]. Discovery algorithms aim at
extracting important pattern such as similarities, trends, or periodicity, with the
aim of recurrent pattern description or prediction [26]. Encouraging results in
building behavioral profiles of a person living in a smart home are highlighted
in [17] in which a feature mining algorithm is presented. Also in [23] and [22]
behavioral pattern identification methods are proposed using binary similarity
and dissimilarity measures on data generated from occupancy sensors includ-
ing door and motion sensors in a smart home. Understanding the behavioral
profile of a user is extremely important to detect behavioral changes possibly
related to a deterioration of the user physical and psychological status. This is
an emerging research topic addressed in several works for supporting indepen-
dent living of older people. In [21], authors describe a solution based on home
automation sensors, including movement sensors and door entry point sensors.
By monitoring the sensor data, important information regarding anomalous be-
havior are identified using supervised approaches to predict the future values of
the activities for each sensor in order to inform the caregiver in case anomalous
behavior is predicted. Within the scope of the NESTORE project, we intend
to address the unsupervised detection of these forms of behavioral anomalies,
since collecting ground truth information for a long period in a real house can be
very obtrusive for the user. For this reason, we focus on motif search on sensory
data collected in pilot sites, represented as time series, by exploiting the results
obtained in the field of time series motifs discovery [19, 1]. Time series motifs
are approximately repeated patterns found within the data. Such motifs have
utility for many data mining tasks, including rule-discovery, novelty-detection,
summarization and clustering. Since the formalization of the problem and the
introduction of efficient linear time algorithms, motif discovery has been suc-
cessfully applied to many domains, including medicine, motion capture, robotics
and meteorology. An emerging technique used in the field of motif discovery is
represented by “stigmergy” [30, 9]. This is a term derived from the research on
the foraging behavior of ants, which communicate with each other exchanging
12 F. Palumbo et al.
information through the modification of the environment and the information
can only be accessed when an ant visits the place marked by another ant. Sev-
eral works used this technique in order to infer motifs in time series related to
different fields, from DNA and biological sequences [12] to indoor localization
[29] and intrusion detection systems [16].
In the NESTORE scenario, physical displacements of users in their vital
environments can offer information about the change of their individual behavior,
capturing all the areas (rooms in the home) visited by the user over time. In this
domain, useful insights are given by [21] in the field of the representation of sensor
data for further analysis on behavior deviations detection. Authors propose two
different techniques for the summarization of data: combined activity of daily
living signal as a time series and start time and duration. The first method
involves the use of a signal assuming different levels for each activity of daily
living, where each level of the combined signal represents one of the sensors
triggered by the user. In the second one, the signal is represented by the start
time and the duration of an event representing the user entering in a room and
the duration that she stays in a specific location. This approach overcomes one of
the biggest limitations of existing state-of-the-art techniques in pattern discovery
regarding the possibility of discovering pattern occurrences having the same time
length, failing to capture similarities when the occurrences are uniformly scaled
along the time axis.
In NESTORE, the module in charge of discovering routines and habits of the
user is composed of two tasks: one able to tailor the system to the user based
on his daily routines and a task that detects days with abnormal behavior,
in order to check the effectiveness of the system and to monitor any possible
problem in terms of missing engagement of the user or malfunctioning parts of
the system. The detection of the indoor behavior of the user uses data coming
from BLE environmental sensors. In particular, five environmental BLE beacons
are deployed in the user’s home to give information about the indoor mobility
of the user and his interaction with relevant Point of Interests (PoIs) of the
house. During the installation of the NESTORE system, the five BLE beacons
are deployed in the most used areas of the house (i.e., kitchen, living room,
and bedroom) and on commonly used furniture, like the door of the fridge and
the bathroom door. The BLE beacons, leveraging the capabilities offered by the
radio propagation of the Bluetooth signal, provide information about proximity
of the data-gathering device (i.e., the wristband) worn by the user with the
beacon itself, therefore the position of the user in the area in which the beacon
is installed. Furthermore, the beacons embed an accelerometer that is activated
when it is moved, therefore when the furniture is used. Given this scenario, the
module is able to detect the movements of the user inside the house and his
behavior in terms of occupied areas and interaction with the PoIs over the long
period. In order to provide an indoor behavioral index, the module computes
the total occupancy duration over the day for each area. The identified areas
are the three rooms in which the beacons are installed and two additional areas:
Exploiting BLE beacons in NESTORE 13
“other indoor”, when the system is not able to correctly identify a precise room
but collects data indoor, and “outdoor”, when no beacons are collected.
The Indoor Behavior Index (IBI) is computed as follows:
N
X
IBI = |Ai (T ) − Ai (T − 1)|
i=1
where, Ai (T ) represents the percentage over the 24 hours of the occupancy du-
ration in the area Ai during day T , while Ai (T − 1) represents the percentage
over the 24 hours of the occupancy duration in the area Ai during the previous
day (T − 1).
Fig. 11: Randomly generated percentages for occupancy duration during two weeks.
Fig. 12: Trend of the generated IBI for the two weeks of observation.
Table in Figure 11 shows two weeks of percentages for occupancy duration
generated randomly, in order to show how this information can be used to com-
pute the IBI. Once the IBI is computed for each day, the module can estimate
if there is an abnormal behavior in a particular day, when the IBI falls outside
the range of mean(IBI) over a baseline week plus a threshold of 20%. These
values can be parameterised accordingly to the user’s baseline behavior. Figure
12 shows the trend of the generated IBI over the two weeks of observation from
data shown in Figure 11. We can see that, in this way, it is easy to detect an
abnormal behavior during day 8, 9, and 10.
14 F. Palumbo et al.
The formula used to compute IBI can be modified to detect changes related
to a particular day, for example the same day of the previous week, as follows:
N
X
IBI = |Ai (T ) − Ai (T − 7)|
i=1
where, Ai (T − 7) represents the percentage over the 24 hours of the occupancy
duration in the area Ai during the same day of the previous week. Once a day
with abnormal behavior is detected, the system can check the number of activa-
tion of the beacons installed on the fridge and bathroom door in order to further
analyse the behavior of the user in terms of interaction with the house. The aim
of this module is twofold: (i) the validation of the recommended plans provided
by the NESTORE DSS [36, 28], especially when dealing with sedentariness; (ii)
to check the usage consistency of the provided devices, for example a day with
anomalies can be related with a non-properly functioning/not worn wristband
or an offline beacon.
3.3 Indoor thermal comfort
Thermal comfort is defined in [27] as “the condition of mind that expresses
satisfaction with the thermal environment”. Due to individual differences, it is
impossible to specify a thermal environment that will satisfy everybody. There
will always be a percentage of dissatisfied occupants, but it is possible to spec-
ify an environment predicted to be acceptable by a certain percentage of the
occupants. This leads to an evaluation by a subjective point of view but over
the years, a large amount of empirical studies has been conducted how which
parameters are the most influential. These parameters can be divided into per-
sonal and environmental factors. Personal factors include clothing and personal
activity and condition, while environmental factors comprise thermal radiation,
temperature, air speed and humidity. Following the recent trend in literature [33]
and the large availability of Internet of Things (IoT) devices on the market, we
focus on temperature and humidity as key parameters to identify indoor thermal
comfort.
In literature, we can find two main standards specifying indoor thermal com-
fort: ISO EN 7730 [18] and ASHRAE 55-1992 [4]. Besides complex models that
include air speed and thermal radiation (predominant outdoor rather than in-
door) and also involve personal factors (e.g., predicted mean vote, clothing insu-
lation, metabolic rate, etc.), both standards propose simpler models for thermal
comfort based on Relative Humidity (RH) and Temperature (T), easier to be
measured and controlled in indoor environments.
Figure 14 shows the RH/T diagrams proposed by the two standards. The
IS EN 7730 approach for defining the comfort zone does not take into account
the fact that higher temperatures can be tolerated at low humidity. Hence, its
lower and upper temperature limits are vertical. Although temperature ranges
are specified per season, the relative humidity is set between 70%RH and 30%RH
in summer and winter time, respectively. The limits are set to reduce the risk
Exploiting BLE beacons in NESTORE 15
(a)
(b)
Fig. 14: RH/T diagram showing the comfort zone according to ISO EN 7730 (a) and
ASHRAE 55-1992 (b). Images taken from [33].
of eye irritation, dry or wet skin, microbial growth, and respiratory diseases.
ASHRAE 55-1992 also considers the effect of experiencing higher temperature
together with high relative humidity (as can be seen in Figure 13b, in which
graph presents oblique boundaries for the lower and upper limits of T). In the
NESTORE scenario, we implemented the ISO EN 7730, to present to the user
the thermal comfort status of his house.
4 The NESTORE coaching app
One of the main points of the interaction between users and the NESTORE
ecosystem in represented by the coaching app. A big role in the developing
process of the NESTORE User Interface has been played by the interaction
design approach. During the design process, the system is divided into small
modules, defining the basic building blocks of the User Interface. Then, the
foundational elements are combined to create functioning units with different
features. Finally, units are combined in order to form different sections of the
interface. This flexible methodology, accommodative to changes, allows creating
complex systems, managing consistency and scalability.
Figure 16 shows some screenshots of the NESTORE coaching app regarding
the output of the elaborations based on the environmental devices. In particular,
we can see the graphs produced for indoor social interactions (Figure 15a) and
the indoor status of the user’s environment in a good (Figure 15b) and bad
(Figure 15c) status, together with the screens of one of the other services based
on environmental device, like the sleep monitoring systems (Figure 15d).
16 F. Palumbo et al.
Social Interaction Thermal comfort (Good) Thermal comfort (Bad) Sleep monitoring
Fig. 16: The charts provided to the NESTORE users.
5 Conclusion and future work
The proposed systems for detecting social interactions, indoor behavioural index,
and indoor thermal comfort take advantage of the BLE beacons capabilities of
inferring proximity among devices and in terms of ease of embedding different
sensors. They represent the core set of environmental devices composing the
NESTORE monitoring system.
The BLE beacon capabilities enable rich context-aware interaction scenarios
with the NESTORE system. For future works, being able to know in real-time
the position of the user, the system can proactively deploy notifications and re-
minders in the different user interfaces of the NESTORE system. Indeed, prox-
emics can be used to enable different interactions: when the user is approaching
the tangible coach, the vocal assistant could start speaking; when the user is in
the same room of the tangible coach, peripheral light (LED in the tangible) can
be used to display a notification; when the user is an a different room, sound
notifications can be used; when the user is not home, notifications can be sent
in the NESTORE Coach app.
Acknowledgements
This work has been funded by the EU H2020 project “Novel Empowering So-
lutions and Technologies for Older people to Retain Everyday life activities”
(NESTORE), GA769643. The authors wish to thank all the partners involved
in the project.
Exploiting BLE beacons in NESTORE 17
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