Behavioural Data Modeling: A Case Study in IoT

Jiri Petnik

, Lenka Lhotska

, Jaromir Dolezal

and Jindrich Adolf

Faculty of Information Technology, Czech Technical University in Prague, Th

akurova 9, 160 00 Prague 6, Czech Republic

Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Jugosl

avsk

ych

partyzanu 1580/3, 160 00 Prague 6, Czech Republic

Keywords:

Behaviour Informatics, Behaviour, Sensor, Topology, Internet of Things, Smart Home, Elderly Care.

Abstract:

Modeling and analysis of behaviour by using data extracted from Internet of Things (IoT) sensors is an open

area. We take Behaviour Informatics (BI) as a formal representation into account and describe the case study

of the apartment monitored by IoT sensors. The case study targets persons who live home alone (e.g., elderly

people) without assistants (nurses), or any roommates. We present the apartment as a directed multigraph and

propose the model to deal with the conversion of transactional data coming from IoT sensors into behavioural

feature space represented by behavioural vectors. Further, the article describes a few use cases which can

occur in the apartment with installed sensors and explains how behavioural vectors are created. Last but not

least, we present the high-level overview of the complex system for detection and evaluation of behaviour

identiﬁed from data of IoT sensors.

1 INTRODUCTION

European Union countries are facing unprecedented

and extraordinary challenges linked to the progres-

sive aging of their population. Research is fo-

cused on novel medical solutions on one side and on

technologies that might support independence, self-

management of elderly persons (those living alone

in particular) on the other side. In addition, these

technologies allow long-term monitoring of physio-

logical parameters and changes in behaviour. One of

the enabling technologies in this area is the Internet

of Things (IoT). The IoT as a paradigm deals with

the connection of physical devices, vehicles, build-

ings, and other items – embedded with electronics,

software, sensors, actuators, and network connectiv-

ity that enable these objects to collect and exchange

data. The collected data represent different events,

phenomena, etc. The most frequently analyzed data

are physiological parameters, for example, heart ac-

tivity (heart rate, electrocardiogram), breathing rate.

They are represented as time series derived from mea-

sured analog signals. Besides this kind of data, it is

currently possible to acquire data from sensors placed

in buildings, apartments. Usually, this data has the

binary form (yes/no, logical 0/logical 1). A single

item does not provide information. To be able to in-

terpret a sequence of data from more sensors placed

in the rooms it is necessary to know the topology of

the space, types of sensors, possible actions, etc. It is

evident that these items cannot be represented in the

same form as physiological signals. We found a for-

malism that enables efﬁcient representation and ma-

nipulation of data – Behaviour Informatics (BI).

The paper is structured as follows. Section 2 de-

scribes the basic features of BI and deﬁnes behaviour

model, vector and sequence. Section 3 presents

a case study, describes the apartment as a directed

multigraph, and proposes the model how to convert

transactional data coming from IoT sensors into be-

havioural feature space represented by behavioural

vectors. In Section 4, illustrative use cases are shown.

Section 5 discusses the proposed model and its use

in a complex system for detection of behaviour. Sec-

tion 6 concludes the paper.

2 BEHAVIOUR INFORMATICS

One of the critical IoT application within the smart

healthcare area is called behavioural detection, be-

havioural analysis, or BI (Sheriff et al., 2015). BI can

be used for initial problem description and formalism.

Petnik, J., Lhotska, L., Dolezal, J. and Adolf, J.

Behavioural Data Modeling: A Case Study in IoT.

DOI: 10.5220/0007691902590264

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 259-264

ISBN: 978-989-758-353-7

259

2.1 Deﬁnition

BI is a scientiﬁc ﬁeld which aims to develop method-

ologies, techniques and practical tools for represent-

ing, modeling, analyzing, understanding and/or utiliz-

ing symbolic and/or mapped behaviour, behavioural

interaction and networking, behavioural patterns,

behavioural impacts, the formation of behaviour-

oriented groups and collective intelligence, and be-

havioural intelligence emergence (Cao and Yu, 2009).

Within healthcare, the IoT system with the ana-

lytical layer may help specialists to detect severe ill-

nesses at their early stages and subsequently prevent

the loss of life (Ahmed et al., 2017). Nowadays, we

use several IoT devices to monitor the human body.

The wearables are employed for direct monitoring;

while the sensors installed in the ambient environ-

ment monitor the human body indirectly. In combi-

nation with clinical data, all of these can be used to

model the particular behaviour of speciﬁc individuals

or precision cohorts.

2.2 Abstract Behaviour Model

A behaviour B is described as a four-ingredient tu-

ple (Cao, 2013):

B = (E , O, C , R) (1)

It indicates:

• Actor E =

S E, OE

is the entity that issues a be-

haviour (subject, S E ) or on which a behaviour is

imposed (object, OE ).

• Operation O =

OA, S A

is what an actor con-

ducts in order to achieve certain goals; both objec-

tive (OA ) and subjective (S A) attributes are asso-

ciated with an operation. Objective attribute may

include time, place, status, and restraint; while

subjective aspects may refer to action and its ac-

tor’s belief and goal of the behaviour, and the be-

haviour impact on business.

• Context C is the environment in which a be-

haviour takes place.

• Relationship R =

θ(·), η(·)

is a tuple which re-

veals complex interactions within an actor’s be-

haviours (namely intra-coupled behaviours, rep-

resented by function θ(·)) and that between mul-

tiple behaviours of different actors (inter-coupled

behaviours by relationship function η(·)).

2.3 Behaviour Vector

Behaviour can also be represented as a behavioural

vector

−→

γ which consists of behavioural attributes

(basic properties) including social and organizational

factors. When deploying this abstract model into dif-

ferent domains, some attributes and properties may

not be present, and thus a simpliﬁed behavioural

model

−→

can be deﬁned, e.g., as follows (Cao, 2010):

−→

= (s, o, a, f , t) (2)

It indicates that a behavioural subject (s) conducts an

action (a) on an object (o) at a time (t) which leads to

a certain impact ( f ).

2.4 Behaviour Sequence

Further, a behaviour sequence Γ

of an actor can be

represented in terms of a vector sequence

−→

, which

consists of the behaviour instances represented in vec-

tors (Cao, 2010):

−→

, ...,

−→

(3)

3 CASE STUDY

Basic task of BI is the preparation of behavioural

data. Usually, we deal with transactional data which

are not relevant for further behavioural analysis, and

thus a data conversion from transactional space to be-

havioural feature space is necessary (Cao, 2010). In

the behavioural feature space, behavioural elements

are presented in behavioural itemsets.

The movement of a person in the environment of

his/her apartment can be considered as one of the pri-

mary activities, which can be monitored. It is evident

that possible movement is given and constrained at the

same time by the actual structure of the apartment, its

topology. The topology clearly deﬁnes the organiza-

tion of individual rooms and mutual interconnections.

We can differentiate the movement either within the

particular room or movement (transition) between the

rooms.

Assuming we can detect the movement itself,

we can further infer the activity of the individual –

whether he or she is active during the day, or rather

stays in a single room, whether he or she has some

problems with sleeping, how often he or she visits

the restroom which can inform us about some urinary

problems, etc.

This case study targets persons who live home

alone (e.g., elderly persons), without assistants

(nurses), or any roommates. Thus we consider a sin-

gle actor model. The movement is detected in an indi-

rect way by using sensors, so we talk about so-called

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

260

Mapped behaviour. The main focus is to detect and

be able to describe transitions between each room.

3.1 Floor Plan and Sensors Placement

The apartment ﬂoor plan is depicted in Figure 1. This

is a common apartment with living room, kitchen,

bedroom, bathroom, and toilet. The central part is

formed by the main hall which interconnects most of

the rooms together. The unﬁnished wall partially sep-

arates the space of the living room and the kitchen.

The main entrance door is marked in green. The rest

of the doors are marked in bold black (including the

door to the balcony). Windows are highlighted with

blue. For further description, each room is labeled

with a letter mentioned in parenthesis.

Figure 1: The ﬂoor plan of the apartment with sensors (PIR

sensors as red dots, Grid-EYE sensors as purple squares,

magnetic sensors on all windows and doors are not high-

lighted). Windows are marked in blue, the main door in

green, the rest of the doors in bold black.

The selection and placement of the sensors is

based on the study and the experimental veriﬁcation

how to deal with its data as discussed in (Lhotska

et al., 2018). We propose to use:

• Passive Infrared (PIR) Sensor – is a standard mo-

tion detection sensor used in security technology

solutions. Since it works in an infrared portion of

the spectrum, it operates reliably during both day

and night. This sensor has two possible states at

its output; logic 0 if it does not detect movement,

or logic 1.

• Grid-EYE Infrared Array Sensor – is the sensor

made by Panasonic. The output is an 8 x 8 matrix

of temperatures of the scanned area. The built-

in lens includes a 60-degree viewing angle which

gives us, assuming ceiling height to be 3m, a de-

tection area about (3.48 x 3.48) m.

• Magnetic Sensor – consists of two non-connected

parts. The ﬁrst part which contains a magnet is

usually placed on movable parts (doors or win-

dows). The second part is formed by a circuit with

a switch that is turned on and off by the magnetic

ﬁeld of the ﬁrst part.

To detect a simple movement inside a room or

translation between rooms, PIR sensors with the com-

bination of magnetic sensors on the doors should be

sufﬁcient. In case we want to reﬁne position within

the apartment, the use of Grid-EYE sensor is pro-

posed. Since the Grid-EYE sensor provides com-

plex information about temperature footprint, this can

also be used as a conﬁrmation that the motion activ-

ity was performed by a living person and, e.g., not by

a robotic vacuum cleaner which is cleaning the apart-

ment.

To collect data from the sensors a Programmable

Logic Controller (PLC) is used which fulﬁlls regular

data acquisition (Lhotska et al., 2018). The sensors

are connected via a wired bus or wireless bus. In an

ideal case, the wired infrastructure for connecting the

sensors is built during the construction of the apart-

ment. Another option is to implement the system with

the use of the IoT platform (Petnik and Vanus, 2018).

In any case, we must deal with the data which needs to

be processed, evaluated, and put into context. A mix

of different sensors and their data can enhance ﬁnal

accuracy.

The algorithms and approaches used for data pro-

cessing and evaluation depend on the chosen data ac-

quisition method. It can be either regular reading of

all sensors at once or event-driven approach where

sensors provide data independently to each other. The

comparison of possible approaches is out of the scope

of this article, and the topic can be a subject of subse-

quent research.

Further sections deal with the formal description

of how a structural arrangement of the apartment can

inﬂuence and support the acquisition of behavioural

data.

3.2 Topology

The apartment can be represented as a directed multi-

graph:

G = (V, E, f ) (4)

which consists of a set V of vertices (or nodes), a set

E of edges, and a function f : E → V × V mapping

each edge with its incident vertices. The orientation

Behavioural Data Modeling: A Case Study in IoT

261

of edges is preserved by order of nodes in the map

function f .

Let V be the uniﬁcation of three sets as follows:

R ∪ EV ∪UV = V (5)

where R is a set of vertices which represent rooms

(e.g., living room, kitchen, bedroom), EV is a set of

virtual vertices which represent areas directly con-

nected to natural entrances of the apartment (e.g.,

shared hall, garden), and UV is a set of virtual vertices

which represent areas directly connected to possi-

ble but non-standard entrances of the apartment (e.g.,

space outside the window).

Let E be the uniﬁcation of two sets as follows:

DE ∪W E = E (6)

where DE is a set of oriented paths through standard

building holes (e.g., doors, gateways, some logical ar-

rangement of two spaces shared in one room – living

room with shared kitchen), and WE is a set of ori-

ented paths through non-standard entrances (usually

windows).

Map function f can be represented by unsymmet-

rical vertex-edge incidence matrix of a graph G, de-

noted VE, which is determined by the incidences of

vertices and edges in G. Let’s use +1 values for pos-

itively incident edges, the -1 values for negatively in-

cident edges, otherwise use 0 values.

3.2.1 Model Description

The apartment in Figure 1 can be represented by the

topology in Figure 2 where R = {a, c, e, h, k, l,t} is

the set of rooms with balcony. EV = {s} is the set

with the single item representing the shared hall of

an assisted living facility (ALF). Outer space behind

all the windows and balcony is described by the sin-

gle node as part of the UV = {o} set. The DE set

contains all standard transitions between rooms (see

black and green edges), while the W E set includes

unusual transitions via windows (see dashed edges

marked in blue).

Partial VE incidence matrix of the topology of the

apartment can be seen in Equation 7.

VE(G) =







h−a a−h ··· o−k k−o

a −1 1 ··· 0 0

c 0 0 ·· · 0 0

e 0 0 ·· · 0 0

h 1 −1 ··· 0 0

k 0 0 ··· −1 1

l 0 0 ·· · 0 0

t 0 0 ··· 0 0

s 0 0 ·· · 0 0

o 0 0 · ·· 1 −1







(7)

Figure 2: The topology of the apartment (virtual nodes are

dashed, entrance transition via the main door is marked in

green, unusual transition via windows are highlighted with

dashed blue).

3.3 Behavioural Walk

Let’s deﬁne a Behavioural walk BW as an alternation

sequence of nodes and edges:

BW = hv

, e

, v

. . . , v

n−1

, e

, v

i (8)

where (∀v; v ∈ V ) and (∀e; e ∈ E), which represents

sequential behavioural activity (movement) of a per-

son in an apartment from the room v

to the ﬁnal room

. BW can be used for description of person’s trans-

lations between apartment rooms for the relevant pe-

riod (i.e., an hour, a day, a week or so). The length of

a BW is the number m of its edges.

m = len(BW ) (9)

3.4 From Behavioural Walk to

Behavioural Vector

The deﬁnition of BW is based on a walk from graph

theory and its usage here is suitable for the formaliza-

tion mapping of a topology describing the structure

of the apartment itself into the behavioural vector

−→

which forms a cornerstone of further behavioural pat-

tern analyses.

Let

−→

be a simpliﬁed behavioural vector:

−→

= (s, a, p,t) (10)

It indicates that a behavioural subject s (a person) con-

ducts an action a (a translation between rooms) in

place p at a time t (a timestamp when the translation

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262

was detected). The attribute p can be used to spec-

ify a room (both physical and virtual) from which the

translation was started, or, e.g., a ﬂoor of the house.

Except for the starting node v

of Behavioural

walk BW , all subsequent adjacent pairs of nodes and

edges (e

k−1

, v

) where 1 < k ≤ n can be converted by

the process B into the simpliﬁed manifestation of be-

haviour vector as described in Equation 10:

B : {e

k−1

, v

} →

−→

(11)

The ﬁnal Behaviour vector sequence

−→

, ...,

−→

(12)

where 0 < i ≤ m then contains m behaviour vectors

which is equal to the length of initial Behavioural

walk BW .

4 USE CASES

We assume that a person’s behaviour is partly pre-

dictable and that he or she does not try to disguise

his or her behaviour intentionally. Following sections

describe several typical situations which can occur

in the monitored apartment and presents how the be-

havioural vector sequence

−→

is gradually created. It

is obvious that

−→

can be created by batch process-

ing or by real-time processing when the system can

decide with a certain degree of conﬁdence. For the

description, we present the real-time variant.

The person (identiﬁed by ‘s’) gets up of his bed

in the morning and is moving around the bedroom.

PIR sensors detect movement. The Grid-EYE sen-

sor conﬁrms the living person is present. We do not

create any behaviour vector that would describe this

state. Right now, we understand movement in the

room as a standard situation which does not need to

be further detailed. The person is opening the bed-

room’s door, and the magnetic sensor warns that the

door is opened. The person is going through to the

hall. After a while, PIR sensors in the bedroom do

not detect any activity, nor does the Grid-EYE. On

the other hand, PIR sensors and the Grid-EYE sensor

in the hall detect the presence of the person. Taking

the description of deﬁned Behavioural walk (BW ) in

mind, the person just moved from the starting node

to the node v

via the edge e

. This transition is

therefore described by simpliﬁed behavioural vector

−→

= (‘s’, ‘eh’, ‘e’, t

) where ‘s’ stands for the per-

son, ‘eh’ is the identiﬁcation of the movement ac-

tion from node e to h, and t

is the variable repre-

senting the timestamp when this behaviour was de-

tected. The system resets (updates, stores, etc.) its

internal state and understands the living person is

present in the hall. The person further visits the

toilet, has breakfast in the kitchen, and then leaves

the apartment. Without the same level of details,

the rest of these steps is identiﬁed by the following

behaviour vectors: (‘s’, ‘ht’, ‘h’,t

), (‘s’, ‘th’, ‘t’, t

(‘s’, ‘hl’, ‘h’, t

), (‘s’, ‘lk’, ‘l’, t

), (‘s’, ‘kl’, ‘k’, t

(‘s’, ‘lh’, ‘l’,t

), (‘s’, ‘h−exit’, ‘h’,t

). The last action

‘h −exit’ expresses that the person left the apartment.

By taking this situation as the initial state, we can

imagine that the person has a robotic vacuum cleaner

which starts to clean the apartment. Movement is de-

tected by PIR sensors, but the evidence of living per-

son is missing, so the activity is logged, but this has

no inﬂuence on the overall behaviour of the person.

Last but not least, let’s imagine an extreme situ-

ation. A living person has left the apartment, and

suddenly the magnetic sensor of the window in the

kitchen detects the window is being opened. This can

simply mean the sensor could have been broken and

it needs to be repaired, but in combination with the

PIR sensors and the Grid-EYE sensor in the kitchen,

the presence and movement of a living person are de-

tected. Since this is the transition from the node o to k

via the edge ‘ok’, this behaviour should be described

by a behaviour vector, but in this case, the system

is missing the context, and the activity is suspicious.

This behaviour is logged, but the identiﬁer for an un-

known person is used. As can be seen in Figure 2,

this particular transition follows dashed line marked

in blue. This edge is a part of W E set which can be

understood as the set of edges with warnings. Any

of behaviour vectors which are created by following

these types of lines must be reported as unusual and

the operator of the system, as same as the user (if it is

relevant or possible), should be informed about these

events.

5 DISCUSSION

The proposed model deals with the conversion of

transactional data coming from IoT sensors into be-

havioural feature space represented by behavioural

vectors. These behavioural vectors can be used fur-

ther by the processes which try to identify behavioural

patterns. It is obvious that the topology of the mon-

itored apartment must be taken into account and it

forms an undivided part of the whole system. For

instance, the distribution of edges among two sep-

arated sets DE, and WE allows immediately distin-

guish which transition (detected behaviour) is natural

and which requires further actions (transitions follow-

ing edges which are as part of the latter set). Events

Behavioural Data Modeling: A Case Study in IoT

263

in the system must be created as reactions to these sit-

uations. As was presented in the previous Section 4,

the example can be a housebreaker entering the apart-

ment in case a resident is outside. In extreme case,

the similar situation can happen if a resident falls out

of the window, etc.

In Figure 3 we present an overview of the com-

plex system where the behavioural data are prepared

by using a combination of standard data processing /

evaluation and the topology of monitored apartment.

Both the events emitted by data processing layer and

ﬁndings of behaviour pattern analysis should be fur-

ther processed by an expert system which can decide

and act.

Figure 3: Proposal of the complex system for detection and

evaluation of behaviour from data of IoT sensors.

6 CONCLUSIONS

Technology may be advantageous when monitoring

persons’ health state and activities during everyday

life in their homes and at work continuously because

it helps adjust a personalized health state model, in

particular for a person with a chronic disease. We are

well aware of the fact that many elderly people prefer

not to use wearables, at least in their homes. There-

fore one of the aims of our presented study is to ﬁnd

methods and tools for indirect monitoring based on

sensors installed in the ambient environment. Next

step is then to identify methods that can easily pro-

cess and evaluate data acquired from such sensors.

We showed the basic principles of BI that might be

utilized for transparent representation of the environ-

ment, sensors, data, and moving persons.

ACKNOWLEDGEMENTS

Research has been supported by the Czech Ministry

of Industry and Trade project No. FV-20696 Personal

health monitoring and assistive systems.

REFERENCES

Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed,

A. I. A., Imran, M., and Vasilakos, A. V. (2017). The

role of big data analytics in Internet of Things. Com-

puter Networks, 129:459 – 471. Special Issue on 5G

Wireless Networks for IoT and Body Sensors.

Cao, L. (2010). In-depth behavior understanding and use:

The behavior informatics approach. Information Sci-

ences, 180(17):3067 – 3085. Including Special Sec-

tion on Virtual Agent and Organization Modeling:

Theory and Applications.

Cao, L. (2013). Behavior Informatics and Computing. Ad-

vanced Analytics Institute, University of Technology

Sydney, Australia.

Cao, L. and Yu, P. (2009). Behavior Informatics: An Infor-

matics Perspective for Behavior Studies. IEEE Intel-

ligent Informatics Bulletin, 10:6–11.

Lhotska, L., Dolezal, J., Adolf, J., Potucek, J., Krizek, M.,

and Chbani, B. (2018). Z

ere

a zpr

ava re

ser

se sen-

zor

u a

cidel -– project No. TRIO FV20696. [Unpub-

lished project report, CTU CIIRC Prague].

Petnik, J. and Vanus, J. (2018). Design of Smart Home

Implementation within IoT with Natural Language In-

terface. IFAC-PapersOnLine, 51(6):174 – 179. 15th

IFAC Conference on Programmable Devices and Em-

bedded Systems PDeS 2018.

Sheriff, C. I., Naqishbandi, T., and Geetha, A. (2015).

Healthcare informatics and analytics framework. In

2015 International Conference on Computer Commu-

nication and Informatics (ICCCI), pages 1–6.

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

264