Evaluating the Reliability of Ambient-Assisted Living Business

Processes

Ricardo Martinho

, Dulce Domingos

and Ana Respício

Polytechnic Institute of Leiria and CINTESIS - Center for Health Technology and Services Research, Leiria, Portugal

LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

Centro de Matemática, Aplicações Fundamentais e Investigação Operacional, Faculdade de Ciências,

Universidade de Lisboa, Lisboa, Portugal

Keywords: Ambient-Assisted Living, Reliability, Business Processes, BPMN.

Abstract: Ambient-Assisted Living (AAL) systems provide a wide range of applications in order to improve the

quality of life of patients. These systems commonly gather several components such as sensors, gateways,

Information Systems or even actuators. Reliability of these components is of most importance, mainly due

to the impact that a failure can have on a monitored patient. In spite of the existing reliability evaluations

and countermeasures that can be associated with an AAL system component, we need to take into account

the overall reliability for the several activities and interactions that exist between all the AAL system

components, for each time a certain value is registered or a certain alert is triggered. In this paper, we

propose a new approach to calculate the overall reliability of an AAL system. We take a Business Process

Management (BPM) approach to model the activities and interactions between AAL components, using the

Business Process Model and Notation (BPMN) standard. By extending the BPMN standard to include

reliability information, we can derive the overall reliability value of a certain AAL BPMN process, and help

healthcare managers to better allocate the appropriate resources (including hardware or health care

professionals) to improve responsiveness of care to patients.

1 INTRODUCTION

The major purpose of Ambient-Assisted Living

(AAL) systems is to improve the quality of life and

care responsiveness for patients at risk while staying

at their homes and performing their normal daily

routines (Islam et al., 2015). AAL provides them

with an overall surveilled environment, allowing the

delivery of care where and when needed, and also

supporting caregivers, families and care

organizations.

Applications of AAL not only provide

continuous health monitoring through, for instance,

vital signs recording for medical history analyses,

but also play a major role in detecting emergency

situations. In turn, caregivers and/or other health

professionals can better organize their care business

processes by receiving alerts and actuating when

needed, and with the appropriate resources. Some

AAL applications can even replace (self) care

activities, such as auto injecting insulin when blood

sugar values increase at a certain rate.

Although many times associated with support in

assisting elderly people (see for instance H2020 calls

of European Commission), AAL systems can also be

used in patients suffering from chronic diseases such

as diabetes, asthma and heart attacks. Therefore, the

impact of a less reliable system can range from a

false alarm transmitted to a certain caregiver and/or

emergency unit service, to serious patient injury due

to wrong, delayed or even non-delivered care.

Current research works and industry products

related with AAL and overall to Internet of Things

(IoT) applied to healthcare already provide

redundancy checks and alerts to prevent greater

impacts to patients using them (see, for instance,

Parente et al., 2011; Siewiorek and Swarz, 2014).

Nevertheless, these efforts to increase reliability are

usually self-contained to some components of an

AAL system, i.e., reliability is commonly evaluated

for each component, regardless of its position in a

certain sequence of activities to trigger some action

(alert, register or even actuate).

In this work, we propose a new and consolidated

approach to calculate the overall reliability of an

AAL system, by using a Business Process

528

Martinho, R., Domingos, D. and Respício, A.

Evaluating the Reliability of Ambient-Assisted Living Business Processes.

In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 528-536

ISBN: 978-989-758-187-8

Management (BPM) approach and the Business

Process Model and Notation (BPMN) (OMG, 2011)

standard de facto for modelling AAL business

processes. We consider each component of an AAL

system as part of a business process containing

essentially sensors, actuators and gateways, which

interact through a sequence of activities, decision

nodes and messages in order to produce alerts, to

Information System, or even to trigger actuators to

provide immediate care. Since these interactions are

usually subjected to several conditions, we model

them as BPMN process models, in order to calculate

their combined reliability. This way, we can derive

the overall AAL system reliability, such as in the

following example: a measure is taken by a heart

rate sensor, transmitted through a network, evaluated

through an Information System, and the appropriate

alerts are triggered to prevent potentially fatal

consequences for the patient.

This paper is organized as follows: section 2

presents background on AAL and a typical AAL

system scenario modelled with BPMN. In section 3

we refer to related work on reliability applied to

most common components of an AAL system, and

in section 4 we explain how we include reliability

information in an AAL BPMN process model, in

order to calculate its overall reliability and how we

apply the Stochastic Workflow Reduction (SWR)

algorithm to compute the reliability of combined

BPMN process elements. Section 5 presents an

application scenario for the calculus of the overall

reliability for a typical AAL BPMN business

process. Finally, section 6 concludes the paper and

presents future work.

2 BACKGROUND

This section presents a typical AAL process model

(see for instance the proposals of Bui and Zorzi

(2011) and Dar et al. (2014)).

The AAL BPMN process model, as illustrated in

Figure 1, uses a collaboration diagram with four

pools, one for each participant or AAL component

(Rodrigues et al., 2012; Rashidi and Mihailidis,

2013; Memon et al., 2014; and Islam et al., 2015).

The Body Area Network (BAN) sensor devices

are used for monitoring vital signs, i.e., heart and

body activity in this example (based on Parente et

al., 2011). The heart activity is assessed through the

heart rate, the blood oxygen, and the blood pressure,

by using a heart rate monitor, a pulse oxymeter and a

sphygmomanometer, respectively. The system

monitors the body activity by using an

accelerometer. While this process only uses sensors,

BANs can also include actuators. For instance BAN

devices can, on a diabetic patient, auto inject insulin

through a pump, while monitoring the insulin level

(Jara et al., 2011).

As defined in this process, sensors read values

from the patient from time to time by using a timer

and send them to the BAN gateway. The interaction

between sensors and the BAN gateway can also be

implemented through the request-request paradigm,

Figure 1: AAL BPMN process model.

Evaluating the Reliability of Ambient-Assisted Living Business Processes

529

where the BAN gateway starts the interaction asking

for the values. Depending on sensor computational

capabilities, they can also filter the data they

transmit, sending only values that are considered

relevant. However, for this reliability study, these

differences are not significant.

The BAN gateway, another participant of the

process, is responsible for the communication inside

that BAN and to the home gateway. Besides it

receives the values from sensors, it also validates,

aggregates and analyses these values. The reception

of sensor values is modelled with a BPMN Event-

Based Exclusive Gateway. The information about

heart rate should be provided by at least two out of

three devices, and this behaviour is modelled with a

BPMN Complex Gateway. After evaluating sensor

values, the BAN gateway sends an alarm to the

health monitoring system (HMS) to assist the

patient, in case any emergent situation is detected.

The communication between the BAN gateway and

the HMS is performed through the home gateway.

Smart phones or wireless routers can be used as

home gateways. They communicate with the BAN

gateway through wireless technologies (Bluetooth or

WiFi, for instance) and provide the connectivity to

the internet. From the point of view of the process

model we could omit the Home gateway pool, as it

does not define any business logic. However, this

way, the participants of the process are coherent

with the components of a generic AAL architecture

and it simplifies the reliability study as the process

includes all the components and connections.

Finally, with the health monitoring system,

caregivers and physicians monitor patients remotely.

3 RELATED WORK

Koren and Krishna (2007) define reliability of a

system at time t, denoted by (), as the probability

of the system to be up continuously in time interval

[0,]. This metric is adequate for systems operating

continuously, where a single momentary failure can

have a high or even critical impact.

McNaull et al. (2012) discuss the quality issues

of each component of an AAL system. BAN devices

(sensors and actuators) reliability depends on their

quality and manufacturer. According to the same

authors, the mean-time between failures (MTBF)

metric can be used to assess it. In addition, sensors

data quality (accuracy) also interferes with reliability

as anomalous values can be discarded, for instance,

in BAN gateways. Quality of data depends on sensor

calibration as well as on the correct use and

application of sensors. For instance, other heat

sources can affect temperature sensors.

Parente et al. (2011) present a use case where

they monitor the health of patients considering heart

and body activities. The system uses a heart rate

monitor, a pulse oxymeter, and a sphygmo-

manometer to monitor the heart activity. The body

activity of patients is monitored with an

accelerometer on knees and a motion detector in the

room. Taking into account the required reliability of

the system, the authors determine the minimal

combinations of sensors the system needs. However

they only use the information about the reliability of

each device.

BAN gateways can be used to increase the

reliability of the system. They may evaluate sensor

data and detect anomalous and inconsistent values,

considering the expected ones, which may have been

established during the testing period of the AAL

system (McNaull et al., 2012). In case of anomaly,

erroneous sensor values are discarded and BAN

gateways can request for new sensor values. If the

problem persists, the BAN gateway can alert the

health monitoring system. Another way to increase

system reliability is by defining a fault tolerant

behaviour for the BAN gateway.

Body sensors and actuators communicate with

each other and with the BAN gateway using mostly

wireless technologies, such as IEEE802.15.4

/ZigBee (IEEE, 2011). The latest international

standard for wireless BAN (WBAN) is the

IEEE802.15.6 (IEEE, 2012). Home and BAN

gateways also communicate through wireless

technologies (Bluetooth or WiFi, for instance).

Reliability of wireless networks depends on

interferences of other devices; obstruction of the

signal due to lifts or wall, and attenuation, i.e., the

strength of the signal reduces during transmission.

Baig et al. (2014) compare wireless transmitted data

with manual recorded data and hospital collected

data. They use a total of approximately 2500

transmissions of 30 hospitalized patients and they

conclude that, in wireless transmitted data, losses

vary from 20% (blood glucose) to 80% (blood

pressure and heart rate). They also conclude that

data losses were mainly due to distance and data

transmission delays were due to poor signals, signal

drops, connection loss and/or poor location.

Despite the evaluation of the reliability of each

AAL component is crucial, it is not sufficient to

study the overall system. This way, in the following,

we present related work about computing reliability

for composite tasks and/or even for the overall

process.

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

530

Indeed, while reliability has been a major

concern for networking, critical and real-time

applications, as well as middleware (Parente et al.,

2011; Siewiorek and Swarz, 2014); the increasing

use of workflow, specifically, in more critical

systems, justifies the works on workflow reliability.

In the context of workflow modelling, Cardoso

(2002) defines task reliability as the probability that

the components operate on users demand, following

a discrete-time model. In this context, the failure rate

of a task can be described by the ratio number of

unsuccessful executions/ scheduled executions. The

task reliability, denoted by

R(A), is the opposite of

the failure rate, that is:

R(A) = 1 – failureRate(A).

In the same work, Cardoso proposes a predictive

Quality of Service (QoS) model for workflows and

web services that, based on atomic task QoS

attributes, is able to estimate the QoS for workflows,

considering the following dimensions: time, cost,

reliability, and fidelity. To compute QoS for the

overall workflow, the author developed the

Stochastic Workflow Reduction algorithm, which

applies a set of reduction rules to iteratively reduce

construction workflow blocks until only one activity

remains. The QoS metrics of the remaining activity

corresponds to the QoS metrics of the process.

Cardoso defines reduction rules for the following

construction blocks: sequential, parallel, conditional,

loop, fault tolerant, and network systems. He applies

his proposal to the METEOR workflow management

system (Krishnakumar and Sheth, 1995). To

estimate the reliability of web services compositions,

Coppolino et al. (2007) generalize the Cardoso

proposal, covering all the generic workflow patterns

of van Der Aalst et al. (2003).

Within the WS-BPEL context, Mukherjee et al.

(2008) compute the reliability of WS-BPEL

processes taking into account most of the workflow

patterns that WS-BPEL can express, while the

method of Distefano et al. (2014) also incorporates

advanced composition features such as fault,

compensation, termination and event handling.

Using Unified Modeling Language (UML)

models, Rodrigues et al. (2012) annotate system

component interactions with their failure

probabilities. They convert them into a formal

executable specification, based on a probabilistic

process algebra description language, which are

executed on PRISM. This way, they can, for

instance, identify the components that have the

highest impact on the reliability system.

By focusing their work on BPMN, Respício and

Domingos (2015) calculate the reliability of BPMN

business processes by using the Stochastic

Workflow Reduction method of Cardoso (Cardoso,

2002; Cardoso et al., 2004). To meet this goal, they

extend BPMN with reliability information and they

identify the BPMN process blocks for which they

can apply one of the reduction rules.

The work we describe in this paper applies and

extends the proposals of Respício and Domingos

(2015) to evaluate the reliability of AAL processes.

Listing 1: BPMN extension for reliability - XML Schema.

<?xml version="1.0" encoding="UTF-8"?>

<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="http://.../relybpmn"

xmlns:bpmn=http://www.omg.org/spec/BPMN/20100524/MODEL

targetNamespace="http://.../relybpmn">

<xsd:import namespace="http://www.omg.org/spec/BPMN/20100524/MODEL"

schemaLocation="BPMN20.xsd"/>

<xsd:group name="relyBPMN">

<xsd:sequence>

<xsd:element name="ReliabilityInformation" type="tReliabilityInformation"

minOccurs="0" maxOccurs="1"/>

<xsd:element name="Probability" type="tProbability" minOccurs="0"

maxOccurs="1"/>

</xsd:sequence>

</xsd:group>

<xsd:complexType name="tReliabilityInformation" abstract="false">

<xsd:attribute name="requiredReliability" type="xsd:decimal"/>

<xsd:attribute name="calculatedReliability" type="xsd:decimal"/>

</xsd:complexType>

<xsd:complexType name="tProbability" abstract="false">

<xsd:attribute name="value" type="xsd:decimal"/>

</xsd:complexType>

</xsd:schema>

Evaluating the Reliability of Ambient-Assisted Living Business Processes

531

4 RELIABILITY INFORMATION

IN BPMN PROCESSES

To include reliability information in BPMN business

processes we use the extension, whose XML

Schema we present in Listing 1. The definition of

this extension is based on the work proposed by

Respício and Domingos (2015).

The extension has two elements. The first

element, named

ReliabilityInformation, has

two attributes: the

requiredReliability which

defines the minimum accepted reliability value for

the process or flow node, and the

calculatedReliability which is the reliability

of atomic activities and events (initialised with a

pre-determined value) or the reliability for

decomposable activities (sub-processes) and

processes computed using the SWR method of

Cardoso (2002).

The second element is the

Probability. The

probability value is used with conditional

SequenceFlow elements within conditional process

or loop process blocks and defines the probability of

the process execution path of taking them.

The reliability of processes is calculated with the

SWR method of Cardoso (it is similar for

decomposable activities). This method applies a set

of reduction rules to the process, iteratively, until

only one activity remains. The reliability of the

remaining activity corresponds to the reliability of

the process. Table 1 presents the application of the

six reduction rules of Cardoso to BPMN, identifying

the BPMN process blocks for which the reduction

rules can be used (Respício and Domingos, 2015).

As the AAL BPMN process subject of our study

Table 1: Reliability of Reduced Block (Respício and Domingos, 2015).

Initial Block Reduced Block Reliability of Reduced Block

Sequential



(



)

=

(



)

∗()

Parallel



(

1

)

=

(



)

1≤≤

Conditional



(

1

)

= 



()

1≤≤

Loop



(

′

)

(

1 − 

)

()

1 − ()



(

′

)

=()



Fault tolerant



(

1

)

∑

…

∑(



(∑





−



=1

)

∗



=0,1

=0,1

∏

1−



(

2



−1

)



(



)



=1

)

Network

(′) = (1)

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

532

also has events (see Figure 1), we use the same

reduction rules for process blocks composed by

events or activities, in an undifferentiated way.

In addition, when using reduction rules with

collaboration diagrams, they are applied to the

overall diagram by omitting pools and lanes.

However, to overcome the limitations of the block

structured approach of Cardoso, where one starting

point and one ending point are needed, we transform

the collaboration diagram by adding two new

gateways. To have a unique starting point, we add an

Exclusive Event-Based Gateway without any

incoming sequence flows and with one outgoing

sequence flow to each start event of the

collaboration diagram. Similarly, to have a unique

end point, we add an

Inclusive or Merge

Gateway with an incoming sequence flow from

each end event and without any outgoing sequence

flows (Ouyang et al., 2007).

5 RELIABILITY STUDY

This section presents a case study focusing on the

reliability evaluation of the AAL process presented

in section 2.

Initially, process designers set up the minimum

accepted values for the reliability of activities and

sub-processes (

requiredReliability). The

BPMN process model is then enriched, through the

relyBPMN extension, considering these values as

well as pre-estimated values of the attributes

calculatedReliability

(initialized with pre-

estimated values for atomic activities and events)

and

Probability. Then, the SWR algorithm

iteratively computes the

calculatedReliability

for sub-processes, reaching the reliability value for

the overall process (the collaboration diagram).

In the following, we describe the application of

this method to assess the reliability of the

collaboration diagram displayed in Figure 1,

considering different scenarios.

The experiment started by establishing a base

case scenario and computing the corresponding

reliability. After, a sensitivity analysis on the process

reliability was made. The objective of this analysis

was to evaluate the impact of changes in the

individual reliability of separate elements on the

reliability of the overall process. We made vary the

reliability of the following elements: each sensor,

the transmission from sensors to the BAN gateway,

and the transmission from the BAN gateway to the

HMS through the home gateway.

Parente et al. (2011) propose reliability values

for the type of sensors used in our use case, namely

the Heart Rate Monitor (HRM), the Pulse Oxymeter

(POxy), the Shygmomanometer (Shygm), and the

Accelerometer (Acc), which are used to initialise the

atribute

calculatedReliability of the tasks

“read value”.

Based on the measures of Baig et al. (2014), we

establish the reliability value associated to the

transmission from sensors to the BAN gateway,

which is used to initialise the

calculatedRelia-

bility

of the “receive value” tasks. For setting the

reliability value for the transmission from the BAN

gateway to the HMS, through the home gateway, we

consider both connections together to simplify the

study. This reliability value is used to initialise the

calculatedReliability of the task “receive

alarm” of the HMS.

The base case scenario, as illustrated in Table 2,

considers the values proposed for the reliability of

sensors (Parente et al., 2011); the value 0.992 for the

reliability of transmission from sensors to the BAN

gateway; and the value 0.99 for the reliability of

transmission from the BAN gateway to the HMS.

The

calculatedReliability attribute was set

to 1.0 for the remaining activities and events, such as

the process start, the evaluation of the received

values in the BAN gateway, and the “assist patient”

activity. In addition, the

requiredReliability

value for all process activities and events was set to

0.6, as this was assumed to be the minimum

acceptable reliability.

The reduction rule for the fault-tolerant gateway

considers four feasible combinations of receiving

two out of three signal devices: (HRM, POxy,

Shygm), (HRM, POxy), (HRM, Shygm), and (POxy,

Shygm).

Table 2: Reliability values for activities and transmissions

for the base case scenario.

Raw Reliability

BAN

devices

(sensors)

Sensor Sensors to

Gateway

BAN

Gateway

to HMS

HRM 0.8 0.992 0.99

POxy 0.7 0.992 0.99

Shygm 0.6 0.992 0.99

Acc 0.9 0.992 0.99

Overall

reliability

0.6901

For the base case scenario, the reliability of the

process takes the value 0.6901.

Evaluating the Reliability of Ambient-Assisted Living Business Processes

533

The study continued by making variations on

different reliability values and assessing the

resulting reliability of the global process. Figure 2

displays the results of this study. Chart (a) displays

the results of the variation of the Accelerometer

reliability in three scenarios. The base case scenario

corresponds to fix all the other values of the original

base case (Table 2) and making the reliability of the

accelerometer vary in the interval [0.6; 1], using

steps of 0.01. The worst case scenario differs by

setting the reliability values of the remaining sensors

to 0.6 (the minimum allowed value), while for the

best case the reliability of the other sensors was set

to 0.99 (considering an optimistic value). Chart (b)

shows the effects on the process reliability due to

variation of the HRM reliability considering the

same scenarios. As receiving (or not) information

from the other sensors in the fault tolerant pattern

has the same impact, this chart would be the same

for the sensors POxy and Shygm. Chart (c) displays

the impact of varying the reliability of transmission

from the sensors to the BAN gateway, for similar

scenarios – worst case (all the sensors’ reliability set

to the minimum 0.6), base case (all values set to the

base) and best case (all the sensors’ reliability set to

0.99). Finally, chart (d) discloses the dependence of

process reliability from the reliability of the BAN

gateway to the HMS transmission, using the

previous scenarios.

The results reveal that the reliability of the

process is mostly sensitive to reliability variations of

the transmission from the sensors to the BAN

gateway (chart (c)), then to variations of the

accelerometer reliability (chart (a)), to variations of

transmission from the BAN gateway to the HMS

(chart (d)), and, finally, to the reliability of a single

sensor (HRM, Pulse Oxy, Shygm) (chart (b)). The

analysis of scenarios for the different charts allows

concluding that the process reliability is more

sensitive to variations of the value under analysis in

the best case scenario and less sensitive in the worst

case scenario. Nevertheless, the process reliability is

insensitive to reliability variations of the sensors

HRM, POxy, and Shygm for the best case scenario.

The charts also allow identifying variation ranges

for reliability values of the different elements that

meet the required reliability for the overall process.

In addition, few conditions allow to reach an overall

reliability greater than 0.9 – if the transmission from

the sensors to the BAN gateway has a reliability of

at least 0.92.

Figure 2: - Impact on the process overall reliability due to varying separate reliabilities: a) variation of accelerometer

reliability (upper left); b) variation of HRM reliability (upper right); c) variation of sensors to BAN gateway transmission

reliability (lower left); variation of BAN gateway to HMS transmission reliability (lower right).

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534

6 CONCLUSIONS AND FUTURE

WORK

In this paper we presented a new approach to

calculate the overall reliability of a certain AAL

system and the way its components interact with

each other. We use a BPM approach to model these

interactions and to derive the combined reliability.

For this, we extend the BPMN language to include

reliability information for each process element and

use the SWR algorithm to calculate the overall

process reliability.

The study presented in section 5 exemplifies

how to proceed to assess different conditions of an

AAL BPMN process that involves AAL system

components. This assessment can be made at design

time to analyse the feasibility of the process, for

instance, if a minimum level of reliability is assured.

It allows to identify the elements which have the

highest impact on process reliability and, therefore,

to design the system architecture and set the

requirements for system elements.

Additionally, reliability can be computed at run

time to monitor process executions hence providing

an approach to identify low reliability services. In

that case, for instance the sensor timers could be

adjusted as well as the transmission rate increased at

run time. We intend to extend a Business Process

Management System (such as jBPM -

www.jbpm.org/), in order to include reliability

information in BPMN processes, as well as runtime

reliability monitoring features. These features can

then help health care professionals to better allocate

resources to provide the adequate care to certain

AAL-monitored patients, taking into account their

overall reliability.

ACKNOWLEDGEMENTS

This work is partially supported by National

Funding from FCT - Fundação para a Ciência e a

Tecnologia, under the projects

UID/MAT/04561/2013 and UID/CEC/00408/2013.

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