SAPA Technology: An AAL Architecture for Telemonitoring

Hubert Ngankam

, Maxime Lussier

2 b

, Aline Aboujaoud´e

2 c

, C´edric Demongivert

3 d

H´el`ene Pigot

, S´ebastien Gaboury

3 f

, Kevin Bouchard

3 g

, M´elanie Couture

4 h

, Nathalie Bier

2 i

and Sylvain Giroux

Laboratoire DOMUS, D´epartement d’Informatique, Universit´e de Sherbrooke, Sherbrooke, Canada

Centre de Recherche de l’Institut Universitaire de G´eriatrie de Montr´eal - CIUSSS-CSMTL,

Universit´e de Montr´eal, Montr´eal, Canada

LIARA Lab, Universit´e du Qu´ebec `a Chicoutimi, Chicoutimi, Canada

CREG

ES, CIUSSS West-Central Montreal, Cˆote-Saint-Luc, Canada

{nathlie.bier, aline.aboujaoude}@umontreal.ca, lussier.maxime@gmail.com,

{cedric.demongivert1, Sebastien

Gaboury, Kevin Bouchard}@uqac.ca

Keywords:

Ambient Assisted Living, Event-driven Architecture, Event Streaming, Apache Kafka, Spark, IoT, ADL,

Lambda Architecture.

Abstract:

Ambient Assisted Living (AAL) aims to allow frail older adults to stay safe at home, partly through remote

monitoring which offers clinicians a means to prevent and manage risks. AAL needs an architecture to support

the large set of data emanating from multiple sensors dispatched in several smart homes. These data must be

processed in real-time to take the appropriate decisions in time. In this article, we propose an Event-Driven

Architecture according to the publish-subscribe pattern. The proposed architecture is the core of our system,

named SAPA Technology. It is composed of three layers: data gathering, data ingestion, and data processing.

To ingest the data stream, we choose Apache Kafka, an open-source broker, and Apache Spark, a streaming

system to process the ingested data. The SAPA Technology architecture respects scalability, homogeneity, and

modularity. It supports at least thirty-eight smart homes.

1 INTRODUCTION

The aging of population in Canada and elsewhere in

the world changes the way social and health services

must be delivered (Organization, 2015). Only 10% of

Canadian over 65 years-old live in a retirement home,

or in a nursing home if their health status necessi-

tates more medical services (Roy et al., 2018). But

the number increases when incapacities and manage-

ment of multiple chronic disease necessitate moving

into a nursing home, or relying on increase home-care

https://orcid.org/0000-0002-7658-5303

https://orcid.org/0000-0000-0000-0000

https://orcid.org/0000-0001-6142-3611

https://orcid.org/0000-0001-8458-6536

https://orcid.org/0000-0001-5520-5677

https://orcid.org/0000-0001-7749-3470

https://orcid.org/0000-0002-5227-6602

https://orcid.org/0000-0002-0088-3865

https://orcid.org/0000-0002-2940-694X

https://orcid.org/0000-0003-0602-5957

services. Even in the context of disability, older adults

express the will to stay at home for as long as possible.

However, some diseases can lead to severe cognitive,

physical, and sensory deﬁcits such as neurodegener-

ative diseases, strokes, arthritis, or macular degener-

ation. These conditions can have signiﬁcant impacts

on functional independence, especially in Activities

of Daily Living (ADL). They may cause multiple risk

situations whether immediate, such as falls, or on the

long term, such as social isolation, malnutrition, or

poor hygiene (Boulos et al., 2017; Robinson, 2018).

Research into Ambient Assisted Living (AAL) fo-

cuses on automated methods or processes to support

safe performance in ADL, particularly for older adults

with multiple health conditions while being supported

by family, friends and medical staff (Rashidi and Mi-

hailidis, 2013). ALL involvea sensitive, adaptive, and

responsive software system to changes in the condi-

tions of the persons living in the smart homes.

One of the AAL system aims is to support the clin-

ical decision-making of clinicians delivering home

892

Ngankam, H., Lussier, M., Aboujaoudé, A., Demongivert, C., Pigot, H., Gaboury, S., Bouchard, K., Couture, M., Bier, N. and Giroux, S.

SAPA Technology: An AAL Architecture for Telemonitoring.

DOI: 10.5220/0010973400003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 892-898

ISBN: 978-989-758-552-4; ISSN: 2184-4305

care services (Lussier et al., 2020c; Lussier et al.,

2020a). To do this, the AAL system monitors ADL

performance and changes in habits and produce re-

ports to inform clinicians. Clinical decisions re-

lated to the services that should be delivered to the

older adults are then based on objective informa-

tion (Lussier et al., 2020c). Offering periodic reports

thus requires mechanisms for collecting and analyz-

ing real-time data coming from smart homes.

Usually, data collection is done using sensors,

connected objects and Internet of Things (IoT) de-

vices capable of responding to an environmentalstim-

ulus. Thanks to this equipment, monitoring (Ka-

nis et al., 2013; Zaric et al., 2015), context aware-

ness (Wood et al., 2008; Schilit et al., 1994) and activ-

ity recognition (Charlon et al., 2013;

Alvarez-Garc´ıa

et al., 2013) can offer real-time services. The con-

tinuous ﬂow of data emanating from the smart home

requires computer methods to process them within a

reasonable time frame. Above all, rapid data process-

ing provides to the clinicians means to take informed

decisions.

The great diversity of sensors and smart devices

available implies to set up an infrastructure that man-

ages heterogeneous data. Each device may contain

several sensors with one or more parameters, which

results in the creation of heterogeneous data. The

AAL architecture must then manage the real-time

streaming data emitted by these sensors as well as ap-

plications for data ingestion and processing to provide

appropriate services to the older adults and the clini-

cal staff.

To support the independence of older adults,

the health care system in Qu´ebec, Canada, has

implemented a program called SAPA (Soutien `a

l’Autonomie des Personnes

Ag´ees; Support for the

Autonomy of the Older Adults). SAPA Technology

is a project conducted by our team in close collabo-

ration with SAPA programs in Qu´ebec, that aims to

promote home care services for isolated older adults

with cognitive deﬁcits through the use of remote mon-

itoring of ADL. It aims to assess use cases and usabil-

ity of the monitoring system. Ultimately, the project

aims to support home care services in delivering the

right services, for the right person, at the right time.

This article mainly deals with the technological aspect

of the SAPA Technology project. It presents, through

software and hardware architecture, how remote mon-

itoring is constructed, to facilitate home care through

the analysis and treatment of ADL.

This article presents a scalable architecture opti-

mized for real-time and batch data processing. Based

on the Lambda architecture approach (Kiran et al.,

2015), the proposed architecture combines the capa-

bilities of collection, ingestion and batch and ﬂow

processing to recognize ADL, such as hygiene, meals

preparation, rest, outings and inactivity. Lambda ar-

chitecture is a data-processing architecture designed

to handle massive quantities of data by taking advan-

tage of both batch and stream-processing methods.

Driven by events, the proposed model subscribes to

IoT equipment events to offer to clinicians an ADL

analysis interface.

The remainder of the article is structured as fol-

lows: the clinical needs for telemonitoring are pre-

sented in Section 2; the SAPA Technology architec-

ture model based on Lambda architectures is pre-

sented in section 3; the tools used to implement the ar-

chitecture are presented in section 4; the whole SAPA

Technology system and some clinical elements are

discussed in Section 5; and, ﬁnally, our conclusions

are given in section 6.

2 CLINICAL NEEDS FOR

TELEMONITORING

AAL is a promising alternative to promote aging at

home and prevent risks. Remote monitoring offers

means to the clinicians to be warned when a haz-

ardous event occurs, and to determine social and med-

ical services needed when the health status of the

older adults evolves (Lussier et al., 2020b). Thanks

to the sensors scattered around the person’s home,

precise information about their everyday life routine

is collected. They can be combined with informa-

tion reported by the older adults themselves, and/or

their family, about how ADL are performed. Among

those, the most important ADL are meals preparation,

hygiene, sleep and outings (Rashidi and Mihailidis,

2013; Pollak and Perlick, 1991; Roy et al., 2016). The

global level of indoors activity complements these

ADL, since inactivity informs on the potential need

of interventions regarding a health condition or moti-

vation.

The expectations of the SAPA stakeholders taking

part in the study is to be able to better understand the

everyday life routine of the older adults with cogni-

tive deﬁcits and to obtain speciﬁc information about

some of the activities they are engaging in. To do so,

the SAPA Technology provides reports on the activ-

ities occurring at the older adults home to the clin-

ician who may then decide to modify, or not, their

intervention plan. Thereafter, the clinician can follow

the intervention plans’ implementation through regu-

lar reports offered by SAPA Technology (Figure 1).

This remote monitoring of ADL is based on an AAL

environment deployed at the older adults’ home and

SAPA Technology: An AAL Architecture for Telemonitoring

893

gathering continuous information about activities oc-

curring at home (i.e, meal preparation, hygiene, sleep,

outings and periods of inactivity).

Figure 1: SAPA Telemonitoring process.

3 ARCHITECTURAL MODEL

By their nature, AAL systems are real-time systems

that monitor the performance of ADL with the aim

to assist when needed. An AAL system therefore in-

volves the modeling of a real-time architecture.

This real-time prerequisite implies that the ambi-

ent sensors operate most often in event mode, namely

in publish-subscribe patterns. According to this pat-

tern, every detection of an action in the smart home

triggers a sensor event (Kenfack Ngankam et al.,

2020). An architecture that follows the publish-

subscribe pattern is named Event-DrivenArchitecture

(EDA) (Chandy, 2006). This approach contrasts with

the SOA architecture approach that gathers data for

responding to requests (Hanson, 2005).

The subscription model is better suited to push

events to consumers rather than to offer passive data

read mechanisms. With each signiﬁcant change in the

home, EDA promotes production, propagationand re-

action to events. Thus, an event architecture favours

responsiveness of the AAL system, as systems based

on events are designed purposely for unpredictable

and asynchronous environments (Hanson, 2005). Re-

leased from the data storage constraint imposed by re-

quests, an event architecture greatly contributes to the

scalability and adaptability of AAL systems. The re-

mainder of the section ﬁrst presents the criteria taken

into account for modeling the EDA and, thereafter,

the three components constituting the SAPA Technol-

ogy architecture: data gathering, data ingestion and

data processing.

3.1 EDA Requirements

The SAPA Technology architecture must deliver in-

formation to the clinicians, and assistance to the older

adults, on a real-time basis, but it must also guarantee

reliability and security for all the analyses and pro-

cesses realized. Therefore, the EDA must satisfy three

types of speciﬁcations:

1. Respond to the needs of ﬁnal users

(a) Adaptable to the different needs of the older

adults.

(b) Non-intrusive to facilitate the acceptability.

cessing capability.

2. Be devices independent

(a) Homogeneous to support different manufactur-

ers and protocols in the same network.

(b) Generic enough to be independent of commu-

nication technology.

3. Be extensible to integrate new services

(a) Modular, stand-alone and based on reusable

components.

(b) Scalable to allow new sensors and components

to be added or removed.

Lambda architecture is a data processing archi-

tecture, which takes the advantages of both batch

processing and stream-processing to handle a large

amount of data effectively (Kiran et al., 2015). The

SAPA Technology architecture is a model based on

Lambda architecture and consists of three layers. The

data gathering layer is fully distributed and dissemi-

nated in the environment. Data ingestion occurs both

at the edge and in the cloud to enable better interoper-

ability. In the third layer, processing and application

are performed in the cloud to beneﬁt from the perfor-

mance of compute tools.

3.2 Data Gathering

Sensors and IoT devices gather smart homes events

to identify ADL. The interfacing of speciﬁc sen-

sors/devices with the system is achieved by a local,

distributed and autonomous data collection compo-

nent. This component acts as an intermediate layer

aimed at clearly separating IoT devices from the rest

of the system. This separation makes it possible to

increase the modularity and to increase the weak cou-

pling between the components of the architecture, as

shown in Figure 2. To facilitate context awareness,

and promote the efﬁciency of exchanges, the privi-

leged communications are of the machine-to-machine

(M2M) type. The data collected by the sensors is

transmitted to a local processing unit and to a data

stream ingestion unit in real-time.

Smart CommuniCare 2022 - Special Session on Smart Living Environments to Support Aging-in-Place in Vulnerable Older Adults

894

Figure 2: SAPA Lambda Architecture.

3.3 Data Ingestion

We propose a heterogeneous data ingestion model, as

shown in Figure 2. The model can receive or extract

heterogeneous data from multiple sources and save it

in a uniﬁed format. Built to do data joins, aggrega-

tions, ﬁlters and transforms, the data ingestion model

is based on distributed event streaming. Event stream-

ing generates a continuous ﬂow and interpretation of

data to ensure that the right information is in the right

place at the right moment. Four strategies based on

high performance data pipelines are used to model the

unit of ingestion. They are high availability, scalabil-

ity, high throughput, and permanent storage.

In the SAPA Technology architecture, data is in-

gested from multiple different data sources at the

same time. Each source submits new tuples on a

stream, which are then received by the data ingestion

mechanism. This data ingestion primarily serves as

a mail queue. It routes the tuples to the correct des-

tination while continuously triggering the appropriate

Extract, Transform, and Load (ETL) process as new

data arrives. The publish and subscribe process that

analyses and transforms data ensures interoperability

and reusability. The ingestion of each event does not

require a response to the entity that delivered the data.

It is a one-way data pipeline.

3.4 Data Processing

As a Lambda architecture, the SAPA Technology ar-

chitecture was designed to ingest and process data in

real-time (Figure 2). The data processing unit is a

software component working either asynchronously

or synchronously. It detects independent incoming

events of different types and identiﬁes a high-level

event (e.g., ADL, by correlating these sensor events

with the others). In this sense, high-level events

can be deﬁned as the output generated after process-

ing many small IoT independent input data streams.

High-level events can be: sleep, rest, meal prepara-

tion, hygiene, outings and periods of inactivity (De-

mongivert et al., 2021; Demongivert et al., 2020).

Data coming from data ingestion is sent both to

the batch processing layer and the speed layer for pro-

cessing. Batch processing uses a batch query to gen-

erate analyses and to identify high-level events. The

main function of the batch layer is to use a histori-

cal archive to keep all the data collected. The speed

layer is similar to the batch layer. It computes also

similar analyses, except these analyses are realized in

real-time and concern only the most recent data. To

achieve this, it uses the queuing and streaming mech-

anisms. The ﬂexibility of such an architecture makes

it easy to adapt to the scalability of the older adult’s

needs.

4 IMPLEMENTATION

The implementation of the SAPA Technology archi-

tecture was primarily driven by the real-time nature

of IoT systems. It integrates hardware and software

components to support telemonitoring services. All

the sensors of the SAPA Technology architecture use

the Z-Wave and ZigBee communication protocols to

transmit data.

Mainly due to the event-driven nature of IoT sen-

sors, the implementation favored transmission in pub-

lish and subscribe mode. The publication and sub-

scription mechanism involves three types of compo-

nents: a client that sends messages, called a pub-

lisher, a second client that receives messages, called

SAPA Technology: An AAL Architecture for Telemonitoring

895

a subscriber, and a broker, responsible for manag-

ing the communication. MQTT (Message Queuing

Telemetry Transport) (Hunkeler et al., 2008) is a

lightweight network publish and subscribe protocol

based on the Transmission Control Protocol (TCP).

MQTT is open, simple, and easy to implement on de-

vices with limited resources, in environments such as

M2M communication and IoT. The MQTT broker is

the main component of the data collection unit. A hub

module is used as edge computing for local process-

ing and ambient decision making.

A streaming system needs a messaging infrastruc-

ture at the start of its pipeline to feed the system with

data. In the context of AAL systems, messages typi-

cally indicate the state of an IoT device at any given

time.

Apache Kafka (Kreps et al., 2011) is an open-

source message broker and highly scalable publish

and subscribe messaging system capable of handling

thousands of clients and hundreds of megabytes of

read and write per second. Kafka emphasizes high

speed messaging, scalability and sustainability. Kafka

supports both batch consumers which may be ofﬂine

and online consumers which require lowlatency. Well

suited to equipment, subject to strong resource con-

straints, Kafka can manage long message delays to

provide periodic system ingestion. It allows con-

sumers to replay messages as needed, it is capable

of queuing new tuples or pushing new ETLs to the

streaming processing unit. This functionality is im-

portant for our architecture that is shared by multiple

services. Kafka was chosen for these two reasons.

The automatic failover is realized by failure de-

tection and active node election mechanism imple-

mented on a distributed conﬁguration service with

Apache Zookeeper. Currently, in the SAPA Tech-

nology implementation, Kafka serves exclusively as

a message queue for individual tuples, each of which

is routed to the appropriate data ﬂow graph in the

streaming processing engine.

The Lambda architecture was proposed by Marz

and Warren (Warren and Marz, 2015) to provide a

scalable and fault-tolerant architecture for processing

real-time and historical data in an integrated manner.

It is the pillar of the SAPA Technology architecture to

analyze large amounts of data in an efﬁcient, fast, and

fault-tolerant manner.

The SAPA Technology architecture uses

Spark (Zaharia et al., 2010), a modern stream-

ing system offering highly scalable processing with

low latency. Spark provides data-driven processing,

batch processing, and streaming primitives, all of

which seem like a natural ﬁt for SAPA Technology.

Spark is specially designed for latency sensi-

tive applications that involve large volumes of data

streams with time stamps, such as trading systems,

fraud detection and surveillance applications. Spark

provides the ability to perform in-memory calcula-

tions using resilient distributed data sets (RDDs), al-

lowing it to provide faster compute times for iterative

applications. Spark streaming processes data streams

in micro-batches, where each batch contains a collec-

tion of events that occurred during the batch period

(regardless of when the data was created).

The SAPA Technology is currently installed in

thirty-eight smart homes. In each of these smart

homes, an older adult is followed by a clinician who

regularly carries out home care services follow-ups

to adapt the range of services needed. The SAPA

Technology has been validated and deployed in ac-

cordance with the Ministry of Health and Social Ser-

vices’ rules and regulations regarding data security

and protection. Each house contains an average of

thirty-eight connected devices. Typically, devices are

composed of one to six built-in sensors. The SAPA

Technology architecture currently supports more than

three thousand sensors and processes, eight hundred

raw events per minute for the detection of more than

twelve high-level events (rest, meal preparation, out-

ings, etc.) per minute (Figure 3).

5 DISCUSSION

The passage from a SOA architecture to a Lambda

architecture allows to process a large amount of data

coming from multiple smart homes. Therefore, the

analysis is made easily on real-time or on batch mode

to provide in depth analysis. The scalability con-

cerns the number of devices installed in the homes,

the number of smart homes and also the number of

clinicians who could observe the data. The SAPA

Technology architecture is also less prone to faults

as the archive offers means to recover false analy-

sis. The three layers architecture offers interoperabil-

ity as communications between the layers have been

judiciously chosen to ensure that various components

may communicate together through an intermediate

layer.

The SAPA Technology architecture offers modu-

larity thanks to the Kafka pipeline that enables ser-

vices to share data and the analysis they have made.

Clinicians are able to visualize ﬁve ADL on a secure

web platform. Regular reports are available to give

indicators on ADL to support their clinical decision-

making regarding services needed. Thanks to the

modularity, we also plan to add services to the older

adults in order to assist them and to provide a safe en-

Smart CommuniCare 2022 - Special Session on Smart Living Environments to Support Aging-in-Place in Vulnerable Older Adults

896

Figure 3: SAPA Lambda Architecture Performance.

vironment without changing the internal code struc-

ture or the underlying technology.

The deployment of the SAPA Technology in a

smart home necessitates installing the physical de-

vices at home and to linking them to the SAPA archi-

tecture. In our architecture, services for the installer

facilitate and shorten the time to deploy the sensors at

home.

6 CONCLUSION

This article has presented the architecture that pro-

vides remote monitoring in an AAL system for older

adults. This research on computer architecture is part

of a larger research program conducted by a transdis-

ciplinary team composed of computer scientists, oc-

cupational therapists, psychologists and designers. It

is the cornerstone of multiple services for older adults

and clinicians as well as for the research program.

Without a reliable architecture that allows to

gather accurate information and to provide appropri-

ate reasoning in real-time, clinicians and researchers

may not be able to conduct other research programs

and to develop services. This architecture is the result

of the transdisciplinary team as all the needs identi-

ﬁed by each discipline have contributed to elaborate

the speciﬁcations of the architecture to attain a com-

mon goal.

In a near future, we plan to add services to the

older adults, such as a calendar to remember appoint-

ments and planned activities. Thanks to SAPA Tech-

nology it will be easy to link the additional services

to the existing ones in order for the applications to

share the same data. To identify the ADL occurring

in the smart home, SAPA Technologies infer the ADL

recognition from rules and cross-referencing of event

data. It is planned to use data mining approaches,

which are available in Spark, to better follow the evo-

lution of the older adults autonomy.

ACKNOWLEDGEMENTS

Special thanks to the DOMUS laboratory develop-

ment team who spent several weeks developing and

testing this architecture, in particular Paul Guer-

lin - Yannick Drolet - Mauricio Chiazzaro - Math-

ieu Gagnon. The research is funded by the AGE-

WELL network and the Collaborative Health Re-

search Projects of Canada. Nathalie Bier is supported

by a salary award from the Fonds de la recherche du

Qu´ebec - Sant´e.

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