a systematic review on recent advances in mhealth systems...

Review ArticleA Systematic Review on Recent Advances in mHealth Systems:Deployment Architecture for Emergency Response

Enrique Gonzalez, Raul Peña, Alfonso Avila, Cesar Vargas-Rosales, andDavid Munoz-Rodriguez

Tecnologico de Monterrey, 64849 Monterrey, NL, Mexico

Correspondence should be addressed to Enrique Gonzalez; [email protected]

Received 4 November 2016; Revised 29 May 2017; Accepted 8 August 2017; Published 17 September 2017

Academic Editor: Denise Ferebee

Copyright © 2017 Enrique Gonzalez et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in anymedium, provided the original work is properly cited.

The continuous technological advances in favor of mHealth represent a key factor in the improvement of medical emergencyservices. This systematic review presents the identification, study, and classification of the most up-to-date approachessurrounding the deployment of architectures for mHealth. Our review includes 25 articles obtained from databases such as IEEEXplore, Scopus, SpringerLink, ScienceDirect, and SAGE. This review focused on studies addressing mHealth systems foroutdoor emergency situations. In 60% of the articles, the deployment architecture relied in the connective infrastructureassociated with emergent technologies such as cloud services, distributed services, Internet-of-things, machine-to-machine,vehicular ad hoc network, and service-oriented architecture. In 40% of the literature review, the deployment architecture formHealth considered traditional connective infrastructure. Only 20% of the studies implemented an energy consumptionprotocol to extend system lifetime. We concluded that there is a need for more integrated solutions specifically for outdoorscenarios. Energy consumption protocols are needed to be implemented and evaluated. Emergent connective technologies areredefining the information management and overcome traditional technologies.

1. Introduction

Efforts and developments for pervasive healthcare are grow-ing swiftly worldwide; this fact reveals that universities,researchers, and enterprises are aware of the need for func-tional and accurate systems that are able to satisfy theincreasing demand for a better medical attention infrastruc-ture [1]. Although healthcare monitoring systems are beinggradually adopted, their performance in clinical settings isstill controversial [2]. Despite this controversial performance,technology has changed the vision of how caregivers shouldreact under medical emergencies. Traffic accidents havebecome a major cause of death around the world, demandingbetter emergency care services. In 2012, the World HealthOrganization (WHO) estimated that traffic accidents werethe leading cause of death in people between the ages of 15and 29. The WHO also predicted that traffic accidents wouldbecome the seventh leading cause of death around the worldby 2030 [3]. Additionally, people suffering chronic and

degenerative diseases, especially elderly people [4], alsodemand better healthcare services. Diabetes, high blood pres-sure, and cardiovascular disorders are examples of chronicand degenerative diseases that limit people from doing theirdailyoutdoor activities.Therefore, the availabilityof improvedemergency and healthcare services is the technological chal-lenge that can let people to live in a safe and independent way.

Health monitoring is one of the ways to improve theemergency and healthcare services. Heath monitoringenables the early detection of diseases and the prompt medi-cal care under emergencies resulting in the reduction ofsuffering and medical costs. The use of sensors for monitor-ing and transmitting the patient’s vital signs is useful foridentification of patients under risk. The medical personnelcan determine the actions to safeguard the patient’s healthusing the sensor information. The delivery of opportunemedical care could be the difference between life and death.

Advances in mHealth services incorporate platformsbased on wireless body sensor networks (WBSN) as a central

HindawiJournal of Healthcare EngineeringVolume 2017, Article ID 9186270, 13 pageshttps://doi.org/10.1155/2017/9186270

https://doi.org/10.1155/2017/9186270

aid for remote reporting of data to a medical center undermedical emergencies. The WBSN collects data from bio-medical sensors and cameras using a user-friendly interface.The WBSN is also able to transmit images, physiologicalsignals, and video [5]. In [6], the authors present theEmergency Remote Pre-Hospital Assistance platform. Thisplatform enables medical surveillance in real time fromaccident location.

1.1. Emergency Situations. An emergency condition can becaused by multiple and different factors, such as naturaldisasters, human carelessness, explosions, traffic accidents,and degenerative diseases. On these events, early attentionis very important, also to have a plan of action, and, ofcourse, the caregiver capabilities. Once the caregivers arenotified about the emergency situation, the prehospitalassistance starts with the intention to control the emer-gency by stabilizing the patient for further transportationto a medical center.

Events resulting in an emergency condition are naturaldisasters, human carelessness, explosion, traffic accidents,degenerative diseases, and so forth. These events may bemay be multiple and different. Addressing these events alsorequire (1) early attention, (2) a plan of action, and (3) acapable caregiver.

After notifying the caregiver about the emergency, theprehospital assistance starts with the intention of stabilizingthe patient for further transportation to a medical center.

Providing first aid and requesting the assistance of aspecialist physician at the same time increases the chancesof a better outcome for patients in critical conditions. Todo this, the assistance request to the specialist physicianmust include an accurate overview of the patient. This

overview should be prepared by collecting and sendingpatient’s information in real time.

Under this emergency scenario, the incorporation of awireless body sensor network (WBSN) enables real-timemonitoring and results in delivering better and opportunisticmedical services. Nevertheless, developing this integratedsystem for emergency care becomes a challenging task. Manyfactors play important roles and the accurate design of thesystem could become the key to obtain the desired outcomes.This integrated system should be capable of handling thelarge amounts of data obtained in a very short time fromdifferent vital sign sensors. Common sensors collect vital signinformation such as heart rate, blood pressure, or electrocar-diography (ECG). Also, the accurate design of this integratedmHealth system should address energy consumption issuesin the network, the mobile computer, and the medicalsensors [7]. Other import issues are the design and selectionof the connectivity architecture and the communicationtechnologies [7]. Meeting quality of service (QoS) require-ments, in terms of the network parameters, ensures to haveinnovative, accurate, and cost-effective mHealth systems.Figure 1 illustrates an example of mHealth architecture andinfrastructure for emergency scenarios.

The aim of this paper is to systematically review thearchitectures, connective technologies, and energy optimiza-tion protocols implemented in the mHealth systems foremergency scenarios in outdoors in the last five years. Thereare several surveys and taxonomies about the deployedmHealth architectures for healthcare domain, and also, somestudies are targeted for emergency scenarios. However, to thebest of our knowledge, no systematic review has beenconducted to overview the different types of mHealtharchitectures currently being implemented for emergency

Cruz Roja MexicanaDistrito Federal

2nd tier3rd tier

1st tier

Zigbee/Bluetooth

Cellular networksGSM, 3G, LTE, 5G

Physiological sensors

Environmental sensors

Video technology

GPS Local servers

Medical centerEmergency center

Remote serversDoctor’s PDAAmbulanceCaretaker site

Internet

VANET

Multiple BANSnetworking

Cloud services

IoT

PSI

(i)

(i)(ii)

(iii)

(ii)

Wi-Fi, WiMAX

Figure 1: Conceptual mHealth system architecture for emergency situation in outdoors.

2 Journal of Healthcare Engineering

scenarios in outdoors. Moreover, our systematic review seeksfor the efforts surrounding the mHealth systems to attend thepatient in emergency situations in terms of architecture inte-gration, communication, and energy optimization proposals.In that manner, this review collects valuable informationabout the following: (1) mHealth third-tier architecture foremergency scenarios in outdoors, (2) the traditional andemergent infrastructure connectivity technology betweenthe three tiers, and (3) energy optimization protocolsemployed to increase the lifetime of the mHealth systems.The study concludes by discussing the future directions inthe field.

2. Materials and Methods

This examination was conducted in September 2016–April2017. The main goal was to find, study, and evaluate therecent efforts related to the infrastructure and architecturein favor of mHealth systems. This objective was fulfilled viathe Internet by selecting the following databases: IEEEXplore, Scopus, SpringerLink, ScienceDirect, and SAGE.The keywords used (employing Boolean phrases) in thesearching were as follows: mobile health, m-health, mHealth,remote health, ubiquitous health, mobile healthcare, perva-sive health, architecture, infrastructure, and emergency.Considering our interest to provide an analysis of novel pro-ductions, articles published after 2012 were considered. Weincluded exclusively productions published in English andresearch from peer-reviewed journals. During the first stage,duplications were excluded and papers were analyzed bytheir title and abstract seeking for a match with our scopeof interest. On a second stage, the remaining productionswere subjected to a full-text review.

The initial query displayed 5801 results. After the firststage (title and abstract evaluation and elimination of dupli-cates), the number was reduced to 366. Once initiated thefull-text review, we aimed our analysis in publicationsaddressing the remote tracking of medical emergencies inoutdoor scenarios, considering the importance previouslydescribed about traffic accidents or sudden-onset emergen-cies as a consequence of chronic degenerative assistance.The inclusion criteria were performed as follows: (i) articlesincluding emergency events handling (e.g., wireless transmis-sion of patient’s vital signs in real time, alarm systems,emergency services automatic request, and patient’s locationvia messages or calls); (ii) the study includes system andprototype developments, evaluation or validation of algo-rithms, protocols, models, and systems; (iii) the study was apeer-reviewed article published in journals; (iv) articleswhere approaches considered collecting data for at least onewireless sensor; (v) studies displaying evidence in the use ofwireless technologies, that means, their infrastructure andarchitecture included two or more tiers; and (vi) articles werewritten in English language. In the course of full-text reviewexclusion criteria was determined by (i) articles derived fromposters, glossaries, conferences, congresses, meeting, frontcovers, and book chapters; (ii) articles not written in Englishlanguage; (iii) articles not supporting emergency situations;(iv) articles considering emergency management for hospital

scenario; and (v) studies where the architecture for mHealthservices included just organizational and conceptual models(lack of infrastructure in the deployment). All articles wereevaluated looking for real evidence in the outcomes pre-sented. After two stages of evaluation process, 25 articleswere selected for the present analysis.

3. Results

Inour review,wedetectedonly5 (20%)articles that are specificfor outdoor environments. The other reviewed architectures20 (80%) were targeted for indoors and outdoors. After theanalysis of the selected articles,we extracted themain informa-tion of the recent architectures used for mHealth monitoringin emergency situations. Table 1 summarizes the extractedinformation of the selected studies. The following sectionspresent the results of (1) demographic data extracted fromthe articles, (2) the sensor, mobile, local, and remote unitsfor the existing m-Health architectures in emergency scenar-ios, (3) the infrastructure connectivity between the three-tiers, and (4) the current energy optimization deployments.

3.1. Demographics. This section reports demographic dataextracted from the 27 selected articles. The articles were from20 different journals; the most common journal was Interna-tional Journal of Distributed Sensor Networks 4 (16%).

Figure 2 presents the frequency of the articles per yearand per continent. As the data sample range was too short;it was not possible to consider possible trends. However, itwas important to report that most of the selected articles werepublished in 2016. The graph also illustrates that most of thereviewed mHealth systems were from Europe 11 (44%),followed by Asia 10 (40%), North America 2 (8%), andAustralia with 2 (8%) studies reported.

3.2. mHealth Architecture for Emergency Scenarios. Thetaxonomy of mHealth system architecture for emergencyscenarios in outdoors is focused on the three tier structure:intra-WBSN communication for the first tier, inter-WBSNcommunication for the second tier, and beyond-WBSN com-munication for third tier.

3.2.1. First Tier: Sensor Units. The first tier considers theimplementation of a body sensor network (BSN). The sen-sors have a direct communication with the personal server(PS) implemented in the second tier. The main sensor unitsfor mHealth systems in emergency scenarios in outdoors cor-respond to physiological and environmental sensors. Theselected studies used a diverse type of physiological data tomonitor the internal condition of the person; however,authors in [18] did not report the acquisition of physiologicalsignals into the mHealth system architecture. The physiolog-ical signal most commonly found was electrocardiography(ECG). It was also common to find environmental sensorsto monitor ambient conditions at the emergency location.The reported ambient conditions were temperature, light,noise, and humidity [10, 11, 20, 24]. In terms of sensor datasampling, authors in [8] reported AN error-free transmissionof biomedical signals. An adaptive dithered signed-errorconstant modulus algorithm was implemented into the

3Journal of Healthcare Engineering

Table1:Summaryof

therecent

architecturesform-H

ealth

inem

ergencysituations.

Ref.

Emergencycontext

Target

loc.

1sttier

2ndtier

3rdtier

Energyop

t.Infras.

conn

ec.

Typeof

dev.

Sensors

Com

.tech.

Mobile

unit

Com

.tech.

Localserv.

Rem

.serv.

[8]

Directtransm

ission

ofECG

throughmobile

network

where

thenearestmedical

center

canbe

contacted,

and

byGPS,thepatientcanbe

foun

dandsaved.

Outdo

ors

ECG

EEG

Bluetooth

orZigbee

Smartpho

newithGPS

GSM

orEDGE/

Bluetooth

orWi-Fi

Basestation

NetworkedPC

→Internet

→healthcare

station

—Tradition

alAlgorithm

evaluation

[9]

Aperson

alem

ergency

respon

sesystem

withan

automaticdetectionon

falls

tosend

analarm

tothe

caregivers.

Indo

ors/

outdoors

ACC

Bluetooth

Smartpho

newithinternal

ACC,G

PS,

andGUI

——

—

Can

beredu

cethetransm

ission

1st-2n

dtiers.1st

detectsfalls

insteadof

2nd.

Tradition

alSystem

reliability

[10]

Abn

ormalmedicalparameters

aresent

immediatelyto

medicalfacilitiesb

yreserving

priorityslotstorepo

rtthem

inrealtime.

Indo

ors/

outdoors

Phys.

Environ

.PAN

coord

Bluetooth

orZigbee

Smarthpo

neor

tablet

withGPS

andGUI

GSM

,3G,or4G

→Ambu

lance

→VANET

Hospital

server

LAN,W

LAN

→Rem

ote

database

orDoctor’sPDA

Pow

ermanage-

mentmecha-

nism

.Efficient

MACprotocol

tocontrol

sensor’sactivity

VANET

Mod

elprop

osal

and

algorithm

evaluation

[11]

Multiplevideos

andmedical

data

aremultiplexed

and

transm

ittedin

realtimeto

the

hospital.M

edicalstaff

can

coordinate,visualize,and

prearrange

treatm

ent.

Indo

ors/

outdoors

Ambient

videos

—

Ambu

lance

withcam-

eras,G

PS,

USG

,and

vitalsigns

LTE

Hospital

center

——

Tradition

alAlgorithm

evaluation

[12]

Emergencymedical

inform

ationistransm

itted

directlythroughacellu

lar

networkwitho

utmultiho

pcommun

ications.Itiscritical

toallocateadedicated

channeltothebase

station.

Indo

ors/

outdoors

EEG

ACC

ECG

Tem

p.BP

EMG

Glucose

Bluetooth

Smartpho

neCellularnetwork

orWi-Fi

Basestation

AP →

Internet

→Doctor’sPDA

Determine

dynamicallythe

numberof

chan-

nelsto

efficiently

utilize

the

resourcesof

the

network

Tradition

alEnergy

scheme

evaluation

[13]

Patient’svitalsigns

aretrans-

mittedsimultaneou

slydu

ring

adisaster

tothemedicalcen-

terforfastanalysisand

diagno

sis.

Indo

ors/

outdoors

HR

RR

BP

SpO2

Zigbee

Smartpho

neLT

Eor

Wi-Fi

Basestation

AP →

Internet

→Emergency

center

NDN

redu

ceup

stream

traffi

cto

efficientlyuti-

lizetheresources

ofthenetwork

Cloud

services

Protocol

network

evaluation


Table1:Con

tinu

ed.

Ref.

Emergencycontext

Target

loc.

1sttier

2ndtier

3rdtier

Energyop

t.Infras.

conn

ec.

Typeof

dev.

Sensors

Com

.tech.

Mobile

unit

Com

.tech.

Localserv.

Rem

.serv.

[14]

Intheho

spitalem

ergency

room

,the

BANpatientd

atais

displayedin

realtimeto

the

medicalcoordinator.Patient

may

betransferredto

further

treatm

ent.

Indo

ors/

outdoors

EEG

Positioning

ACC

SpO2

EMG

Glucose

BP

Hearing

MACIEEE

802.15.4

BAN

coordinator

Internet

Database

server

Emergency

ambu

lance

device

pro-

vidersystem

Energy-aw

are

peeringrouting

protocol

Centralized

anddistrib-

uted

services

Rou

ting

protocol

implem

ent.

[15]

Wearablelight

device

(WLD

)detectsan

abno

rmalevent.

Mobile

phon

erepo

rtsan

alarm

signalto

caretaker

witho

utuser

interaction.

The

caretakercouldrequ

est

furthermeasurements.

Indo

ors/

outdoors

ECG

SpO2

PPG

Tem

p.ACC

WLD

coord.

Videoconf.

GPS

Bluetooth

Smartpho

ne—

Caretaker

site

—

Onlytransm

its

alarmsto

redu

cedata

transm

ission

Tradition

alSystem

dev.

[16]

Inaweb

portalto

real-tim

emon

itoringof

health

status

ofelderly,theirfamilies

orclini-

cianscanvisualizeaperson

’slocation

andstatus

ofalarm

ofabno

rmalph

ysiological

parameters.

Indo

ors/

outdoors

ECG

Tem

p.ACC

Glucose

EMG

SpO2

Arduino

coord

Bluetooth

Smartpho

newithGPS

andRFID

tag

Wi-Fi

orLT

E—

Web

services

→Health

care

managem

ent

system

Fuzzylogicto

decide

events

andminim

ize

commun

ication

resources

Internet-of-

things

System

implem

ent.

[17]

Heartratesignalsare

availablethroughPSI

network.Alarm

isgenerated

andanearby

hospitalisno

ti-

fied.T

hesystem

provides

accessto

EHR.

Indo

ors/

outdoors

ECG

Arduino

coord

Zigbeeor

Bluetooth

orWi-Fi

Smartpho

nePSI

Storageand

visual.

device

——

Distributed

services

Con

ceptual

andproto-

type

implem

ent.

[18]

Ifthem-C

arer

system

detects

acriticalsituation,

itsend

sa

message

tothelocation

server

andto

theem

ergencyservice

toinform

thelocation

ofthe

patient.

Outdo

ors

——

Smartpho

newithGPS

Wi-Fi

or3G

Emergency

service

Location

and

preference

servers

→Internet

→Doctors,

relatives

—Tradition

alSystem

dev.


Table1:Con

tinu

ed.

Ref.

Emergencycontext

Target

loc.

1sttier

2ndtier

3rdtier

Energyop

t.Infras.

conn

ec.

Typeof

dev.

Sensors

Com

.tech.

Mobile

unit

Com

.tech.

Localserv.

Rem

.serv.

[19]

Ifan

abno

rmalcond

itionis

detected

such

ascritical

cardiacevent,patientsare

referred

totheem

ergency

department.

Indo

ors/

outdoors

ECG

Bluetooth

Smartpho

ne3G

orWi-Fi

Clin

ical

decision

supp

ort

NetworkedPC

→Internet

→Doctor’sPDA

BATCH

trans-

mission

toim

provebattery

duration

versus

greedy

transm

is-

sion

node

Tradition

alSystem

dev.

[20]

CARAsystem

reason

ingis

capableto

predictpo

ssibly

riskysituations

bycorrelating

differentsensor

readings

over

timeandto

notify

the

emergency.

Indo

ors/

outdoors

ECG

RR

HR

BP

ACC

Environ

.

Bluetooth

Smartpho

ne—

—

Web

services

→Internet

→Emergency

services,

caregivers

—Cloud

services

System

implem

ent.

[21]

After

detectionof

afallor

cardiacanom

aly,alertenabler

system

automaticallycontact

patient’s

family

orem

ergency

services.

Outdo

ors

Biomedical

Bluetooth

Smartpho

newithGPS

3Gor

Wi-Fi

Local

database

Rem

ote

database

—Tradition

alApp

dev.

[22]

The

system

mon

itors

continuo

uslythepatient’s

health

cond

ition,

andifan

emergencysituationis

detected,a

requ

estfor

ambu

lanceissent.

Outdo

ors

ECG

EEG

Bluetooth

Tablet

3G,4Gor

Wi-Fi

—

Web

services

→Internet

→Doctor’s

device

Energyand

resource-aware

mon

itoring

Cloud

services

Mod

elevaluation

and

implem

ent.

[23]

Asystem

allowingreal-tim

eanalysisof

sensors’data,

providingguidance

and

feedback

totheuser.T

his

approach

generateswarnings

basedon

theuser’sstate,level

ofactivity,and

environm

ental

cond

itions

relatedto

patients.

Indo

ors/

outdoors

BP

HR

ECG

EKG

Tem

p.Video

Aud

ioGPS

M2M

Area

network

Mobile

devices,

M2M

gateway

M2M

Com

mun

ication

techno

logy

(Internet)

Database

server

Doctor’sPDA,

insurance

companies.

Optim

ization

schemefor

movem

ent

coordination

techniqu

eand

data

routing

withinthe

mon

itored

area.

M2M

Movem

ent

tracking

algorithm

evaluation

[24]

Sportsenvironm

entmon

itor-

ingsystem

.Analarm

issent

ifanyhazardou

scond

ition

appearsor

anyof

thedefined

user

upperor

lower

thresh-

olds

aresurpassed.

Outdo

ors

HR

RR

ACC

Tem

pEnviron

.

Bluetooth

Smartwatch

orsm

artpho

ne

Internet(tech-

nology

not

specified)

Emergency

services

——

Distributed

services

Wireless

sensor

network

app

develop.


Table1:Con

tinu

ed.

Ref.

Emergencycontext

Target

loc.

1sttier

2ndtier

3rdtier

Energyop

t.Infras.

conn

ec.

Typeof

dev.

Sensors

Com

.tech.

Mobile

unit

Com

.tech.

Localserv.

Rem

.serv.

[25]

Asystem

isdeveloped

throughaprogram

runn

ing

onasm

artpho

neintegrated

withBluetooth.T

hesm

art-

phon

eisableto

receivedata

from

sensorsin

orderto

detectfallandissuealarm.

Indo

ors/

outdoors

ACC

Gyroscope

GPS

Bluetooth

Smartpho

ne3G

/GSM

Family

and

Health

care

providers

Efficient

learning

algorithm

with

thehighest

accuracy

runn

ingon

acell

phon

e

Tradition

alSystem

develop.

[26]

Acontext-aw

aresm

artsolu-

tion

ableto

trackanddeter-

minedisabled

user’slocation

.Iftheuser

ishaving

acritical

health

issue,amessage

will

besent

toacaregiver.

Indo

ors/

outdoors

RFID

tags

(for

user’s

movem

ents)

GCS

BS

Video

Hearing

RFID/

Wi-Fi

APs

Smartpho

neWi-Fi

Database

server

Caregiver’s

PDA/

smartpho

ne

Use

ofRFID

techno

logy

Tradition

alSystem

develop.

[27]

Apervasivehealth

system

enablin

gself-managem

entof

chronicpatientsdu

ring

their

everyday

activities.

Indo

ors/

outdoors

HR

SpO2

BP

Weight

Bluetooth

Smartpho

ne(m

obile

base

unit)

3G/4G

HTTP

—

Medicalcenter

Web-based

microblogging

services

(Twitter)

—SO

ASystem

develop.

[28]

Abn

ormaldetectionwhilea

heartattack,the

smartpho

nesend

sthedata

alon

gwiththe

patient’s

location

tothe

remoteclou

d.Adecision

supp

ortsystem

notifies

entrustedem

ergencycenters.

Indo

ors/

outdoors

EEG

Bluetooth

Smartpho

newithmicro-

phon

eand

GPS

UWB,R

SSIon

WLA

N,L

BS,

Wi-Fi,4G,and

/or

RFID

—

Web

services

→Emergency

services,

doctors

—Cloud

services

System

evaluation

[29]

Apatientsufferingfrom

CHF

iscontinuo

uslymon

itored.

The

clou

dservices

generate

alarmsto

ERservices

when

abno

rmalitiesaredetected.

Indo

ors/

outdoors

Glucometer

CO2level

SpO2

ECG

Bluetooth

Tabletor

smartpho

newithGPS

Wi-Fi,L

TE

—

Web

services

→MC,d

octors,

ambu

lance

Afuzzy-based

data

fusion

tech-

niqu

eto

remove

redu

ndantdata

IoTand

clou

dservices

System

evaluation

[30]

Inasm

artfalldetection

system

,the

patientm

onitored

formobilityandfalleventsare

repo

rted

asalarms.

Indo

ors/

outdoors

ACC

Bluetooth

Smartpho

neCellularnetwork

—Web

services

—IoTand

clou

dservices

Algorithm

evaluation


Table1:Con

tinu

ed.

Ref.

Emergencycontext

Target

loc.

1sttier

2ndtier

3rdtier

Energyop

t.Infras.

conn

ec.

Typeof

dev.

Sensors

Com

.tech.

Mobile

unit

Com

.tech.

Localserv.

Rem

.serv.

[31]

Emergencyisdetected

and

notified

byapprop

riatering-

tone

orvibrationpatterns

inthesm

artpho

ne.A

lso,alerts

aresent

totherescuers.

Indo

ors/

outdoors

PPG

Bluetooth

Smartpho

neWi-Fi,2G,3G,

4G—

Web

services

→Doctors,u

sers

EfficientBlue-

toothlowenergy

protocol

stack

IoTand

clou

dservices

Prototype

implem

ent.

[32]

Differentlevelsof

attention

butthepatientismon

itored

continuo

usly,and

inmobility,

itcanrespon

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communication link to obtain a high correlation betweenoriginal and transmitted data samples.

3.2.2. Second Tier: Mobile Unit. The second tier considers theimplementation of a PS (mobile unit). The PS has a directcommunication with the database servers implemented inthe third tier. Smartphones and tablets are the most commonunits used for mHealth systems. The main built-in technolo-gies used by the smartphone users are the video and theglobal positioning system (GPS) technologies. The use ofGPS technology is fundamental to locate the person withthe emergency situation in outdoors. In [26], a smart andunified interface based on context-aware and adaptive inter-face is set for people with disabilities. RFID tags and Wi-Finetworks are used to track and determine the location of peo-ple with disabilities. In [28], the GPS is used for outdoor posi-tioning of the patient; but for indoor positioning, a receivedsignal strength indication (RSSI) is implemented into theWLAN. The framework switches between indoor andoutdoor localization via one transmitted bit according tothe mobility of the person.

The use of video technology is valuable for visual moni-toring the scenario of the emergency in real time. In [11],video streams of multiple cameras were collected and multi-plexed with other medical information. Ambient videos werecollected and transmitted as the ambulance reaches theemergency area. While ambulance reaches emergency area,ambient videos were collected and transmitted. Inside theambulance, paramedics can create videos about the conditionof the patient; the specialized medical staff can later visualizethe emergency situation. In [15], cameras made possible theinteraction with the elders within ambient-assisted livingenvironments. In [23], a wireless multimedia sensor networkusing a video recorder is applied in order to track patient’sactivity and medical condition. In [26], the authors use videotechnology with the intention to record disabled people inhis/her working places. The analysis of these recorded videosfacilitates the guidance and assistance of disabled people.

3.2.3. Third Tier: Local and Remote Servers. The third tierconsiders the implementation of database servers in order

to store, manage, and visualize the patient data. The mainunits used are local and remote servers. The most commondeployments for mHealth systems for emergency scenariosin outdoors include the communication with the local serverand the communication with the remote servers via theInternet. In [11, 15, 17, 24], the physiological and environ-mental data are only stored and visualized at the followinglocal databases: monitoring center, hospital, caretaker site,and emergency center. In [16, 20, 22, 25, 27–31], the data isdirectly transmitted and managed by web services; in thisway, the doctors, users, caretakers, and family receive alertsin case of emergency situations.

3.3. Infrastructure Connectivity. Infrastructure connectivityrefers to the communication technology implementedbetween each tier of the mHealth system. The taxonomy pro-posed for this category includes the traditional connectivetechnology and the emergent connective technology.

3.3.1. Traditional Connective Technology. The traditionalconnective technology considers the available communica-tion services to manage and transmit the information. Eitherwired or wireless sensors transmit the physiological informa-tion to a mobile device via Bluetooth or Zigbee, the mobiledevice sends the data through a cellular network or a localarea network services, and finally, the data is managed, visu-alized, and stored into a local or a remote server.

3.3.2. Emergent Connective Technologies. Nowadays, theinformation is being managed in different manners, not onlyvia one-way traditional services. The emergent Internetconnective technologies consider other types of manners totransmit, process, and manage the data, such as cloudservices, Internet-of-things (IoT), distributed services,machine-to-machine (M2M), vehicular ad hoc network(VANET), and service-oriented architectures (SOA).

The most common implemented connective technologyfor mHealth systems in emergency scenarios in outdoors isthe cloud services. In [13], a named data networking technol-ogy was implemented to deliver rich media contents fromcloud, such as healthcare video adaptive streaming. In [20],

201420132012 20162015 2017

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Figure 2: Frequency of the selected articles per year and per continent.


cloud computing was used to provide the system with sensordata management and remote monitoring services. Cloudenvironment could provide a solution for reasoning compo-nent decisions and data mining. In [22], cloud services sup-port a range of capabilities of storing, processing, andnetworking. Cloud data offers the possibility to scale up thesystemwith the number ofmonitored patients, increasing sizeof data and interconnected components. In [28], an emergingcloud-supported cyber-physical system (CCPS) computingparadigm is implemented. The CCPS and smartphone inte-gration solves many challenges regarding localization; withthe use of the cyberspace, the complex processing task oflocalization could be done in real-time and efficient manner.

The combination of cloud services and IoT connectivetechnologies is another alternative for mHealth systems. In[29–31], the synergy of IoT and cloud computing (CC) isimplemented to obtain the sensor data of the IoT subsystemand use it by the cloud to analyze, process, and classify thesephysiological information in order to improve the accuracy,efficiency, and reliability of the framework. Additionally,the only use of IoT technology is reported in [16]. IoTenabled intelligence capabilities for real-time monitoringuninterruptedly, allowing emergencies to be detected imme-diately. The physical devices and patients became virtualentities and can be monitored from web services. In termsof distributed services such as connective technology, theimplementation of a publish/subscribe internetworking(PSI) is reported in [17]. The network is based on the pub-lish/subscribe paradigm: publishers providing information,subscribers consuming specific information, and an eventnotification service matching the advertised information.The PSI technology is introduced in assistive environmentsand used for an emergency event. The PSI publishes and con-trols patient data, a notification message advertises the data,and the healthcare unit examines and subscribes for the spe-cific data of the emergency. In [26], a middleware distributedservice architecture is implemented as a tool to abstract thehardware features and protocols from high-level layers dueto the inexistence of devices and platforms standardization.In [14], the implementation of centralized and distributedservices is reported. In this architecture, the connectivity isadaptable. In centralized mode, the WBSN only connectswith the healthcare station. In distributed mode, the WBSNconnects with the healthcare station and also sends the datato a medical display coordinator which visualize the informa-tion. Another important emergent technology is the imple-mentation of M2M infrastructure. This technology offers aconvergence between objects and intelligent services. Anoptimized scheme for movement coordination and datarouting technique are introduced in [23]. The schemeincludes a routing framework where several sensor devicescan relay data to all receivers interested in that data. Finally,in [32], a hybrid Wi-Fi peer-to-peer (P2P) architecture isimplemented to provide a high-quality health service thatconsiders the mobility of the user and the optimization ofthe M2M data traffic via a dispersed cross-layer algorithmthat optimizes the TCP/IP stack. In terms of VANET, themobile network was used inside the ambulance to supportmobile connectivity between the hospital and the ambulance

in case of critical cases [10]. Finally, a SOA connective tech-nology is applied in [27] to deal with communication issuesand extend interoperability and usability including micro-blogging services (Twitter) whereby patient’s communitycould be informed of patient’s medical status.

3.4. Energy Consumption and mHealth SystemOptimizations. It is important to take into account the life-time of the mHealth systems especially when these systemsare intended for emergency scenarios, in which the datatransmission is demanding, in real time and critical. More-over, for outdoor environments, the mobility of the platformsprevents the components of the system (sensors, smart-phones) to be energized at any time, so the lifetime of the sys-tem must be sustainable.

3.4.1. Implemented Energy Consumption Protocols. Althoughenergy optimization procedures were proposed for increas-ing the lifetime of the mHealth systems in emergency scenar-ios in outdoors, few studies implemented an energyconsumption protocol. In [12], a lifetime optimization algo-rithm was implemented to maximize the minimum lifetimeof each sensor in the WBSN. The disadvantage reported isthat in case of an emergency the WBSN information is trans-mitted regardless of the energy consumption. In [13], anenergy consumption awareness is evaluated in for LTE andWi-Fi technologies. In addition, the implementation ofNDN technology saves energy and other resources in the net-work. In [14], an energy-aware peering routing protocol(EPR) is used to reduce the traffic load and energy consump-tion choosing the nearest node available. The drawback is theimplementation of the EPR as an indoor scenario. In [29], afuzzy-based data fusion technique is implemented in orderto save bandwidth. The method reduces the transmissionand processing of the sensed data to remove redundant infor-mation from the sensors, thus increasing the networklifetime. In [32], a dispersed cross-layer optimization algo-rithm is used in order to improve the efficiency and reliabilityof the network. One drawback is that each wireless deviceneeds to interoperate and the algorithm should differdepending on the network environment, but this could besolved with a dynamic configuration for the MAC layer.

3.4.2. Not Implemented Energy Consumption Protocols. Theother studies only suggested that some actions could be takento improve battery lifetime. In terms of minor data transmis-sion, [9, 15, 16, 25, 31] reported that only the relevant infor-mation such as alarms could be transmitted. It could reducethe amount of data transmission between the sensors andthe mobile devices, resulting in battery savings. In terms ofcontrolling the idle state of the transmitter, [10, 19]reported that a protocol could turn-off the sensor nodewhile inactive and/or maintain the transmitter idle whenno data is available. It could save the battery consumption.In terms of efficient communication protocol, [31] men-tioned the opportunity to use a simplified protocol stackinto the Bluetooth interface. In terms of energy-awaremonitoring, [22] considers the network resources, memoryavailability, processing computation, and battery drainage.


Finally, [23] proposes an energy-aware routing techniquefor data transmission allowing to choose a path with max-imal residual energy and at a time the path with minimalenergy consumption.

4. Discussion

Architecture and infrastructure for mHealth services varydepending on the different ways to deploy a wireless sensornetwork for emergency scenarios in outdoors. After analyz-ing 25 articles, our review process pointed out that the major-ity of the reviewed mHealth systems were intended forpatients suffering from either cardiac, epilepsy, dementia, orparalysis disease. Also, the elderly was the predominant pop-ulation, and the athletes and disabled people represented thesecondary population.

In the majority of the revised articles, 80%, the mHealthsystems were intended for indoors and outdoors; however,the deployment and evaluation of these systems were onlyfor indoor scenarios. This finding highlights the need to showreal evidence of mHealth systems supporting emergencies inoutdoors scenarios. The need of mhealth systems deployedfor outdoors along with two key factors makes evident ourdecision to highlight how wireless body sensor networks(WBSN) are handling emergency events. The key factorsare the need for emergency care for patients within thegolden hour and the previously stated increment in the rateof traffic accidents [23]. Our review made also possible theidentification of the following important facts related to theuse of WBSN for emergency care in outdoors scenarios. Interms of sensor units, there is a large amount of data gener-ated due to the send-data frequency under emergency eventsand the diversity of the acquired signals: physiological andenvironmental. It is reported that under emergency situa-tions; the personal health information is updated, at least,every 10 seconds [5]. To handle this situation, sensors shouldbe categorized by priority according to the critical conditionsuffered by patients (heart attack, stroke, respiratory arrest,etc.). Accurate scheduling techniques should be part of therouting protocols at the system’s medium access control layerto avoid data loss due to several sensors trying to access thecommunication channel at the same time. Possible tech-niques identified are smart energy-aware sensors that areonly activated at specific time slots; intelligent algorithmsthat take into account conditions like abnormal data, bufferspace remaining, energy constraints, and physiologicalparameter priorities [33]; and fuzzy logic algorithms thatdecide the best time to start a data transmission [34]. Interms of video technology, physicians are more comfortableif they can visualize patient’s condition. The use of videostreams is a strong tool to help physicians for an accuratediagnosis. Factors such as patient’s coloring, muscle stiffness,state of shock evidence, and rapid breathing are only some ofthe significant conditions that could be appreciated by physi-cians through video technologies. In terms of connectiveinfrastructure, the traditional technologies are being over-come by the following emergent technologies: cloud services,distributed services, and IoT. As the accessibility to the infor-mation is an important feature for mHealth systems, these

emergent technologies provide the architectures withresourceful capabilities for different kind of services. Anotherimportant consideration is that the WBSN-based designshave to take into account factors like mobility, infrastructureat urban and rural areas, and crowded environments with thecoexistence of different communication technologies. One ofthe most important features of mHealth systems is the energyconsumption. Important energy optimizations to be consid-ered are (1) the incorporation of signal-processing tech-niques to reduce data volume; (2) the incorporation of vitalsign correlation to improve the communication network per-formance; and (3) the adoption of sampling strategies toreduce the data volume. Signal processing reduces the datavolume using compression techniques. The reduction inenergy consumption occurs if the time to transmit com-pressed data is shorter than the time to transmit uncom-pressed data. Relevant signal-processing techniques withinWBSN are QR peak detection, QRS complex, interpolationalgorithms, and compressive sensing. The reduction inenergy consumption is also possible by correlating vital signdata and by incorporation polling intervals at the sensors,only after the identification of an abnormal condition duringthe correlation process. Reductions in energy consumptionare also possible by adopting sampling strategies that avoida constant sampling rate [35]. The correct selection of a sam-pling strategy can reduce data volume and the energy con-sumption at the time to transmit data over a WBSN. Othersampling strategies that must be considered to improveenergy efficiency are (1) adaptive sampling that modifies inreal time the data collection after analyzing the correlationbetween the sensed data and the energy remaining at thepower supply; (2) hierarchical sampling with multiple com-plex and simple sensors enabling the possibility to trade pre-cision and power consumption; and (3) model-driven activesampling that builds an abstraction of the sensed parameterusing a forecasting model. This model predicts the next datato be acquired by the sensor.

A final WBSN-based design and implementation has toconsider energy efficiency. The whole WBSN-based systemcan fail if one or more nodes run out of battery. TheWBSN-based design has to take into account consumption-aware policies and adaptive behaviors regarding networkand patient conditions [33, 36].

5. Conclusion

Our systematic review presented a comprehensive over-view of the integrated mHealth architectures for outdooremergencies. Our systematic review also highlighted theemergent connectivity technologies and the energy optimi-zation protocols implemented in mHealth systems for out-door emergencies.

Despite the growing presence of remote health monitor-ing in home, hospital, and emergency scenarios, the develop-ment based on wireless body sensor networks (WBSN) is stillat a redefinition stage especially for outdoor emergencies.

Our review also highlighted the need for more integratedmHealth solutions specifically for outdoor scenarios. Onlyone of our surveyed studies presented an integrated solution


based on the need of mHealth systems capable of switchingamong requirements depending on an outdoor or indoorcontext. Also, the integration of emergent connective tech-nologies into mHealth architectures are redefining the wayinformation is being managed. Emergent technologies suchas cloud services and Internet-of-things (IoT) representnew paradigms for the communication among specific layersfound in mHealth systems. The sensor nodes demanded bythese technologies must satisfy several requirements: (1) highdegree of integration with a small size, (2) local processing ofmultiple signals, (3) reduction in power consumption of themicroprocessor, (4) reduction in wireless data transmission,(5) data intensive collection, and (6) reduced duration ofthe processor’s active mode. Finally, our review highlightedthe need for implementing and evaluating energy consump-tion protocols.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by the Department of Engineer-ing and Communications of Tecnologico de Monterrey.

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