Wearable systems for shoulder kinematics assessment: a … · 2019. 11. 15. · RESEARCH ARTICLE...

Carnevale et al. BMC Musculoskeletal Disorders (2019) 20:546 https://doi.org/10.1186/s12891-019-2930-4

RESEARCH ARTICLE Open Access

Wearable systems for shoulder kinematics

assessment: a systematic review

Arianna Carnevale1, Umile Giuseppe Longo1* , Emiliano Schena2, Carlo Massaroni2, Daniela Lo Presti2,Alessandra Berton1, Vincenzo Candela1 and Vincenzo Denaro1

Abstract

Background: Wearable sensors are acquiring more and more influence in diagnostic and rehabilitation field toassess motor abilities of people with neurological or musculoskeletal impairments. The aim of this systematicliterature review is to analyze the wearable systems for monitoring shoulder kinematics and their applicability inclinical settings and rehabilitation.

Methods: A comprehensive search of PubMed, Medline, Google Scholar and IEEE Xplore was performed and resultswere included up to July 2019. All studies concerning wearable sensors to assess shoulder kinematics wereretrieved.

Results: Seventy-three studies were included because they have fulfilled the inclusion criteria. The results showedthat magneto and/or inertial sensors are the most used. Wearable sensors measuring upper limb and/or shoulderkinematics have been proposed to be applied in patients with different pathological conditions such as stroke,multiple sclerosis, osteoarthritis, rotator cuff tear. Sensors placement and method of attachment were broadlyheterogeneous among the examined studies.

Conclusions: Wearable systems are a promising solution to provide quantitative and meaningful clinicalinformation about progress in a rehabilitation pathway and to extrapolate meaningful parameters in the diagnosisof shoulder pathologies. There is a strong need for development of this novel technologies which undeniablyserves in shoulder evaluation and therapy.

Keywords: Shoulder kinematics, Upper limb, Wearable system, Inertial sensors, Smart textile

BackgroundShoulder kinematics analysis is a booming researchfield due to the emergent need to improve diagnosisand rehabilitation procedures [1]. The shoulder com-plex is the human joint characterized by the greatestrange of motion (ROM) in the different planes ofspace.Commonly, several scales and tests are used to

evaluate shoulder function, e.g., the Constant-Murleyscore (CMS), the Simple Shoulder test (SST), theVisual Analogue Scale (VAS) and the Disability of the

© The Author(s). 2019 Open Access This articInternational License (http://creativecommonsreproduction in any medium, provided you gthe Creative Commons license, and indicate if(http://creativecommons.org/publicdomain/ze

* Correspondence: [email protected] of Orthopaedic and Trauma Surgery, Campus Bio-MedicoUniversity, Via Álvaro del Portillo, 200, 00128 Rome, ItalyFull list of author information is available at the end of the article

Arm, Shoulder, and Hand (DASH) score [2–4]. How-ever, despite their easy-to-use and wide application inclinical settings, these scores conceal an intrinsic sub-jectivity [2–4], inaccuracy in approaching diagnosis,follow-up and treatment of the pathologies. Quantita-tive and objective analyses are rapidly developing as avalid alternative to evaluate shoulder activity level, togauge its functioning and to provide informationabout movement quality, e.g., velocity, amplitude andfrequency [5, 6]. This interest in the use of measuringsystems is growing in many medical fields to recordinformation of clinical relevance. For example, elec-tromyography (EMG), force sensors, inertial measure-ment units (IMU), accelerometers, fiber optic sensorsand strain sensors are employed for human motionanalysis, posture and physiological parameters moni-toring [7–10]. From a technological viewpoint, the

le is distributed under the terms of the Creative Commons Attribution 4.0.org/licenses/by/4.0/), which permits unrestricted use, distribution, andive appropriate credit to the original author(s) and the source, provide a link tochanges were made. The Creative Commons Public Domain Dedication waiverro/1.0/) applies to the data made available in this article, unless otherwise stated.

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Carnevale et al. BMC Musculoskeletal Disorders (2019) 20:546 Page 2 of 24

monitoring of shoulder motion is challenging due tothe complexity of joint kinematic which require thedevelopment of protocols exploiting sensing technol-ogy as much as possible reliable and unobtrusive. Inthe last years, a great number of human motion ana-lysis systems have been largely employed for objectivemonitoring. These systems can be classified into twomain categories: wearable and non-wearable [11]. Thelast one includes electromagnetic tracking systems(e.g., Fastrak) [12], ultrasound-based motion analysissystems (e.g., Zebris) [13], stereo-photogrammetricand optoelectronic systems (e.g., VICON, Optotrak,BTS SMART-D) often used as gold standard [14–17].These systems based on magnetic field, ultrasoundand cameras are effectively suitable for 3D motiontracking and analysis due to their accuracy, precisionand reliability [18]. On the other hand, such systemsrequire expensive equipment, frequent calibration and,overall, they restrict measurements in structured en-vironment [19]. Wearable systems overcome theseshortcomings and they are a promising solution forcontinuous and long-term monitoring of human mo-tion in daily living activities. Gathering data in un-structured environment continuously (e.g., homeenvironment) provide additional information com-pared to those obtainable inside a laboratory [20].Wearable sensor-based systems, intended for kine-

matics data extraction and analyses, are acquiringmore and more influence in diagnostic applications,rehabilitation follow-up, and treatments of neuro-logical and musculoskeletal disorders [21, 22]. Suchsystems comprise accelerometers, gyroscopes, IMU,among others [23]. Patients’ acceptance of monitoringsystems that should be worn for long-time relies onsensors’ features whose must be lightweight, unobtru-sive and user-friendly [24]. The increasing trend toadopt such wearable systems has been promoted bythe innovative technology of micro-electro-mechanicalsystems (MEMS). MEMS technology has fostered sen-sors’ miniaturization, paving the way for a revolution-ary technology suited to a wide range of applications,including extraction of clinical-relevant kinematics pa-rameters. In recent years, there has been growth inthe use of smart textile-based systems which integratesensing units directly into garments [11, 25, 26].Moreover, in the era of big data, machine learningtechnical analysis can improve home rehabilitationthanks to the recognition of the quality level of per-formed physical exercises and the possibility to pre-vent disorders in patients’ movement [27].The aim of this systematic literature review is to de-

scribe the wearable systems for monitoring shoulderkinematics. The authors want to summarize the mainfeatures of the current wearable systems and their

applicability in clinical settings and rehabilitation forshoulder kinematics assessment.

MethodsLiterature search strategy and study selection processA systematic review was executed applying thePRISMA (Preferred Reporting Items for SystematicReviews and Meta-Analyses) guidelines [28]. Full-textarticles and conference proceedings were selectedfrom a comprehensive search of PubMed, Medline,Google Scholar and IEEE Xplore databases. Thesearch strategy included free text terms and Mesh(Medical Subject Headings) terms, where suited.These terms were combined using logical Boolean op-erators. Keywords and their synonyms were combinedin each database as follows: (“shoulder biomechanics”OR “upper extremit*” OR “shoulder joint” OR “scapu-lar-humeral” OR “shoulder kinematics” OR “upperlimb”) AND (“wearable system*” OR “wearable de-vice*” OR “wearable technolog*” OR “wearable elec-tronic device*” OR “wireless sensor*” OR “sensorsystem” OR “textile” OR “electronic skin” OR “inertialsensor”). No filter was applied on the publication dateof the articles, and all results of each database wereincluded up to July 2019. After removal of duplicates,all articles were evaluated through a screening of titleand abstract by three independent reviewers. Thesame three reviewers performed an accurate readingof all full-text articles assessed for eligibility to thisstudy and they performed a collection of data tominimize the risk of bias. In case of disagreementamong investigators regarding the inclusion and ex-clusion criteria, the senior investigator made the finaldecision.Inclusion criteria were:

i) The studies concern wearable systems as a tool toassess upper limb kinematics;

ii) The studies used sensors directly stuck on thehuman skin by means of adhesive, embedded withinpockets, straps or integrated into fabrics;

iii) Systems intended for motion recognition andrehabilitation;

iv) Articles are written in English language;v) Papers are published in a peer-reviewed journal or

presented in a conference;

Exclusion criteria were:

i) Use of prosthetics, exoskeleton or robotic systems;ii) Wearable system not directly worn or tested on

human;iii) The study concerns wearable systems for full-body

motion tracking;


iv) Shoulder joint is not included;v) Reviews, books.

Data extraction processData extraction was executed on 73 articles. Data was ex-tracted on the base of the following checklist: authors, yearand type of publication (i.e., conference or full-text); typ-ology, number, brand and placement of the sensors usedto measure or track the kinematic of the interested joint,wearability of the system, target parameters with regard tothe shoulder; system used as gold standard to assess thewearable systems’ performance; tasks executed in the as-sessment protocol; characteristics of the participants in-volved in the study and aim of the study.

ResultsThe literature search returned 1811 results and additional14 studies were identified through other sources. A totalof 73 studies fulfilled the inclusion criteria (Fig. 1), ofwhich 27% were published on conference proceedings andthe remaining 73% on peer-reviewed journal.Three levels of analysis have been emphasized in this

survey: A. application field and main aspects covered, B.

Fig. 1 PRISMA 2009 flow diagram

the typology of sensors exploited to measure kinematicparameters, C. the placement of the single measurementunits on the body segment of interest and how sensingmodules are integrated into the wearable system from awearability viewpoint.

Application fieldFifteen out of the 73 studies focused on evaluating upperlimbs motion in case of musculoskeletal diseases (e.g.,osteoarthritis, rotator cuff tear, frozen shoulder), 26 onneurological diseases and ap-plication in neurorehabilita-tion (e.g., stroke, multiple sclerosis), 15 on general rehabili-tation aspects (e.g., home rehabilitation, physiotherapymonitoring) and 17 focusing on validation and develop-ment of systems and algorithm for monitoring shoulderkinematics. Tables 1, 2, 3 and 4 include, for each of theidentified application fields, data listed in the previous dataextraction process section.

Sensing technologySome studies combined different sensors in their mea-surements system. The most used sensors are acceler-ometers, gyroscopes and magnetometers, a combination

Table

1Shou

lder

motionmon

itorin

gforapplicationin

patientswith

musculoskeletaldisorders

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

Coley

2007,[29]

Full-Text

IMU(n=2),

AnalogDevice

(gyr:A

DXR

S250,acc:

ADXL

210)

Bilateral:

humeri(po

steriorly,

distally)

Adh

esivepatch

ShRO

M(HTjointangles),

RMSE

=5.81°

Mean=1.80°

Zebris

CMS-HS

ShFLX-EXT

ShAB-AD

ShIER

HS(n=10),

25.1±4.1Y

P(n=10),

7RC

,3OA,

4F,6M,

62.4±10.4Y

Find

objectivescores

toassess

shou

lder

functio

n;to

validate

such

scores

comparin

ghe

althy

andaffected

shou

lders

Coley

2008,[30]

Full-Text

IMU(n=4),

AnalogDevice

(gyr:A

DXR

S250,

acc:ADXL

210)

Bilateral:

humeri(po

steriorly,

distally),thorax

Adh

esivepatch

ShRO

M(HTjointangles)

–Dailyactivities

(8h)

HS(n=35)

32±8Y

Quantify

usageof

shou

lder

and

thecontrib

utionof

each

shou

lder

indaily

lifeactivities

Jolles2011,[31]

Full.Text

IMU(n=4),

AnalogDevice(gyr:

ADXR

S250,acc:

ADXL

210)

Bilateral:

Hum

eri(distally,

posteriorly),thorax

(×2)

Adh

esivepatch

ShRO

M(HTjointangles),

r=0.61–0.80

–7activities

ofSST

P(n=34)

27RC

,7OA

25M,9

F57.5±9.9

CG(n=31)

17M,14F

33.3±8.0

Validateclinicallyshou

lder

parametersin

patientsafter

surgeryforGHOAandRC

disease

Duc

2013,[5]

Full-Text

IMU(n=3),

AnalogDevice(gyr:

ADXR

S250,acc:

ADXL

210)

Bilateral:

Hum

eri(distal,p

osterio

r),sternu

mAdh

esivepatch

ShRO

M(HTjointangles),r2=

0.13-0.52(CMS)

r2=0.06-0.30(DASH

)r2=0.03-0.31(SST)

–Free

movem

ents,

daily

activities

HS(n=41)

34±9Y

P(n=21)

53±9Y

RC(unilateral)

Validateametho

dto

detect

movem

entof

thehu

merus

relativeto

thetrun

kandto

provideou

tcom

esparameters

aftershou

lder

surgery(freq

uency

ofarm

movem

entandvelocity)

Körver

2014,[32]

Full-Text

IMU(n=2),

Inertia-Link-2400-SKI,

MicroStrain

Bilateral:

Hum

erus

(distal,po

sterior)

Adh

esive

ShRO

M(HTjointangles),

r=0.39

(DASH

)r=

0.32

(SST)

–Handto

theback,

Handbe

hind

the

head

HS(n=100)

37M,63F

40.6±15.7Y

P(n=15)

5M,10F

57.7±10.4Y,

Subacrom

ial

imping

emen

t

Investigateabou

tthecorrelation

betw

eensubjective(clinicalscale)

andob

jective(IM

U)assessmen

tof

shou

lder

ROM

durin

galong

-term

perio

dof

follow-up

VanDen

Noo

rt2014,[33]

Full-Text

M-IM

U(n=4),

Xsen

sMTw

Unilateral:

Thorax,scapu

la,

UA,FA

Straps,skintape

ShRO

M(HTandST

jointangles)

–FLX(sagittalplane)

andAB(fron

talp

lane

)with

elbextend

edandthum

bpo

intin

gup

HS(n=20)

3M,17F

36±11

Y

Evaluate

theintra-

andinter-ob

server

reliabilityandprecisionof

3Dscapulakine

matics

Pichon

naz2015,

[34]

Full-Text

IMU(n=3),

AnalogDevice

(acc:A

DXL

210,

gyr:ADXR

S250)

Bilateral:

Sternu

m,H

umeri(po

sterior,

distal)

Skin

tape

ShRO

M(HTjointangles)

–Free

movem

ents

(7h)

HS(n=41)

23M,18F

34.1±8.8Y

P(n=21)

Exploredo

minantandno

n-do

minant

arm

usageas

anindicatorof

UL

functio

nafterrotatorcuffrepair

durin

gthefirstyear

aftersurgery


Table

1Shou

lder

motionmon

itorin

gforapplicationin

patientswith

musculoskeletaldisorders(Con

tinued)

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

14M,7

F53.3±9Y

RC

Roldán-Jim

énez

eCuesta-Vargas

2015,[35]

Full-Text

M-IM

U(n=4),

InterSen

seInertiaCub

e3

Unilateral:

Sternu

m,hum

erus,

scapula,FA

(nearwrist)

adhe

sive

tape

and

band

age

ShRO

M(HTandST

jointangles)

–180°

shABandEXTwith

wriin

neutralp

osition

andelbextend

ed

HS(n=11)

8M,3

F24.7±4.2Y

Analyse

ULangu

larmob

ility

and

linearacceleratio

nin

three

anatom

icalaxes

VanDen

Noo

rt2015,[36]

Full-Text

M-IM

U(n=4),

Xsen

sMTw

Unilateral:

Thorax,Scapu

la,U

A,FA

(ISEO

protocol

[33,37])

Straps,skintape

ShRO

M(STjointangles)

–Hum

eralFLX(sagittal

plane)

andAB(fron

tal

plane)

with

elbextend

edandthum

bpo

intin

gup

P(n=10)

8M,2

F24–63Y

SD

Evaluate

thechange

in3D

scapular

kine

maticsusingasing

leand

doub

leanatom

icalcalibratio

nwith

ascapular

locatorversus

standard

calibratio

n;evaluate

differencein

3Dscapular

kine

matics

betw

eenstaticpo

stureanddynamic

humeralelevation

Roldán-Jim

énez

eCuesta-Vargas

2016,[38]

Full-Text

M-IM

U(n=3),

InterSen

se,

InertiaCub

e3

Unilateral:

Hum

erus

(middlethird

,slightlypo

sterior),

scapula,

sternu

mDou

ble-side

dtape

,elastic

band

age

ShRO

M(GHandST

jointangles)

–Sh

AB(fron

talp

lane

)Sh

FLX(sagittalplane)

Wriin

neutralp

osition

andelbextend

ed

Youn

gHS(n=11),

18–35Y

3M,8

FAdu

ltHS(n=14)

>40

Y5M,9F

Analyze

differences

inshou

lder

kine

maticsin

term

sof

angu

lar

mob

ility

andlinearacceleratio

nrelatedto

age

Wang2017,[26]

Full-Text

M-IM

U(n=3),

Adafru

itFLORA

9-DOF

Unilateral:

Shou

lder

(flat

partof

acromion),spine

(C7-T1,

T4-T5)

Zipp

edvest,elasticstrap

with

Velcro

ShRO

M(STjointangles),

RMSE

~3.57°

PST-55/

110

series

60°sh

FLXwith

elb

extend

edandthum

bpo

intin

gup

,place

acookingpo

ton

ashelf,

40°sh

ELin

thescapular

planewith

elbextend

edandthum

bpo

intin

gup

,place

abo

ttleof

water

onashelf

Pwith

musculoskeletal

shou

lder

pain

(n=

8) 3M,5

F50

±6.44Y

Physiotherapist

(n=5)

Evaluate

usability

ofasm

art

garm

ent-supp

ortedpo

stural

feed

back

scapular

training

inpatientswith

musculoskeletal

shou

lder

pain

andin

physiotherapists

who

take

care

patientswith

shou

lder

disorders

Aslani2018,[39]

Full-Text

M-IM

U(n=1),Bosh

SensortecBN

O055

EMG,M

yoWare

MuscleSensor

Unilateral:

UA,d

eltoid

(2surface

electrod

eson

each

delto

idsection)

Band

ShRO

M(azimuthaland

elevationangles)

–Arm

EL(m

edially,anteriorly,

cranially,p

osterio

rly,laterally)

with

elbfully

extend

ed

HS(n=6)

4M,2

F27.3±3.4Y

P(n=1),M

Frozen

Sh42

Y

Evaluate

ameasuremen

tsprotocol

toassess

thepe

rform

ance

ofthe

shou

lder

bycombining

both

ROM

andelectrom

yography

measuremen

ts

Carbo

naro

2018,

[40]

M-IM

U(n=3),Xsens

MTw

Unilateral:

Sternu

m,FA(distal),

ShRO

M(GHandST

jointangles)

–Extra-rotatio

n,Arm

AB(with

different

load)

HS(n=5)

Testadigitalapp

licationintend

edfortele-m

onito

ringand


Table

1Shou

lder

motionmon

itorin

gforapplicationin

patientswith

musculoskeletaldisorders(Con

tinued)

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

Con

ference

scapula(sim

ilarto

the

configurationin

[37])

Elastic

band

s

tele-reh

abilitatio

nof

theshou

lder

muscular-skeletaldiseases

Hurd2018,[41]

Full-Text

Acc

buil.in

ActiGraph

(n=2),A

ctiGraph

GT3XP

-BLE

Unilateral:

Wrist,UA(m

id-bicep

slevel)

Velcro

strap

ShRO

M–

ADL

P(n=14)

7M,7

F73

±6Y

GHOA,RCdisease

Evaluate

change

sin

pain,

self-repo

rted

functio

nandob

jective

measuremen

tof

uppe

rlim

bactivity

afterRSA

Lang

ohr2018,[6]

Full-Text

IMU(n=5),YEI

Techno

logy

Bilateral:

Sternu

m,U

A(lateral

aspe

ctsof

the

midhu

merus),FA

(dorsalaspectof

the

wrist)

Shirt

ShRO

M(hum

eral

elevationandplaneof

elevationangles)

–Dailyactivities

(1day,mon

itorin

gof

11±3

h)

P(n=36)

73±10

YTSA,RTSA

Determinethetotald

ailyshou

lder

motionof

patientsafterTSAand

RTSA

,com

pare

themotionof

the

arthroplasty

shou

lder

with

that

ofthecontralateralasymptom

atic

jointandcompare

thedaily

motionof

TSAandRTSA

shou

lders

accaccelerometer,g

yrgy

roscop

e,mag

nmag

netometer,IMUInertia

lMeasuremen

tUnit,M-IM

UMag

neto

andInertia

lMeasuremen

tUnit,UAUpp

erArm

,FAFo

rearm,R

OM

Rang

eof

motion,

HThu

merotho

racic,ST

scap

ulotho

racic,GHglen

ohum

eral,Shshou

lder,w

riwrist,elbelbo

w,FLX-EXT

flexion

-exten

sion

,AB-ADab

duction-ad

duction,

IERinternal-externa

lrotation,

RMSE

root

meansqua

reerror,r=

correlation,

r2,coe

fficient

ofde

term

ination,

HSHealth

ysubject,CG

Con

trol

Group

,Ppa

tient,M

male,

Ffemale,

RCRo

tatorCuff,OAOsteo

arthritis,Y

Yearsold,

SSTSimpleSh

oulder

test,D

ASH

Disab

ility

oftheArm

,Sho

ulde

ran

dHan

d,CM

SCon

stan

tMurleyScore


Table

2Shou

lder

motionmon

itorin

gforapplicationin

patientswith

neurolog

icaldisorders

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entand

wearability

Target

shou

lder

parameters,

Perfo

rmance

Goldstandard

Task

executed

Participants

Aim

Bartalesi2005,

[8]

Con

ference

Strain

sensor,

WACK

ERLtd.

(ELA

STOSILLR

3162

A/B)

Unilateral:

Sensors’segm

ent

seriesalon

grig

htup

perlim

bShirt

ShRO

M–

–HS

Wearablegarm

entableto

reconstruct

shou

lder,w

ristandelbo

wmovem

ent

tocorrectstroke

patients’rehabilitation

exercises

Hester2006,

[42]

Con

ference

Acc

(n=6)

Unilateral:

thum

b,inde

x,back

ofthehand

,FA

andUA(m

edially),

thorax

Adh

esive

ShRO

M–

Reaching

,prehe

nsion,

manipulation

SP(n=12)

Pred

ictclinicalscores

ofstroke

patients’

motor

abilities

Zhou

2006,[43]

Full-Text

IMU(n=2),

Xsen

sMT9B

Unilateral:

Wrist(inwards),

elbo

w(outwards)

-

Shorientationand

positio

n–

FAFLX-EXT

FAPR-SU,reach

test

HS(n=1,

M)

Prop

oseadata

fusion

algo

rithm

tolocate

theshou

lder

jointwith

outdrift

Willmann2007,

[44]

Con

ference

M-IM

U(n=4),

Philips

Unilateral:

Torso,shou

lder,

UA,FA

Garmen

t

ShRO

M–

–HS

Provideaho

mesystem

forup

per

limbrehabilitationin

stroke

patients

Zhou

2008,[45]

Full-Text

M-IM

U(n=2),

Xsen

sMT9B

Unilateral:

FA(distally,near

thewristcenter),

UA(laterally,on

thelinebe

tween

thelateralepicond

yle

andtheacromion

process)

Velcro

straps

Shorientationand

positio

n,RM

SE=2.5°-4.8°

CODA

reaching

shrugg

ing

FArotatio

n

HS(n=4,

M)

20–40Y

Validatedata

fusion

algo

rithm

Giorgino2009,

[46]

Full-Text

Strain

sensor

(n=19),

WACK

ERLtd.

(ELA

STOSIL

LR3162

A/B)

Unilateral:

Sensors’sensing

segm

entsdistrib

uted

over

theUA,FA,

shou

lder,elbow

,wrist

Shirt

ShRO

M–

GHFLX(sagittalplane)

LateralA

BER

HS(n=1)

Describeasensinggarm

entfor

posturerecogn

ition

inne

urolog

ical

rehabilitation

Lee2010,[47]

Full-Text

Acc

(n=2),

Freescale

MMA7261

QT

Unilateral:

UA,FA

Velcro

strap

ShRO

M,

Meanerror~0°-3.5°

gon

FLX-EXT(sagittalplane)

HS(n=1)

Validatepe

rform

ance

andaccuracy

ofthesystem

Che

eKian

2010,

[48]

Con

ference

OLE

(n=1)

Acc

(n=1)

Unilateral:

UA,elbow

Adh

esivepatch

ShRO

MIGS-190

Cyclic

movem

entswith

arm

exerciser

HS(n=1)

Validatefeasibility

andpe

rform

ance

(accuracy,repe

atability)of

theprop

osed

sensingsystem

design

edto

assiststroke

patientsin

uppe

rlim

bho

merehabilitation

Patel2010,[49]

Full-Text

Acc

(n=6)

Unilateral:

thum

b,inde

x,back

ShRO

M–

8activities

ofFA

SSP

(n=24)

57.5±11.8

Evaluate

accuracy

ofFA

Sscoreob

tained

via

analysisof

theaccelerometersdata


Table

2Shou

lder

motionmon

itorin

gforapplicationin

patientswith

neurolog

icaldisorders(Con

tinued)

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entand

wearability

Target

shou

lder

parameters,

Perfo

rmance

Goldstandard

Task

executed

Participants

Aim

ofthehand

,FA,U

A,

trun

k-

Ycomparin

gsuch

estim

ates

with

scores

provided

byan

expe

rtclinicianusingthis

scale

Pérez2010,[15]

Full-Text

M-IM

U(n=4),

Xsen

sMTi

Unilateral:

UA(18cm

from

acromion),FA

(25cm

from

epicon

dyle),hand

(5.5cm

from

distal

radio-cubitaljoint),

back

(position

isno

trelevant)

Strap

ShRO

M,

r=0.997(fo

rsh

IR,after

calibratio

n)

BTSSM

ART-D

ShFLX-EXT

Shho

rizon

talA

B-AD

ShIR

ElbFLX

ElbPR-SU

WriFLX-EXT

Servingwater

from

ajar

HS(n=1,F)

Validatethesystem

Bento2011,[50]

Con

ference

M-IM

U(n=4)

Bilateral:

Shou

lder,U

A,w

rist

(affected

and

unaffected

side

)Strap

Shorientation

andpo

sitio

n–

FAto

Table

FAto

box

Extend

elbo

wHandto

table

Handto

box

SP(n=5,

M)

35–73Y

Prelim

inaryvalidationof

asystem

ableto

quantifyup

perlim

bmotor

functio

nin

patientsafterne

urolog

icaltrauma

Ngu

yen2011,

[51]

Full-Text

OLE

(n=3)

Acc

(n=3)

Unilateral:

Shou

lder,elbow

,wrist

Clothingmod

ule

fixed

with

Velcro

straps

ShRO

M,

Test2:RM

SE=3.8°

(gon

),RM

SE=3.1°

(SW)

Test3:ICC=0.975(sh)

Test2:go

n,Shape-

Wrap

Test2:Bend

and

Flex

elbo

wTest3:reaching

task

Test2:

HS(n=3,

M);

Test3:

HS(n=5)

Validationof

theprop

osed

motion

capturesystem

Ding2013,[52]

Full-Text

M-IM

U(n=2),

AnalogDevice

(acc:A

DXL320),

Hon

eyWell(magn:

HMC1053),Silicon

SensingSystem

(gyr)

Unilateral:

UA(distal,ne

arelbo

w),FA

(distal,

near

wrist)

Velcro

strap

ShRO

M–

Replicationof

10reference

arm

posture

HS(n=5)

20–27Y

Che

ckthefeasibility

oftheprop

osed

system

which

measuresorientationand

correctsup

perlim

bpo

stureusing

vibrotactileactuators

Lee2014,[53]

Full-Text

M-IM

U(n=7),

AnalogDevice

(acc:ADXL

345)

InvenSen

se(gyr:ITG

3200)

Hon

eywell

(magn:HMC5883L)

Bilateral:

Back,U

A,FAand

hand

Strap

ShRO

M,

Test1:r=

0.963

Test2:RM

SE<5°

Test1:

gon

Test2:VICON

ShFLX-EXT

ShAB

Arm

IER

ElbFLX

FAPR-SU

WriFLX-EXT

Wriradial-ulnar

deviation

SP(n=5)

2M,3

FMeanage:

68Y

Introd

uceasm

artpho

necentric

wireless

wearablesystem

ableto

automatejoint

ROM

measuremen

tsandde

tect

thetype

ofactivities

instroke

patients

Bai2015,[54]

Full-Text

M-IM

U(n=4or

n=1),

Xsen

sMTx

Unilateral

Scapula,UA,FA,

back

ofthehand

oron

lyon

UA

Velfoam

,Velcro

straps

ShRO

M(upp

erlim

bsegm

ents

orientationandpo

sitio

n)

Test1:

gon

Test2:

gon,VICO

N

ShFLX-EXT

ShIER

ShAB-AD

ElbFLX-EXT

FAPR-SU

WriFLX-EXT

Wriradial-ulnar

deviation

HS(n=10)

8M,2

F20–38Y

P(n=1),F

41Y

Evaluate

afour-sen

sorsystem

anda

one-sensor

system

,investig

atewhe

ther

thesesystem

sareableto

obtain

quantitativemotioninform

ation

from

patients’assessmen

tdu

ring

neuroreh

abilitatio

n


Table

2Shou

lder

motionmon

itorin

gforapplicationin

patientswith

neurolog

icaldisorders(Con

tinued)

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entand

wearability

Target

shou

lder

parameters,

Perfo

rmance

Goldstandard

Task

executed

Participants

Aim

Ertzgaard2016,

[55]

Full-Text

IMU(n=5),

AnalogDevice,

Adis16,350

Bilateral:

Back

(upp

erbo

dy),

UAandFA

Strap

ShRO

M(HTjointangles),

ICC=0.768–0.985

Cod

aMovem

entsthat

mim

icactivities

ofdaily

life

HS(n=10)

2M,8

F34.3±13.1Y

SP(n=1),

F,43

Y

Validationstud

yto

characterizeelbo

wandshou

lder

motiondu

ring

functio

naltaskusingamod

ified

Expo

sure

Variatio

nAnalysis(EVA

)

Lorussi2016,

[56]

Full-Text

M-IM

U(n=2),

Xsen

sMTw

Strain

sensor

(n=

1),

Smartex

Unilateral:

M-IM

Usensorson

sternu

mandUA

Textile-based

strain

sensor

ontheback

(from

thespineto

scapula)

Shirt

ShRO

M(HTjointangleand

scapular

translation)

BTSSM

ART-DX

UAFLX(sagittalplane)

UAAB(fron

talp

lane

)HS(n=5)

Validatesensorsanddata

fusion

algo

rithm

toreconstructscapular-hum

eral

rhythm

Mazam

enos

2016,[57]

Full-Text

M-IM

U(n=2),

Shim

mer2r

Unilateral:

UA(distal,ne

arelbo

w),FA

(distal,

near

wrist)

Straps

ShRO

M(seg

men

ts’

orientationand

positio

n)

–EXP1:Reach

andretrieve,lift

object

tomou

th,rotatean

object

EXP2:p

reparin

gacupof

tea

HS(n=18)

M,F

25–50Y

SP(n=4)

M,F

45–73Y

Evaluate

thepe

rform

ance

androbu

stne

ssof

ade

tectionanddiscrim

ination

algo

rithm

ofarm

movem

ents

Jiang

2017,[58]

Con

ference

Acc

(n=4),

AnalogDevice

ADXL362

EMG(n=4),A

nalog

DeviceAD8232

Tempe

rature

(n=1)

Unilateral:

alon

gup

per

limb

Shirt

ShRO

M–

4typicaljoint

actio

nspe

rform

edin

clinical

assessmen

t

HS(n=1),

MIntrod

ucean

IoT-Basesup

perlim

brehabilitationassessmen

tsystem

for

stroke

patients

Li2017,[59]

Full-Text

IMU(n=2),

InvenSen

se,

MPU

-9250

EMG(n=10),

American

Imex,

Dermatrode

Unilateral:

IMU:U

A,w

rist

EMG:FA(n=8),

UA(n=2)

Stretchablebe

lt

ShRO

M–

11tasksinclud

ing:

ShFLX,

ShAB

WriFLX-EXT,

Fetchandho

ldaballor

acylindricroll,finge

rto

nose,

touchtheback

ofthesh,FA

PR-SU

HS(n=16)

10M,6

F36.25±

15.19Y

SP(n=18)

11M,7

F55.28±

12.25Y

Prop

osedata

fusion

from

IMUand

surface

EMGforqu

antitativemotor

functio

nevaluatio

nin

stroke

subjects

New

man

2017,

[60]

Full-Text

IMU(n=3),G

ait

UpSA

Physilog4

Bilateral:

sternu

m,U

A(posterio

r)Velcro

straps,

adhe

sive

patch

ShRO

M(HTjointangles)

–Reaching

movem

ents(lateral,

forw

ard,

upward)

Children

(n=30)

10.6±3.4Y

17bo

ys,13

girls

Testan

IMU-based

system

tomeasure

uppe

rlim

bfunctio

nin

childrenwith

hemiparesisandits

correlationwith

clinicalscores

Yang

eTan

2017,[61]

Con

ference

M-IM

U(n=4),

APD

MOpal

Unilateral:

Waist,tho

rax,UA

(distal,ne

arelbo

w),

FA(distal,ne

arelbo

w)

Straps

ShRO

M(HTjointangles)

Optitrack

Movem

entsrelatedwith

waistjoint,sh

jointandelb

jointsto

achievejoint

rotatio

n

HS(n=2)

Validatetheprop

osed

motion

tracking

system

Dauno

ravicien

eM-IM

U(n=6),

Bilateral:

ShRO

M–

FNTtest(ShEXT,sh

AB,elb

CG(n=24)

TestaM-IM

Ubasedsystem

to


Table

2Shou

lder

motionmon

itorin

gforapplicationin

patientswith

neurolog

icaldisorders(Con

tinued)

Reference,Year,

Type

ofpu

blication

Sensors,Brand

Placem

entand

wearability

Target

shou

lder

parameters,

Perfo

rmance

Goldstandard

Task

executed

Participants

Aim

2018,[62]

Full-Text

Shim

mer

UA,FA,hand(on

centresof

mass)

Strap

FLX,

hand

SU)

7M

31.14±

5.67

17F

28±3.97

MS-P(n=

34)

13M

36.46±

13.07

21F

42.19±

12.55

iden

tifyqu

antitativeparametersfor

evaluatio

nof

ULdisabilityandrelate

itto

clinicalscores

Jung

2018,[63]

Con

ference

M-IM

U(n=5),

Xsen

sMTw

Awinda

Bilateral:

UAandFA

,trunk

Velcro

straps

ShRO

M(HTjoin

angles),

RMSE

=0.32

for

theestim

ationof

movem

entsqu

alities

usingdata

ofthe

entiredu

ratio

nof

movem

ents

Qualityof

movem

ents’label

provided

bythe

therapist

Reaching

exercise

SP(n=5),F

66.6±15.9

Y

Evaluate

movem

entsqu

ality

regarding

compe

nsationandinter-joint

coordinatio

n;exploitin

gasupe

rvised

machine

learning

approach,validate

thehypo

thesisthat

therapists’evaluation

canbe

madeconsideringon

lythe

beginn

ingmovem

entdata

Lin2018,[64]

Full-Text

IMU(n=2)

Unilateral:

UA(distal,ne

arelbo

w),FA

(dorsal

aspe

ctof

thewrist)

Strap

ShRO

M–

ShFLX-EXT

ShAB

ShER

ElbFLX

FAPR-SU

SP(n=18):

n=9,

control

grou

p(62.6±7.1)

n=9,

device

grou

p(52.2±10.2

Y)

Evaluate

thefeasibility

andefficacyof

anIMU-based

system

forup

perlim

brehabilitationin

stroke

patientsand

compare

theinterven

tioneffectswith

thosein

acontrolg

roup

Repn

ik2018,[7]

Full-Text

M-IM

U(n=7),

Myo

armband

,n=2_

with

n=8EM

Gs

built-in

,Thalamic

labs

Bilateral:

-M-IM

U:Backof

the

hand

,wrist,UA

(distal,ne

arelbo

w),

sternu

m-M

YO:FA(in

the

proxim

ityof

elbo

wjoint)

Straps,arm

band

ShRO

M(HTjointangles)

–ARA

Ttasks:19

movem

ents

divide

din

4subtests(grasp,

grip,p

inch,g

ross

arm

movem

ent)

SP(n=28)

18M,10F

57±9.1Y

HS(n=12)

9M,3

F36

±8Y

Quantify

ULandtrun

kmovem

ent

instroke

patients

accaccelerometer,g

yrgy

roscop

e,mag

nmag

netometer,O

LEOptical

Line

arEn

code

r,IMUInertia

lMeasuremen

tUnit,M-IM

UMag

neto

andInertia

lMeasuremen

tUnit,UAUpp

erArm

,FAFo

rearm,R

OM

Rang

eof

motion,

HThu

merotho

racic,GHglen

ohum

eral,Shshou

lder,w

riwrist,elbelbo

w,FLX-EXT

flexion

-exten

sion

,PR-SU

pron

ation-supina

tion,

AB-ADab

duction-ad

duction,

IERinternal-externa

lrotation,

RMSE

root

meansqua

reerror,

r=correlation,

ICCIntraclass

Correlatio

nCoe

fficients,go

ngo

niom

eter,H

SHealth

ysubject,CG

controlg

roup

,SPStroke

Patie

nt,M

S-PMultip

leSclerosisPa

tient,P

patie

nt,M

male,

Ffemale,

YYe

arsold,

FASFu

nctio

nal

AbilityScale,

ARA

TActionresearch

arm

test


Table

3Shou

lder

motionmon

itorin

gforapplicationin

patientsun

dergoing

rehabilitation

Reference,

Year,Type

of publication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,

Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

Cutti2008,

[37]

Full-Text

M-IM

U(n=4),

Xsen

sMT9B

Unilateral:

Thorax

(flat

portionof

the

sternu

m),Scapula(cen

tral

third

,aligne

dwith

the

cranialedg

eof

thescapular

spine),hum

erus

(cen

tral

third

,slightlypo

sterior),

FA(distal)

Dou

ble-side

dtape

,elastic

cuff

ShRO

M(HTandST

joint

angles)

RMSE

=0.2°-3.2°

VICO

NExp1:elbFLX-EXT,PR-

SU shFLX-EXT,IER,sh

EL-DE

andP-R

Exp2:tasksin

Exp1

+sh

IER(arm

abdu

cted

90°),

shAB-AD(fron

talp

lane

),HTN

(sagittalandfro

ntal

plane)

HS(n=1,M)

Develop

aprotocol

tomeasure

humerotho

racic,scapulotho

racicandelbo

wkine

matics

Parel2012,

[65]

Full-Text

M-IM

U(n=3),

Xsen

sMTx

Unilateral:

thorax,scapu

la,hum

erus

Elastic

cuff,adhe

sive

ShRO

M(HTandST

joint

angles)

–Hum

erus

FLX-EXT(sagit-

talp

lane

)andAB-AD

(scapu

larplane)

HS(n=20)

7F,13M

28.3±5.5Y

P(n=20)

8F,12M

43.9±19.9Y

Assesstheinta-andinter-op

erator

agreem

ent

ofISEO

protocol

inmeasurin

gscapuloh

umeral

rhythm

Dapon

te2013a,[66]

Con

ference

M-IM

U(n=2),Zolertia

Z1Unilateral:

UA,FA

Brace

ShRO

M–

ShAB-AD

ElbFLX

HS(n=1)

Discuss

design

andim

plem

entatio

nof

aho

merehabilitationsystem

Dapon

te2013b,

[67]

Con

ference

M-IM

U(n=2),Zolertia

Z1Unilateral:

UA,FA

Brace

ShRO

M,

Test1:

max

gap=

6.5°

(roll)

5.2°

(pitch)

11.6°(yaw

)

Test1:

Tecno

Body

MJS

Test2:BTS

Test1:shou

lder

IR,EL-

DEandho

rizon

talFLX-

EXT

Test2:elbo

wFLX-EXT

(along

sagittalandho

ri-zontalplane)

HS(n=1,M)

Validationof

aho

merehabilitationsystem

Pan2013,

[68]

Full-Text

Acc

(n=2),

LIS3LV02DQ

Acc

built-in

aSm

artpho

ne(n=

1)

Unilateral:

-acc:U

A,tho

rax

-Smartpho

ne:w

rist

Strap,

Arm

band

ShRO

M(HTjointangles)

–Touchear

Use

finge

rsto

clim

bwall

Pend

ulum

clockw

ise

andcoun

terclockw

ise

Active-assisted

stretch

fore

andside

,raises

hand

from

back

HS(n=10)

3M,7

F20-25Y

P(n=14)

5M,9

F44–67Y

Describede

sign

andim

plem

entatio

nof

ashou

lder

jointho

me-basedrehabilitation

mon

itorin

gsystem

Thiemjarus

2013,[69]

Con

ference

Acc

(n=1),

AnalogDevice

(acc:A

DXL330)

Magn(n=1),

Hon

eywell

(magn:HMC5

843)

Unilateral:

UA(proximal)o

rwrist,l

eftor

right

Strap

ShRO

M,

RMSE

=0.86°-

5.05°

–Sh

FLX-EXT,AB-AD,hori-

zontalAB-AD,IER

HS,(n=23)

20–55Y

Evaluate

theeffect

ofsensor

placem

enton

theestim

ationaccuracy

ofshou

lder

ROM


Table

3Shou

lder

motionmon

itorin

gforapplicationin

patientsun

dergoing

rehabilitation(Con

tinued)

Reference,

Year,Type

of publication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,

Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

Rawashd

eh2015,[70]

Con

ference

M-IM

U(n=1),

InvenSen

se(gyr:ITG

-3200)

AnalogDevice(acc:A

DXL

345)

Hon

eywell(magn:HMC5883L)

Unilateral:

UA(lateral)

Strap

ShRO

M–

7sh

rehabilitation

exercises

2sportsactivities

HS(n=11)

Describeade

tectionandclassification

metho

dof

shou

lder

motionge

stures

that

can

beused

topreven

tshou

lder

injury

Álvarez

2016,[71]

Full-Text

M-IM

U(n=4),

Xsen

sMTx

Unilateral:

Back

ofthehand

,FA

(nearwrist),U

A(near

elbo

w),back

Wristband,

Velcro

strap,

elastic

band

ShRO

M,

Labtest:m

ean

error=

0.06°

(FLX)

1.05°(lateral

deviation)

Labtest:

robo

ticwrist

Test1:Mou

ntingof

ashockdu

mpe

rsystem

Test2:ho

ldingatablet

forlong

perio

dsTest3:elbo

wFLX-EXT

Test1:

Mechanical

worker(n=1)

Test2:worker

ofa

commercial

centre

(n=1)

Test3:patient

(n=1)

Dem

onstrate

thefeasibility

ofan

IMU-based

system

tomeasure

uppe

rlim

bjointangles

inoccupatio

nalh

ealth

Lee2016,

[72]

Con

ference

Strain

sensor

(n=2),

MWCNT,Hyosung

:multi-walled

carbon

nano

tube

s,Eco-

Flex0030,Smoo

th-On:silicon

rubb

er

Unilateral:Shou

lder

Skin

adhe

sive

ShRO

M,RMSE<

10°

OptiTrack

ShFLX-EXT

ShAB-AD

HS(n=1)

Validatesensorsandcalibratio

nmetho

destim

atingtw

oshou

lder

jointangles

Tran

eVajerano

2016,[73]

Con

ference

M-IM

U(n=2),

Shim

mer2r

Unilateral:

UA(distal,ne

arelbo

w),

FA(distal,ne

arwrist)

Straps

ShRO

M(HTjointangles)

–Perio

dicarm

movem

ents

HS(n=1)

Validatean

algo

rithm

topred

ictthereceived

sign

alstreng

thindicator(RSSI)andthefuture

joint-anglevalues

oftheuser

Rawashd

eh2016,[74]

Full-Text

M-IM

U(n=1),

InvenSen

se(gyr:ITG

-3200)

AnalogDevice(acc:A

DXL

345)

Hon

eywell(magn:HMC5883L)

Unilateral:

UA(cen

tralthird

)Straps

ShRO

M(HTmotion)

Visual

observation

7sh

rehabilitation

exercises,baseball

throws,volleyserves

HS(n=11)

25±7Y

Validateade

tectionandclassification

algo

rithm

ofup

perlim

bmovem

ents

Wu2016,

[75]

Full-Text

M-IM

U(n=3),Bluetoo

th3-

SpaceSensor,YEI

Unilateral:FA

(nearwrist),

UA(nearelbo

w),thorax

(shifted

totherig

ht)

Strap,

band

age

ShRO

M(HTjointangles)

–12

gestures

common

indaily

life:3staticand9

dynamic

HS(n=10)

9M,1

F22–24Y

Evaluate

accuracy,recalland

precisionof

age

sturerecogn

ition

system

Burns2018,

[27]

Full-Text

Acc

andgyrbu

ilt-in

asm

art-

watch

(n=1),A

ppleWatch

(Series2&3

)

Unilateral:

Wrist

Wristband

ShRO

M–

Pend

ulum

AB

Forw

ardEL

IR ER TrapeziusEXT

Upright

row

HS(n=20)

14M,6

F19–56Y

Evaluate

perfo

rmance

ofacommercial

smartw

atch

tope

rform

homeshou

lder

physiotherapymon

itorin

g


Table

3Shou

lder

motionmon

itorin

gforapplicationin

patientsun

dergoing

rehabilitation(Con

tinued)

Reference,

Year,Type

of publication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,

Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

Esfahani

eNussbaum

2018,[11]

Full-Text

Textile

sensors(prin

ted)

n=11

Bilateral:

Shou

lder

(n=6),low

back

(n=5)

Und

ershirt

ShRO

M,m

ean

error=

9.6°

for

shangle

estim

ation

M-IM

U(Xsens

MTw

Awinda)

ShAB

ShFLX-EXT

ShIER

Left/right

side

bend

ing

Trun

kFLX-EXT

Trun

krotleft/right

HS(n=16),

10M,6

F21.9±3.3

Describeasm

artun

dershirtandevaluate

itsaccuracy

intask

classificationandplanar

angle

measuresin

theshou

lder

jointsandlow

back

Ramkumar

2018,[76]

Full-Text

Acc,gyr

andmagnbu

ilt-in

asm

artpho

ne,A

ppleiPho

neUnilateral:

UA,FA

Arm

band

ShRO

M<5°

gon

AB(coron

alplane)

forw

ardFLX(sagittal

plane)

IER(elbow

fixed

tothe

body

flexedto

90°)

HS(n=10)

5M,5

FMean27

Y

Validateamotion-basedmachine

learning

softwarede

velopm

entkitforshou

lder

ROM

accaccelerometer,g

yrgy

roscop

e,mag

nmag


lMeasuremen

tUnit,M-IM

UMag

neto

andInertia

lMeasuremen

tUnit,UAUpp

erArm

,FAFo

rearm,R

OM

Rang

eof

motion,

HThu

merotho

racic,ST

scap

ulotho

racic,Sh

shou

lder,elb

elbo

w,FLX-EXT

flexion

-exten

sion

,AB-ADab

duction-ad

duction,

IERinternal-externa

lrotation,

P-Rprotraction-retractio

n,R

MSE

root

meansqua

reerror,HSHealth

ysubject,Ppa

tient,M

male,

Ffemale,

YYe

arsold


Table

4Stud

iesfocusedon

validation/de

velopm

entof

system

s/algo

rithm

sformon

itorin

gshou

lder

motion

Reference,

Year,Typeof

publication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,

Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

Jung

2010,

[77]

Con

ference

IMU(n=6),

ADXL

345(acc)

LPY5150

AL(gyr)

HMC5843

(magn)

Bilateral:

UAandFA

(distalthird),thorax

andpe

lvis

Strap

Shorientation

andpo

sitio

nHDRT

system

Arm

sabovehe

adBend

arms

Bend

waist

HS(n=1)

Validatethemotiontracking

algo

rithm

El-Goh

ary

2011,[78]

Con

ference

IMU(n=2),

APD

MOpal

Unilateral:

FA(nearwrist),U

A(distalthird)

Strap

ShRO

M,

r=0.91–0.97

Eagle

Analog

System

ShFLX-EXT

ShAB-AD

ElbFLX-EXT

ElbPR-SU

HS(n=1)

Validatedata

fusion

algo

rithm

Zhang2011,

[79]

Full-Text

M-IM

U(n=3),

Xsen

sMTx

Unilateral:

UA(laterally,abo

vetheelbo

w),

FA(lateraland

flatside

oftheFA

near

thewrist),sternum

Strap,

clothing

ShRO

M,

RMSE

=2.4°

(shFLX-EXT)

RMSE

=0.9°

(shAB-AD)

RMSE

=2.9°

(shIER)

BTSSM

ART-

DFree

movem

ents

HS(n=4)

Validatesensor

fusion

algo

rithm

El-Goh

ary

2012,[80]

Full-Text

IMU(n=2),

APD

MOpal

Unilateral:

UA(m

iddlethird

,slightly

posterior),

FA(distal,ne

arwrist)

Strapband

ShRO

M,

RMSE

=5.5°

(shFLX-EXT)

RMSE

=4.4°

(shAB-AD)

VICON

ShFLX-EXT

ShAB-AD

ElbFLX-EXT

FAPR-SU

Touching

nose

Reaching

forado

orknob

HS(n=8)

Validatedata

fusion

algo

rithm

LeeeLow

2012,[81]

Full-Text

Acc

(n=2),Freescale

MMA7361

LUnilateral:

UA(nearelbo

w),FA

(nearwrist)

-

ShRO

M,

RMSE

=2.12°

(shFLX-EXT)

RMSE

=3.68°

(shrotatio

n)

IMU

(Xsens

MTx)

UAFLX-EXTandmed

ial/lat-

eralrotatio

nFA

FLX-EXT

andPR-SU

(sagittalplane)

HS(n=1)

Validatethefeasibility

oftheprop

osed

algo

rithm

Hsu

2013,

[82]

Con

ference

M-IM

U(n=2),

LSM303D

LH(acc,m

agn)

L3G4200D(gyr)

Unilateral:

UA,FA

Velcro

strap

ShRO

M,

RMSE

=1.34°-5.08°

Xsen

sMTw

,(n=2)

ShFLX,

AB,EXT,ER

andIR

HS(n=10)

8M,2

F23.3±1.33

Y

Validatedata

fusion

algo

rithm

Lambrecht

eKirsch

2014,

[16]

Full-Text

M-IM

U(n=4),

InvenSen

seMPU

-9150

chip

Unilateral:

Sternu

m,U

A,FAandhand

-

ShRO

M,

RMSE

=4.9°

(shazim

uth)

RMSE

=1.2°

(shelevation)

RMSE

=2.9°

(shIR)

Optotrack

Reaching

movem

ents

HS(n=1)

Validatesensors’accuracy

anddata

fusion

algo

rithm

Ricci2014,

[83]

Full-Text

M-IM

U(n=5),

APD

MOpal

Bilateral:

Thorax,U

A(latero-distally)

andFA

(nearwrist)

Velcro

strap

ShRO

M(HTjointangles)

–UAFLX-EXT

UAAB-AD

FAPR-SU

FAFLX-EXTThorax

rotatio

nThorax

FLX-EXT

Children

(n=40)

6.9±0.65

Y

Develop

acalibratio

nprotocol

for

Thorax

andup

perlim

bmotion

capture

Roldan-

Jimen

ezInertialsen

sorsbu

ilt-in

aSm

artpho

ne(n=1),

Unilateral:

UA

ShRO

M–

shAB,EXTwith

wristin

neutralp

osition

andelb

HS(n=10)

7M,3

FStud

yhu

merus

kine

maticsthroug

hsixph

ysicalprop

ertiesthat

correspo

nd


Table

4Stud

iesfocusedon

validation/de

velopm

entof

system

s/algo

rithm

sformon

itorin

gshou

lder

motion(Con

tinued)

Reference,

Year,Typeof

publication

Sensors,Brand

Placem

entandwearability

Target

shou

lder

parameters,

Perfo

rmance

Gold

standard

Task

executed

Participants

Aim

2015,[84]

Full-Text

LGElectron

icsINC,

iPho

ne4

Neo

pren

earm

belt

extend

ed24.2±4.04

Yto

angu

larmob

ility

andacceleratio

nin

thethreeaxes

ofspace

Fantozzi

2016,[17]

Full-Text

M-IM

U(n=7),

APD

MOpal

Bilateral:

Sternu

m,U

A,FA,b

ack

ofthehand

Velcro

strap

ShRO

M(HTjointangles),

RMSE

<10°(sh

FLX-EXT,AB-AD,

IER)

BTSSM

ART-

DX

Simulated

front-crawland

breaststroke

swim

ming

HS(n=8),

M 26.1±3.4Y

Validateaprotocol

toassess

the3D

jointkine

maticsof

theup

perlim

bdu

ringsw

imming

Men

g2016,

[85]

Full-Text

M-IM

U(n=2),

Shim

mer2r

Unilateral:

UA(distal,ne

arelbo

w),

FA(distal,ne

arwrist)

Straps

ShRO

M,

Test2:RM

SE=

from

2.20°to

0.87°

VICON

ShFLX-EXT

ShAB-AD

ShIER

ElbFLX-EXT

ElbPR-SU

Test1:

HS(n=15),

M,19–23

YTest2:

HS(n=5)

Validatean

algo

rithm

toim

prove

accuracy

onmeasuremen

tsof

arm

jointangles

consideringtheprop

erties

ofhu

man

tissue

Crabo

lu2017,[86]

Full-Text

M-IM

U(n=3),X

sens,

MTw

2Awinda

Unilateral:

UA,scapu

la,

Sternu

mVelcro

strap,

doub

le-sided

tape

,elasticband

GHjointcenter

MRI

acqu

isition

Cross

andstar

motions

(2jointvelocities,

2rang

eof

motions)

HS(n=5)

3M,2

F36

±4Y

Evaluate

accuracy

andprecisionof

theGHJC

estim

ation

Kim

2017,

[87]

Con

ference

MYO

armband

(n=1):

contains

8EM

Gand

1IMU,ThalamicLabs,

MYO

armband

Unilateral:

UA(nearelbo

w)

Arm

band

ShRO

M–

ElbFLX(0°,45°,90°)with

shin

neutralp

osition

,elb

FLX(0°,

45°,90°)with

shFLX90°

(sagittalplane)

HS(n=1)

Introd

ucean

algo

rithm

forup

per

arm

andforearm

motionestim

ation

usingMYO

armband

Morrow

2017,[88]

Full-Text

M-IM

U(n=6),A

PDM

Opal

Bilateral:

FAandUA(lateral),he

ad,

sternu

mVelcro

straps

ShRO

M(HTjointangles),

RMSE

=6.8°±2.7°

(shelevation)

Raptor

12DigitalR

eal

TimeMotion

Capture

System

Pegtransfer

(tomim

icminim

allyinvasive

laparoscop

y)

Surgeo

nHS(n=6)

3M,3

F45

±7Y

ValidateaM-IM

Ubasedprotocol

tomeasure

shou

lder

EL,elbow

FLX,

trun

kFLX-EXTand

neck

FLX-EXTkine

matics

Rose

2017,

[89]

Full-Text

IMU(n=6),A

PDM

Opal

Bilateral:

UA(lateral),FA

(dorsal),

sternu

m,lum

barspine

Straps

ShRO

M(HTjointangles)

–Diagn

ostic

arthroscop

ysimulation

Surgeo

nHS(n=14)

Develop

anIMU-based

system

toassess

the

perfo

rmance

oforthop

aedicreside

ntswith

different

arthroscop

icexpe

riences

Tian

2017,

[90]

Con

ference

M-IM

U(n=2),A

cc:

LIS3LV02D,

Magn:HMC5843,

Gyr:ITG

3200

Unilateral:

UA,FA

Straps

ShRO

MVICON

ShFLXandelbFLX(sagittal

plane)

HS(n=1)

Validatedata

fusion

algo

rithm

Pathirana

2018,[91]

Full-Text

M-IM

U(n=1)

Unilateral:

Wrist

Strap

ShRO

MVICON,

Kine

ctForw

ardFLX-EXT

AB-AD

BackwardFLX-EXT

Horizon

talFLX-EXT

HS(n=14)

10M,4

FValidateaccuracy

androbu

stne

ssof

data

fusion

algo

rithm

usingasing

lesensor

tomeasure

shou

lder

jointangles

accaccelerometer,g

yrgy

roscop

e,mag

nmag


lMeasuremen

tUnit,M-IM

UMag

neto

andInertia

lMeasuremen

tUnit,UAUpp

erArm

,FAFo

rearm,R

OM

Rang

eof

motion,

HThu

merotho

racic,GH

glen

ohum

eral,Shshou

lder,elb

elbo

w,FLX-EXT

flexion

-exten

sion

,PR-SU

pron

ation-supina

tion,

AB-ADab

duction-ad

duction,

IERinternal-externa

lrotation,

RMSE

root

meansqua

reerror,r=

correlation,

HSHealth

ysubject,

Mmale,

Ffemale,

YYe

arsold



of them (n = 55) or with other sensors (n = 8), or built-ininto other devices (e.g., smartphones, smartwatch) (n =6); additional studies (n = 4) utilized strain sensors formotion analysis.

B.1 wearable systems based on inertial sensors andmagnetometersAn IMU allows estimating both translational and rota-tional movements. Such sensors comprise gyroscopesthat measure angular velocity and accelerometers thatmeasure proper acceleration, i.e. gravitational force(static) and force due to movements (dynamic) [92]. Themain limitation of the gyroscopes is the issue bias due todrift. Gyroscopes do not have an external reference, asopposed to accelerometers that use gravity vector as ref-erence; in the orientation estimation, gyroscopes sufferof drift during the integration procedures. To compen-sate such issue, these sensors are combined with magne-tometers that measure magnetic field and use the Earth’smagnetic field as reference. The main limitation of mag-netometers is the interference due to the presence offerromagnetic materials in the surrounding environment[92]. We refer to these hybrid sensors as M-IMU (mag-netic and inertial measurement unit). By integrating theinformation derived from each sensor (i.e., acceleration,angular velocity and magnetic field) through sensor-fusion algorithms, M-IMUs provide an accurate estima-tion of the 3D-position and 3D-orientation of a rigidbody. The upper limb can be modelled as a kinematicchain constituted by a series of rigid segments, i.e.,thorax, upper arm, forearm and hand, linked to eachother by joints that allow relative motion among con-secutive links [17]. In the kinematic chain, the shoulderjoint consists of three degrees of freedom (DOFs)correspondent to abduction-adduction (AB-AD),internal-external rotation (IER), and flexion-extension(FLX-EXT) [15, 54, 57, 71, 79]. Shoulder rotations canbe described using Euler angles that identify the anatom-ical DOFs with the roll-pitch-yaw angles [17, 33, 37, 88].Sensor-fusion algorithms can exploit two main ap-proaches, deterministic or stochastic. The deterministicapproach includes the complementary filter that mergesa high pass filter for gyroscope data (to avoid drift) anda low pass filter for accelerometer and magnetometerdata [64, 82, 90, 92]. The stochastic approach includesthe Kalman Filter and its more sophisticated versions [7,55, 66, 67, 78–80, 91, 92]. The Kalman filter (KF) is themost used algorithm to process M-IMU and IMU datadue to its accuracy and reliability [15, 38, 54, 75, 83, 93].Wearable systems based on IMU or M-IMU include a

variable number of sensor nodes that, properly distrib-uted on each body segment of interest, provide kine-matic parameters such as joint ROM, position,orientation, and velocity. Fifty-one out of the included

studies used exclusively IMUs (n = 15) or M-IMUs (n =36). Systems performances were analyzed in terms of theagreement between results obtained from the M-IMU orIMU-based systems and those collected by a gold stand-ard system. Several types of systems were used as goldstandard, such as ultrasound-based system (e.g., ZebrisCMS-HS [29]), diagnostic imaging (e.g., Magnetic Res-onance [86]), optical-based systems (e.g., VICON [37,53, 54, 80, 85, 90, 91], BTS Bioengineering [15, 17, 56,67, 79], Eagle Analogue System [78], Optotrack [16],Optitrack [61], CODA [45, 55]), goniometer [53, 54]. Re-sults from an inertial system were benchmarked againstan ultrasound-based reference system, showing a rootmean square error (RMSE) of 5.81° and a mean error of1.80° in the estimation of shoulder angles of FLX-EXT,AB-AD and IER evaluated in the sagittal, frontal andtransversal planes, respectively [29]. Accuracy of a proto-col based on commercial inertial sensors (MT9B, Xsens)was tested and compared to a VICON system to meas-ure humerothoracic, scapulothoracic joint angles andelbow kinematics [37]. Results demonstrated high accur-acy in the estimation of upper limb kinematics with anRMSE lower than 3.2° for 97% of data pairs. A BTS ref-erence system was used to validate accuracy of a wear-able system comprised of commercial sensors (Xsens)and results showed a mean error difference of 13.82° forFLX-EXT, 7.44° for AB-AD, 28.88° for IR [15]. In aprotocol-validation study, commercial Opal sensors werecompared to a BTS system to assess upper limb jointkinematics during simulated swimming movements.Data showed a median RMSE always better than 10°considering movements of AB-AD, IER and FLX-EXT infront-crawl and breaststroke [17]. Opal wearable sensorswere compared to optical motion capture systems to es-timate shoulder and elbow angles [78, 80]. Planar shoul-der FLX-EXT and AB-AD were performed showing anRMSE of 5.5° and 4.4°, respectively [80]; a good correl-ation between the measurements performed on shouldermotion with the two systems was also found in [78] (nodata regarding measurements error were proposed).Some studies (n = 11) compared data obtained from

wearable sensors, custom or commercial, with a goldstandard to validate their own sensors data fusion algo-rithm (for more details see Table 4). Two different algo-rithms were compared to a customized KF [79].Comparing the results derived from the BTS system andthe inertial-based system (Xsens), the proposed algo-rithm showed a smaller error than the other twomethods for computing shoulder FLX-EXT (RMSE =2.4°), AB-AD (RMSE = 0.9°), IER (RMSE = 2.9°) [79]. Theaddition of the magnetometer-based heading correctionin the sensor data fusion algorithm was investigated totest the accuracy of an inertial-based motion trackingsystem using the Optotrak Certus (Northern Digital Inc.,


Waterloo, ON, Canada) as reference. Results showed aRMSE of 4.9°, 1.2° and 2.9° for shoulder azimuth, eleva-tion and internal rotation, respectively [16].Four studies used only accelerometers [42, 47, 49, 81].

Systems performance analysis in measurement of armmotion, showed a RMSE lower than 3.5° and 3.68° forshoulder ROM when results from the accelerometers-based systems were benchmarked against a goniometerand commercial M-IMUs, respectively [47, 81]. Evalu-ation of upper limbs’ physical activity was performed re-cording data of accelerometers built-in wearable deviceas ActiGraph (Pensacola, Florida, Model GT3XP-BTLE)to obtain objective outcomes in patients after reverseshoulder arthroplasty [41].Shoulder ROM has been also estimated by means of a

single sensor node which integrated an accelerometerand a magnetometer [69]. Sensor fusion algorithms ofaccelerometers and magnetometers data provide accur-ate orientation estimation in static or semi static condi-tion, e.g., in a rehabilitation session in which patientsperform slow movements [81]. M-IMUs comprised of a3D accelerometer, 3D gyroscope and 3D magnetometerare the most appropriate choice for motion tracking ei-ther in static that in dynamic condition.Two accelerometer-based sensors were combined with

those built-in a smartphone to realize a smart rehabilita-tion platform for shoulder home-rehabilitation [68]. Mo-bile phone or a smartwatch, with their built-in inertialsensor units, were used as mobile monitoring devices [27,76, 84]. These results give proof of the growing trend inthe application of commercial devices in clinical settingfor rehabilitation purposes. Data has been processed usingmachine learning algorithms to extract salient featuresand for gesture recognition related to shoulder motion. Inthese techniques, the main steps are the data collection,followed by segmentation process, feature extraction andclassification [27, 49]. For instance, the identification ofdifferent types of RC physiotherapy exercises has beenperformed processing data from inertial sensors built-in awrist-worn smartwatch [27]. Data from inertial sensorsbuilt-in a smartphone were benchmarked against a man-ual goniometer. Angular differences between a machinelearning-based application and goniometer measurementsresulted less than 5° for all shoulder ROM (i.e., AD, for-ward FLX, IR, ER) [76].Two studies combined accelerometer(s) with Optical

Linear Encoder (OLE) [68, 84]. An OLE-based systemacts as a goniometer providing measures of joint angles.Despite of the simplicity and low cost of the proposedsystems, differences in shoulder ROM estimation re-sulted not negligible when data collected by the wearablesystems were compared against an inertial-based motioncapture (i.e., IGS-190 [54]) and a fiber optics-based sys-tem (i.e., ShapeWrap [71]).

Three studies included EMG sensors in their assess-ment tool in combination with accelerometers [58],IMUs [59] and M-IMU [39]. EMG sensors placed on thebiceps, triceps [59] and deltoid muscles [39] provideadditional information about upper limb motor functionand shoulder assessment, evaluating muscles activity.Quantification of upper limb motion was executedthrough a wearable device, MYO armband by Thalamiclabs, that combines EMG sensors to record electrical im-pulses of the muscles [7, 87].

B.2 wearable systems based on strain sensorsFour studies used smart-textiles instrumented by strainsensors with piezoresistive properties to estimate kine-matic parameters and to perform motion analysis [8, 11,46, 56]. Such sensing elements are stretched or com-pressed during movements of the examined body seg-ments, with consequent variation of their electricalresistance [94, 95]. Using a M-IMU system as reference,accuracy evaluation of a smart-textile with printed strainsensors showed a mean error of 9.6° in planar motionsmeasurements of shoulder joint [11]. Shoulder kinemat-ics was assessed combining a strain sensor for scapularsliding detection with two M-IMUs for HT orientationmeasure [56].Piezoresistive strain sensors directly adhered to the skin

were used to estimate shoulder ROM; the comparison be-tween reference data from an optical-based system (i.e.,Optitrack) and strain sensors showed a RMSE less than10° in shoulder FLX-EXT and AB-AD estimation [72].

Sensors placement and wearabilityPlacement of the sensing technology on the body land-marks has shown a heterogeneous distribution linked tothe different nature of the employed technology and tothe purpose for which monitoring system was designed.With respect to the monitored upper limb, 53 out of the73 studies included in this review showed a unilateraldistribution of the sensing elements while the remainingstudies utilized a bilateral placement. Several configura-tions using different number of sensors and placementshave been investigated as reported in detail in each tableand Fig. 2.Regarding the wearability, we classified the systems in

terms of how the sensors were fixed to the human body:i) by adhesive patch, ii) by means of straps or embeddedwithin pocket, iii) the sensing element is physically inte-grated into the fabric. Four studies did not specify themethod of attachment, 12 studies have stuck sensors dir-ectly on human skin by means of adhesive patch, 52studies have attached sensors through straps or embed-ding them in modular clothing, and 5 studies have inte-grated sensors directly into garments. For more detailsrefers to Tables 1, 2, 3 and 4.

Fig. 2 Placement of sensing units (NOTE One study [90] is not included because the specific position of each sensor nodes is not so clear.Legend: N = number of studies, U = Unilateral, B = Bilateral)


DiscussionThis paper summarizes the main features of wearablesystems that have been employed in clinical setting andresearch field to evaluate upper limb functional perform-ance and particularly for shoulder ROM assessment.Shoulder complex is characterized by the greatest mobil-ity among all human joints and, due to its complexity,reviewed articles evidenced heterogeneity on the moresuitable protocol for capturing joint ROM [96].

Wearable technologyAlthough 73% of the reviewed papers use commercialproducts for tracking joint angles, many of thesepersonalize the positioning of the sensors, the calibrationmethodology and the algorithms used to process the re-corded data. This customization makes strenuous a dir-ect comparison among protocols, especially if sensingunits of different nature (e.g., M-IMU vs. strain sensors)are used to measure the same kinematic parameters,leaving still open the issue of the protocols’ definitionwith general validity.About studies using inertial-based motion tracking

systems, most in this summary (88%), calibration proce-dures before data acquisition and data processing repre-sent a relevant issue about accuracy and reliability of thesystem. Typically, the M-IMUs are attached on the seg-ment of interest to estimate its orientation, so the cali-bration is necessary to relate sensors’ measurements to

movements of the tracked body segment. Sometimes themanufacturer suggests how to perform calibration, e.g.,positioning sensors on a flat surface [15, 35] to align co-ordinate system or assuming static anatomical position[65], as N-pose [79], to compute orientation differencesbetween segments and sensors coordinates in order toobtain sensor-to-segment alignment [56]. Dynamic orfunctional anatomical calibration has also been per-formed in some studies, but the sequence of movementsexecuted varied among these [17, 33, 55, 83]. One inter-esting improvement that may be done to have a positiveimpact on the accuracy of inertial-based motion trackingsystems, is to define a standard set of movements for theinitial calibration and a standard method of data pro-cessing by which extrapolate kinematic parameters ofhigh clinical relevance.Some works have reported remarkable results in hu-

man motion tracking using e-textile sensors [8, 46].Technological improvements in the development of con-ductive elastomers allowed to integrate such strain sen-sors directly into garments making them comfortableand unobtrusive [11, 56]. Although conductive elasto-mers ensure flexibility and performances comparablewith those of the M-IMU sensors, the main limitationsare the hysteresis, uniaxial measurements and non-negligible transient time [56]. Wearable systems basedon strain sensors are a promising technology for kine-matics analysis that may overcome the main M-IMUs


drawbacks, as interferences due to surrounding ferro-magnetic materials, gyroscopes’ error drift and long-term use. On the other hand, errors may occur withstrain sensors-based systems in the estimation of shoul-der kinematics for their inherent hysteresis behaviour.Among wearable systems reviewed in this summary,

differences resulted in terms of sensors typology, num-ber and size, placement, and wearability features. Sen-sors placement and method of attachment must becarefully investigated as they could influence the out-comes reliability (e.g., effects of soft tissues’ artefacts).Human skeleton is covered by skin tissue and muscles.The combination of skin’s elasticity and muscle activitymay cause negative effects in the measurement of thebones’ movement. In studies where M-IMU sensorswere used to track shoulder kinematics, soft tissue prop-erties were opportunely included in mathematicalmodels to reduce soft tissue artifacts [79, 85]. The bodyfat percentage was found the main influencing factorthat negatively affects the inertial sensors’ orientation[85]. To reduce such source of error, either when sen-sors are directly adherent to the skin that embedded in atextile, sensing units should be placed as near as possibleto the bone segment to reduce soft tissue artifacts [97,98]. Wearability is a key factor to consider because itcan influence the level of patients’ acceptance [26].Thereare several relevant requirements that wearable systemsmust meet to encourage their applications in continuousmonitoring of patient status. Indeed, execution of move-ments, either in home environments or in clinical set-tings, should not be hindered by measurements systemsso they must be non-invasive, modular, lightweight, un-obtrusive and include a minimal number of sensors [33,40, 51, 56, 66, 67, 91]. Most studies have employed mag-neto and inertial-based tracking systems in which sen-sors were attached to the upper limb through Velcrostraps or including them in modular brace and garments[26, 45, 67, 82, 88].Upper limb includes the shoulder, elbow and wrist

joints (Fig. 3a). Humerus, scapula, clavicle, and thoraxconstitute the shoulder complex: humeral head articu-lates in the glenoid fossa of the scapula to form GHjoint, the AC joint is the articulation between the lateralend of the clavicle and the acromion process, the SCjoint articulates the medial end of the clavicle and thesternum and the functional ST joint allows rotationaland translational movements of the scapula with respectto the thorax [96] (Fig. 3b). The ST joint and GH jointact togheter in arm elevation according to scapular-humeral rhythm described in [99]. From a biomechan-ical point of view, shoulder complexity is justified by thehigh degree of coupling and coordination betweenshoulder joints (i.e., shoulder rhythm) and the action ofmore than one muscles over more than one joints in the

execution of a movement. Data extraction of shoulderkinematics is frequently based on movements pattern inthe sagittal, frontal and transversal planes, so monitoringof complex movements (e.g., daily activities) in multipleplanes, performed through wearable sensors, requires amore stringent evaluation and accurate interpretation.As resulted in the review, the shoulder is generally ap-proximated as a ball-and-socket joint [56]. This assump-tion provides an approximate representation of thewhole shoulder girdle (e.g., it neglects the contributionof scapular movements). A standardized protocol hasbeen proposed (i.e., The ISEO®, INAIL Shoulder andElbow Outpatient protocol) to improve the performanceof M-IMUs in the estimation of scapular kinematics, bylocating inertial sensors on the back in correspondenceof scapula [33, 35–38, 40, 65]. An adequate investigationof scapular motions may be beneficial to assess shoulderdisorders [100].For long-term monitoring of shoulder kinematics con-

sidering also scapular motions, the combination of M-IMUs and smart-textile with embedded strain sensors isa perfect balancing of accuracy, flexibility and wearability(i.e., strain sensors positioned on the scapula could in-crease the portability and acceptance of the wearablesystem for long-term monitoring of ADLs) [86].

Applicability in clinical setting and rehabilitationAlterations in the complex shoulder kinematics can de-rive by both neurological or musculoskeletal disordersand result in pain and limited movements [68]. Com-pensatory movements in patients with shoulder disor-ders are the most common consequential responses topain or to difficulty in performing free-pain movements.In such situations, information retrieved by posturemonitoring may be beneficial in clinical application andrehabilitation [26]. In the last years, the application ofwearable devices for gathering motion data outside thelaboratory settings is growing. Avoiding complex labora-tory set-up, wearable systems employed to assess upperlimb kinematics have proven to be a well-founded alter-native to obtain quantitative motions parameters. Quan-titative outcomes about shoulder motions recorded bywearable sensors are beneficial in clinical practice interms of time-saving and they are becoming a promisingalternative to improve assessment accuracy overcomingthe subjectivity of clinical scales. The automatic assess-ment of motor abilities can also provide therapists a tan-gible and, therefore, measurable awareness of theeffectiveness of the treatment and the recovery pathchosen.In clinical practice, the severity level of patients’ condi-

tion with musculoskeletal disease is usually assessedthrough questionnaire-based scores [36, 42]. Algorithmsfor kinematic scores computing were developed to

Fig. 3 a Anatomy of the Upper limb; b Anatomy of the Shoulder complex


evaluate shoulder functional performance after surgeryin subjects with GH osteoarthritis and RC diseases, elab-orating data obtained from IMU sensors [29, 31]. Highcorrelation (0.61–0.8) between shoulder kinematicscores (i.e., power score, range of angular velocity scoreand moment score) and clinical scales (e.g., DASH, SST,VAS) was found [31]. Unlike clinical scores, kinematicscores showed greater sensitivity in detecting significantfunctional changes in shoulder activity at each post-operative follow-up with respect to the baseline status[29, 31]. In a five-year follow-up study, asymmetry inshoulder movements was evaluated in patients with sub-acromial impingements syndrome. Asymmetry scores,derived from an IMU-based system, showed post-treatment improvements with greater sensitivity thanclinical scores and only a weak correlation was foundwith DASH (r = 0.39) and SST (r = 0.32) [32]. Quantita-tive evaluation of arm usage and quality of movementsin every kind of shoulder impairment contributes to out-line a clinical picture about the functional recovery andthe effectiveness of the treatment [30, 49]. Using thesame number of IMU (n = 3) and the same placementon both humeri and sternum, the shoulder function wasevaluated before and after treatment, in patients under-went surgery for RC tear [5, 34]. Results showed signifi-cative differences in movements frequency betweenpatients and control group during activities of daily life[5], with limited use of arm at 3months after surgery[34]. With a bilateral configuration based on 5 IMU,shoulder motion was assessed to extrapolate relevantclinical outcomes about Total Shoulder Arthroplasty(TSA) and Reverse Total Shoulder Arthroplasty (RTSA)[6]. Patients underwent either TSA or RTSA showedshoulder ROM below 80° of elevation, indiscriminately;

but, on average, patients treated with RTSA performedmovements above 100° less frequently [6]. Objectivemeasurements (i.e., mean activity value and activity fre-quency) of limb function after RTSA did not show sig-nificant improvements 1 year after surgery, despiteDASH scores and pain perception have improved com-pared to preoperative outcomes [41].In patient with neurological impairments (e.g., stroke), as-

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