Eme ging Topics
A Re iew on Deep Lea ning o Quali y o Li e
Assessmen Th ough he Use o Wea able Da a
Vasileios Ska amagkas1,2, Ioannis Kyp akis1,2,3, Geo gia S. Ka anasiou4,5, Dimi is I. Fo iadis4,5,Fellow,
IEEE, and Manolis Tsiknakis1,2,Membe , IEEE
1Biomedical In o ma ics and eHeal h Labo a o y, Dep . o Elec ical and Compu e Enginee ing, Hellenic Medi e anean Uni e si y,
He aklion, 71410 C e e, G eece
2Ins i u e o Compu e Science, Founda ion o Resea ch and Technology Hellas (FORTH), He aklion, 70013 C e e, G eece
3Dep . o Science e Techniques, Uni e si y o Bu gundy, 21000, Dijon, F ance
4Uni o Medical Technology In elligen In o ma ion Sys ems, Uni e si y o Ioannina, 45110, G eece
5Biomedical Resea ch Ins i u e, FORTH, Ioannina 45110, G eece
CORRESPONDING AUTHOR: V. Ska amagkas (e-mail: [email p o ec ed])
Abs ac
Quali y o Li e (QoL) assessmen has e ol ed o e ime, encompassing di e se aspec s o human exis ence beyond jus heal h.
This pape p esen s a comp ehensi e e iew o he in eg a ion o Deep Lea ning (DL) echniques in QoL assessmen , ocusing
on he analysis o wea able da a. QoL, as de ined by he Wo ld Heal h O ganisa ion, encompasses physical, men al, and social
well-being, making i a mul i ace ed concep . T adi ional QoL assessmen me hods, o en elian on subjec i e epo s o in o mal
ques ioning, ace challenges in quan i ica ion and s anda diza ion. To add ess hese challenges, DL, a b anch o machine lea ning
inspi ed by he human b ain, has eme ged as a p omising ool. DL models can analyze as and complex da ase s, including
pa ien - epo ed ou comes, medical images, and physiological signals, enabling a deepe unde s anding o ac o s in luencing an
indi idual’s QoL. No ably, wea able senso y de ices ha e gained p ominence, o e ing eal- ime da a on i al signs and enabling
emo e heal hca e moni o ing. This e iew c i ically examines DL’s ole in QoL assessmen h ough he use o wea able da a, wi h
pa icula emphasis on he subdomains o physical and psychological well-being. By syn hesizing cu en esea ch and iden i ying
knowledge gaps, his e iew p o ides aluable insigh s o esea che s, clinicians, and policymake s aiming o enhance QoL
assessmen wi h DL. Ul ima ely, he pape con ibu es o he adop ion o ad anced echnologies o imp o e he well-being and
QoL o indi iduals om di e se backg ounds.
INDEX TERMS Quali y o Li e, Deep Lea ning, Wea able Da a, Heal hca e, Machine Lea ning
IMPACT STATEMENT This e iew highligh s he ans o ma i e po en ial o deep lea ning echniques and wea able
echnology in assessing physical and psychological aspec s o Quali y o Li e, enabling mo e pe sonalized and accu a e heal hca e
in e en ions.
I. INTRODUCTION
THE no ion o Quali y o Li e (QoL) has been examined
om mul iple pe spec i es, esul ing in he ecogni ion
ha heal h- ela ed QoL and o al QoL a e equen ly synony-
mous. The Wo ld Heal h O ganisa ion (WHO) cha ac e ises
heal h as a holis ic condi ion o physical, men al, and social
well-being, unde sco ing i s impo ance in imp o ing quali y o
li e. In addi ion o heal h, QoL includes employmen capaci y,
social suppo , and he physical en i onmen [1]. Resea che s
ha e sugges ed ha QoL can be examined om se e al pe -
spec i es, such as psychological, economic, and medical, hence
complica ing i s de ini ion and assessmen [2].
Con en ional app oaches o e alua ing QoL ha e depended
on in o mal enqui ies by heal hca e p o essionals, which
may be subjec i e and a iable. Two p incipal me hodologies
o sys ema ic assessmen ha e a isen: (1) alida ed pa ien -
epo ed ou comes (PROs) ins umen s ha ga he subjec i e
da a [3]; and (2) objec i e da a acquisi ion ia echnologies ha
eco d physiological signals and beha iou s [3]. In esponse o
he necessi y o a ho ough Quali y o Li e e alua ion ool,
he WHO c ea ed he WHOQOL assessmen ins umen , which
includes many domains such as physical heal h, men al well-
being, ela ionships, and en i onmen al ac o s [4] (Fig. 1).
Recen ly, a pa adigm change in QoL assessmen has oc-
cu ed wi h he inco po a ion o Deep Lea ning (DL) ap-
p oaches, which u ilise complica ed da ase s o imp o e com-
p ehension o QoL domains [6]. This inno a ion acili a es he
analysis o many da a sou ces, such as PROs, medical imaging,
and physiological signals, yielding enhanced insigh s in o he
de e minan s o quali y o li e [7]. Wea able echnologies ha e
signi ican ly al e ed he landscape by p o iding con inuous,
eal- ime da a on i al signs and o he heal h pa ame e s, hus
imp o ing he p ecision o quali y o li e assessmen s [8], [9].
This e iew examines he unc ion o deep lea ning app oaches
in e alua ing he physical and psychological heal h subdomains
o QoL, emphasising he p og ess and p ospec i e applica ions
o wea able echnology in his eme ging and c i ical heal hca e
sec o [10].
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Fig. 1. WHOQOL Ins umen Domains and Subdomains [5].
II. PHYSICAL HEALTH ASSESSMENT
The main enance o physical heal h is an essen ial aspec ha
con ibu es signi ican ly o an indi idual’s holis ic well-being.
I comp ises a b oad spec um o ac o s pe aining o he phys-
iological unc ioning and o e all wel a e o he human body
[11]. One o hese ac o s, Human Ac i i y Recogni ion (HAR),
has p og essed ma kedly due o he eme gence o DL, u ilising
wea able senso da a o p ecisely ca ego ise ac i i ies o daily
li ing (ADL) such as walking, jogging, and d i ing. Con o-
lu ional Neu al Ne wo ks (CNN) ha e exhibi ed ema kable
e icacy in ex ac ing spa ial cha ac e is ics om senso da a,
as e idenced by Dua e al. [12], whe e a CNN-GRU hyb id
a ained an accu acy o 96.00% ac oss se e al da ase s. Long
Sho -Te m Memo y (LSTM) ne wo ks, in ended o sequen ial
da a, ha e p o en e ec i e, wi h Kuncan e al. [13] a aining
98.42% accu acy u ilising Mo i Pa e ns. Hyb id models such
as CNN-LSTM [14] enhance pe o mance, achie ing accu acy
le els o up o 99.00% on pa icula da ase s. Recen ly, a en-
ion mechanisms and ans o me s ha e imp o ed he accu acy
o HAR, as demons a ed in Sa ka e al. [15] and Di go a
Lup ako a e al. [16], whe e ans o me -based models a ained
o e 99.00% accu acy by e ec i ely cap u ing empo al de-
pendencies in senso da a. Ne e heless, nume ous esea ch,
including hose employing benchma k da ase s like UCI-HAR,
a e cons ained by es ic ed sample numbe s and insu icien
a ie y, which aises issues o e hei gene alisabili y ac oss
popula ions wi h a ying demog aphics o ac i i y pa e ns.
These cons ain s may impede he model’s e icacy in a -
ied eal-wo ld en i onmen s. Fu he mo e, da ase s equen ly
inadequa ely ep esen he inhe en di e si y o human be-
ha iou s, leading o models ha a e ailo ed o ce ain, o en
idealised ci cums ances ins ead o he unp edic able na u e o
eal-wo ld si ua ions.
Mo eo e , medica ion adhe ence, an essen ial elemen o e -
ec i e he apy, has signi ican ly imp o ed wi h DL algo i hms
and wea able da a, p o iding eal- ime eedback and accu acy
in moni o ing. Odhiambo e al. [17] used a Deep Neu al
Ne wo k (DNN) wi h accele ome e da a om sma wa ches
o iden i y in olun a y mo emen s associa ed wi h medicine,
a aining a p ecision o 96.50%. CNNs ha e been e ec i ely
u ilised, as demons a ed by Lee e al. [18], who employed
a came a image senso combined wi h wea able de ices o
moni o medicine adhe ence, achie ing an accu acy o 92.70%.
CNN-based app oaches o moni o ing ch onic diseases and
glucose le els ha e shown encou aging ou comes [19]. Pe as
e al. [20] employed LSTM ne wo ks, ecognised o hei
capabili y in empo al da a p ocessing, o iden i y audio e en s
om inhale s, achie ing accu acy a es as high as 94.00%,
su passing con en ional app oaches.
Ene gy and a igue (EF) a e essen ial measu es o an in-
di idual’s heal h and p oduc i i y, wi h p ecise measu emen
i al o e alua ing o e all well-being. Recen s udies ha e
in es iga ed inno a i e echniques o iden i ying EF using
wea able senso s and deep lea ning models. Sha ma e al. [21]
employed CNN o moni o w is mo ions and ecognise ea ing
e en s wi h an accu acy o 89.00%, whe eas Wang e al. [22]
in eg a ed CNNs wi h a en ion mechanisms o assess ea ing
speed, achie ing a minimal e o o 0.11. Ad ancemen s in
men al a igue de ec ion ha e been made by deep lea ning
app oaches; Wu e al. [23] employed a Con ac i e Spa se
Au o-encode o ca ego ise a igue s a es om EEG da a,
a aining an accu acy o 83.00%. Bai e al. [24] u ilised a sel -
a en ion LSTM model o a igue de ec ion using ECG and
ac ig aphy da a, illus a ing he e icacy o in eg a ing empo al
and a en ion mechanisms. Addi ional signi ican applica ions
in ol e employing CNNs and BiLSTM o he de ec ion o
d i e sleepiness [25], [26] and u ilising HRV signals om
Eme ging Topics
wea ables o assess d i e a igue [27], wi h hese models a -
aining accu acy a es o up o 94.31%. No wi hs anding hese
de elopmen s, a ade-o exis s be ween he accu acy o high-
pe o ming models, such as hyb id CNN-LSTM a chi ec u es,
and he easibili y o hei implemen a ion on esou ce-limi ed
wea able de ices. The compu a ional equi emen s o hese
models, especially when managing ex ensi e da ase s o eal-
ime da a s eams, may hinde hei deploymen on de ices wi h
limi ed p ocessing capaci y o ba e y longe i y. This equi es
he in es iga ion o mo e compu a ionally e icien algo i hms
ha can sus ain high accu acy while emaining p ac ical o
wea able de ices.
Mobili y is ano he essen ial aspec o public heal h, en-
compassing physical mobili y, ambula ion, and anspo a ion,
all o which enhance an indi idual’s QoL [28]. GPS-enabled
wea ables enable he assessmen o li e-space mobili y, which
is associa ed wi h social suppo and gai speed [29], whils
accele ome e s moni o eloci y and physical ac i i y [30].
Nume ous esea ch e o s u ilise wea able senso s o e alua e
all isk and mobili y challenges, especially among he olde
popula ion. Kulu ka e al. [31] a ained a 95.87% accu acy
in all de ec ion u ilising LSTM and IoT-based sys ems. In
pa ien s wi h Pa kinson’s Disease, eezing o gai (FOG)
was accu a ely p edic ed u ilising ans o me -based opologies
combined wi h BiLSTM, esul ing in ele a ed speci ici y and
sensi i i y [32].
Pain pe cep ion is a mul i ace ed and subjec i e expe ience
ha p esen s di icul ies o objec i e assessmen [33]. Recen
b eak h oughs in wea able echnologies, including elec ode -
mal ac i i y (EDA) senso s and DL algo i hms, p o ide no el
me hods o pain quan i ica ion, hence imp o ing quali y o li e
e alua ions. Gkikas e al. [34] used mul i- ask lea ning (MTL)
neu al ne wo ks wi h ECG da a, enhancing he p ecision o
pain assessmen . Rojas e al. [35] employed unc ional nea -
in a ed spec oscopy and a BiLSTM model o a ain 90.60%
accu acy in e alua ing pain in non-communica i e pa ien s.
Pou om an e al. [36] enhanced pain in ensi y classi ica ion
using a cus omised BiLSTM model, achie ing a 1-sco e o
0.81 and an AUROC o 0.93 ac oss mul iple pain s a es.
Hu e al. [37] p o ed he e icacy o LSTM in ch onic pain
iden i ica ion, a aining p ecision and ecall a es o 97.20% ia
balance and body sway analysis. Wang e al. [38] in es iga ed
p o ec i e beha iou ecogni ion wi h laye ed LSTM me hod-
ologies, achie ing an ideal F1-sco e o 0.82. Fu he mo e, Yu
e al. [39] employed EEG signals o objec i ely assess pain,
a aining classi ica ion accu acy o 97.37%, ia CNN-based
models.
Addi ionally, sleep is a i al physiological condi ion ma ked
by a ansien loss o consciousness and modi ied ce eb al
ac i i y, se ing a undamen al unc ion in bo h physical and
men al well-being [40]. Eme ging wea able echnology and
sophis ica ed deep lea ning app oaches a e c ucial o p ecisely
measu ing sleep, imp o ing pe sonal unde s anding o sleep
pa e ns, and aiding heal hca e p o essionals in de ec ing sleep
p oblems and e ining ea men s a egies. The Ne Heal h
da ase [41], which examined da a om 698 college s u-
den s, e ealed ha CNN could p o icien ly e alua e sleep
quali y, a aining a mean absolu e e o (MAE) o oughly
0.04. Fu he mo e, Yildi im e al. [42] p esen ed a 1D-CNN
model ha au oma ed he classi ica ion o sleep s ages u ilising
polysomnog am (PSG) da a, a aining accu acies anging om
91.00% o 98.06%. Mousa i e al. [43] c ea ed SleepEEGNe ,
which employed single-channel EEG da a o a ain an accu acy
o 84.26% by in eg a ing CNN and sequence- o-sequence mod-
els. In con as , Sup a ak e al. [44] me ged CNN and BiLSTM
ne wo ks in he DeepSleepNe model, achie ing an accu acy o
86.20%. Fu he mo e, ac ig aphy senso s ha e shown e icacy
in o ecas ing sleep e iciency, wi h CNN achie ing he bes
accu acy o 97.30% [45]. LSTM models, as emphasised by
Phan e al. [46], success ully o ecas ed sleep quali y ia
physical ac i i y da a, a aining an accu acy o 61.00. Finally,
Ma sumo i e al. [47] u ilised a hyb id CNN-LSTM model wi h
a ligh weigh EEG senso , a aining an accu acy o 78.60%,
equi alen o clinical PSG sys ems.
Las ly, wo k capaci y, as de ined by he Ame ican College
o Spo s Medicine (ACSM), e e s o he maximum physical
wo k an indi idual can pe o m, assessed h ough powe ou pu
o endu ance and in luenced by ac o s like ca dio espi a o y
i ness and muscula s eng h [48]. T adi ional e alua ions ha e
elied hea ily on sel - epo ins umen s, which o en su e
om eliabili y issues due o biases and ecall p oblems [49].
The Wo k-abili y Suppo Scale (WSS) e ec i ely measu es
oca ional capabili y a e disabili y, co e ing physical, cogni-
i e, and social domains [50]. O he assessmen s, such as he
Func ional Capaci y E alua ion (FCE) and he Wo k Abili y
Index (WAI), ocus on job-speci ic physical and cogni i e
equi emen s [51]. The Wo k Capaci y Tes (WCT), used by
o ganiza ions like he U.S. Fo es Se ice, assesses physical ca-
pabili ies o demanding oles [52]. Wea able ac i i y acke s
can quan i y many wo k capaci y ac o s, making hem use ul
o physically demanding jobs. Howe e , like mobili y, we
assume ha he subse o wo k capaci y ha can be e alua ed
using DL using wea able senso da a is closely connec ed wi h
ADL e alua ion.
Fo a comp ehensi e summa y o s udies employing wea -
able de ices o physical heal h assessmen , including da ase s,
senso s, and me hodologies, we e e eade s o Table I in he
supplemen a y ma e ial.
III. PSYCHOLOGICAL HEALTH
The QoL o an indi idual is signi ican ly in luenced by hei
physiological heal h, encompassing a ious dimensions such
as eelings, sel -es eem, memo y, spi i uali y, and body image
[53]. The impo ance o physiological heal h wi hin he la ge
amewo k o QoL becomes appa en when we conside i s
di ec in luence on many domains. One such domain, eelings,
encompassing bo h posi i e and nega i e s a es, a e unda-
men al o well-being and QoL [54]. Recen ad ancemen s in
wea able echnology ha e signi ican ly imp o ed he abili y o
iden i y emo ions by moni o ing physiological signals such as
hea a e a iabili y, skin conduc ance, and acial exp essions
[55]. Resea ch employing DL me hodologies has demons a ed
his po en ial; o ins ance, he eSEE-d da abase u ilises eye-
acking da a o emo ion es ima ion, achie ing an accu acy o
Eme ging Topics
up o 92.00% o posi i e alence [56]. Fu he mo e, sys ems
in eg a ing senso s wi h deep lea ning models, such as a
sma wa ch-based adap i e sys em o mul i-senso y emo ion
de ec ion, ha e a ained an accu acy o 74.30% in iden i-
ying a ousal and alence [57]. In addi ion, sel -supe ised
lea ning has shown obus ness o da a deg ada ion, achie ing
81.00% accu acy in emo ion ecogni ion [58], while emo ion
ecogni ion in olde adul s using LSTM ne wo ks has eached
accu acies o up o 95.00% [59]. Mo eo e , hyb id CNN-
LSTM models ha e demons a ed e icacy wi h p ecision a es
as high as 99.00% [60]. La ge Language Models (LLMs) like
GPT ha e been u ilized o analyzing pa ien na a i es and
emo ion es ima ion, complemen ing senso -based me hods o
psychological heal h assessmen . Fo example, ecen s udies
[61] ha e demons a ed how hese models can p ocess uns uc-
u ed ex da a o de i e insigh s in o emo ional well-being,
he eby en iching he unde s anding o QoL dimensions.
Sel -es eem, which e e s o an indi idual’s sel -accep ance
and sel - ega d, is shaped by pe sonal and cul u al s anda ds
and hei pe cei ed compe ency in essen ial li e domains [62].
T adi ionally, sel -es eem e alua ions ha e elied on sel - epo
ins umen s such as he Rosenbe g Sel -Es eem Scale (RSE)
and he Single I em Sel -Es eem Scale (SISE) [63], [64].
Ins umen s like he Mul idimensional Sel -Es eem In en o y
(MSEI) and he Con ingency o Sel -Wo h Scale (CSWs)
ocus on speci ic dimensions o sel -es eem [65], [66]. How-
e e , wea able echnology p esen s inno a i e ye complex
possibili ies o measu ing sel -es eem. A no el me hod u il-
ising EEG da a and CNN models has achie ed an accu acy
exceeding 79.00% in di e en ia ing be ween high and low
sel -es eem [67]. Al hough CNN-LSTM models demons a e
g ea accu acy in emo ion ecogni ion, hei lack o explain-
abili y hinde s heal hca e p ac i ione s om us ing and im-
plemen ing hese me hods in p ac ice. The opaque na u e o
deep lea ning models hinde s he in e p e abili y o ou comes,
pa icula ly in sensi i e domains like psychological heal h,
whe e p ac i ione s equi e clea and comp ehensible insigh s
o in o med decision-making. Explainable AI (XAI) models
a e necessa y o o e come hese conce ns and enhance us in
such echnologies o clinical applica ion.
Spi i uali y, which encompasses he acknowledgmen o a
highe o ce and he pu sui o meaning beyond senso y
expe ience, poses unique challenges o echnological quan-
i ica ion [68]. Ins umen s such as he Spi i ual Well-Being
Scale (SWBS) [69], he Spi i ual Needs Ques ionnai e (SpNQ)
[70], and he Spi i uali y Ques ionnai e [71] a e commonly
employed o e alua e spi i ual well-being. Despi e he p omise
o e ed by wea able senso s and deep lea ning o quali y
o li e assessmen s, he subjec i e and con ex ual na u e o
spi i uali y p esen s conside able obs acles, as physical da a
may inadequa ely ep esen spi i ual expe iences.
Thinking, comp ising undamen al men al p ocesses such as
pe cep ion, memo y, p oblem-sol ing, and decision-making, is
i al o nume ous aspec s o li e, including emo ional con ol
and communica ion. Recen ad ancemen s in DL ha e acili-
a ed he classi ica ion o cogni i e s a es h ough wea able de-
ices. Fo example, in eg a ing EEG da a wi h CNN models has
achie ed an accu acy o up o 96.70% in classi ying cogni i e
wo kload in d i e s [72]. Simila ly, DL app oaches employing
EEG and eye- acking da a ha e shown g ea accu acy (up o
97.00%) in iden i ying cogni i e e o and men al bu den [73].
These me hodologies, despi e acing obs acles, exhibi g ea
po en ial o enhancing cogni i e e alua ion and, consequen ly,
QoL.
Body image e e s o an indi idual’s cogni i e and emo ional
pe cep ions ega ding hei physique, encompassing elemen s
such as o m, size, and a ac i eness [74]. While wea able
de ices like sma wa ches and ac i i y acke s can ga he
da a on physical me ics such as blood p essu e and bodily
mo emen s [75], hey a e limi ed in hei abili y o encapsula e
he in ica e, subjec i e aspec s o body image, including
body accep ance and sel -pe cep ion [76]. A comp ehensi e
e alua ion o body image necessi a es an amalgama ion o
objec i e me ics and sel - epo ed ins umen s, including he
Body-Image Accep ance and Ac ion Ques ionnai e [77] and he
Body Image Scale [78]. By in eg a ing hese di e se elemen s,
we can gain a mo e nuanced unde s anding o psychological
heal h and i s impac on o e all quali y o li e.
Fo a comp ehensi e summa y o s udies employing wea -
able de ices o psychological heal h assessmen , we e e
eade s o Table II in he supplemen a y ma e ial.
IV. PUBLICLY AVAILABLE DATASETS
This sec ion discusses he s eng hs and weaknesses o
da ase s ela ed o QoL subdomains ha include wea able
senso da a and a e publicly accessible. Table 1 highligh s
signi ican a ia ion in pa icipan da a, wi h sample sizes
anging om 4 (”OPPORTUNITY”) o 700 (”Ne Heal h”) and
ages spanning 18 o 78 yea s, as seen in he ”Sleep-EDF”
da ase . Such di e si y enhances he gene alizabili y o indings
ac oss age coho s. Gende dis ibu ion also a ies; o ins ance,
”BioVid Hea Pain” includes 43 emales and 44 males, while
”MHEALTH” lacks gende -speci ic da a. Demog aphic di e -
si y aids in unde s anding how ac o s like age and gende
in luence QoL assessmen s h ough wea able da a [105].
The da ase s encompass a wide a ay o s imuli and ac i i-
ies, demons a ing he adap abili y o wea able echnology in
e alua ing a ious ace s o daily li e. Fo ins ance, ”Ex a-
senso y” assesses 51 beha io al ac i i ies, whe eas ”MIT/BIH
PSG” concen a es on o e nigh sleep eco dings. Nume ous
da ase s, like ”UCI-HAR,” ”WISDM,” and ”PAMAP,” ocus
on ADL, ende ing hem especially pe inen o quali y o
li e e alua ions in his subdomain. In con as , da ase s such
as ”MIT/BIH PSG” and ”Sleep-EDF” ocus on sleep- ela ed
s imuli, co esponding o he Ene gy and Fa igue, and Sleep
(EF/Sleep) subdomains. This a iabili y enables esea che s o
cus omize hei inqui ies o pa icula aspec s o QoL. Fu -
he mo e, he da ase s employ se e al wea able senso s, such
as accele ome e s (ACC), gy oscopes (GYRO), and elec oca -
diog ams (ECG), o assess quali y o li e (QoL) ho oughly.
The MHeal h da ase in eg a es ACC, GYRO, hea a e (HR),
and ECG da a, ende ing i sui able o assessing ac i i ies o
daily li ing (ADL) in QoL esea ch. Likewise, EEG and EMG
da a in ”Sleep-EDFX” and ”MASS” a e cus omized o sleep-
ela ed subdomains. The emo ional aspec s o quali y o li e
Eme ging Topics
TABLE I. Summa y o iden i ied publicly a ailable da ase s con aining wea able da a and s imuli ela ed o QoL domains.
Da ase Subjec s Age Gende
(F/M)
S imuli Wea able da a Subdomain
UCI-HAR [79] 30 19-48 6 ADL ac i i ies ACC, GYRO (50 Hz) ADL
WISDM [80] 36 6 ADL ac i i ies ACC (20 Hz) ADL
PAMAP [81] 9 27.2±3.3 1/8 18 ADL ac i i es ACC, GYRO, HR (100 Hz) ADL
Ex a-senso y [82] 60 18-42 34/26 51 beha iou al ac i i ies ACC, GYRO, MAG (40Hz),
Wa ch ACC (25Hz), GPS,
Audio
ADL
OPPORTUNITY
[83]
4 5 ADL mo ning ac i i-
ies
ACC, GYRO, MAG (30 Hz) ADL
UniMib-SHAR
[84]
30 18-60 6/24 9 ADL ac i i ies, 8 alls ACC (50 Hz) ADL
Daily and Spo
Ac i i ies [85]
8 20-30 4/4 19 ADL and spo s ac-
i i ies
ACC, GYRO, MAG (25 Hz) ADL
REALWORLD16
[86]
15 31.9±12.4 8/7 6 ADL ac i i ies ACC, GYRO, MAG, Loca-
ion, Audio
ADL
MHEALTH [87] 16 12 physical ac i i ies ACC, GYRO, HR, ECG (50
Hz)
ADL
BioVid Hea Pain
[88]
87 20-65 43/44 Hea s imulus ECG, EMG, SCL Pain
EmoPain [89] 50 44
(mean)
29/21 Physio he apy ac i i ies ACC, GYRO, EMG (1 kHz) Pain
MobiAc [90] 57 20-47 15/42 Falls ACC, GYRO (20 Hz) Mobili y
MIT/BIH PSG
[91]
16 32-56 0/16 Whole-nigh sleep
eco dings
EEG, EOG, EMG, BVP,
OS, RS, CV (250 Hz)
EF
FD I&II [92] 61 Whole-day ea ing
episodes
IMU (64 Hz) EF
Sleep-EDF [93] 22 18-78 7/15 Whole-nigh sleep
eco dings
EEG, EOG, EMG (50 Hz) EF/Sleep
Ne Heal h [94] 700 ADL ac i i ies, sleeping
ask
HR, Sleep bioma ke s Sleep
Sleep-EDFX [95] 24 18-79 15/9 Whole-nigh sleep
eco dings
EEG, EOG, EMG (50 Hz) Sleep
MASS [96] 200 18-76 103/97 Whole-nigh sleep
eco dings
EEG, EOG, EMG, ECG, RS
(256 Hz)
Sleep
Apnea ECG [97] 27 27-63 6/21 Whole-nigh sleep
eco dings
ECG (100 Hz) Sleep
eSEE-d [56] 48 18-47 27/21 Emo ion e oking ideos Eye acking me ics Feelings
A ec i eROAD
[98]
10 24-34 5/5 Real wo ld d i ing ses-
sions
BVP, ACC (36 Hz), EDA (4
Hz), HR (1 Hz), ECG, BR,
ST (4 Hz)
Feelings
CASE [99] 30 22-37 15/15 Emo ion e oking ideos ECG, BVP, EMG, EDA
(1000 Hz)
Feelings
CLAS [100] 60 20-50 Emo ion e oking ideos,
men ally demanding
asks
ECG, PPG, EDA, ACC (256
Hz)
Feelings
K-EmoCon [101] 32 19-36 12/20 Na u alis ic
con e sa ions
ECG (1 Hz), EEG (125 Hz),
BVP (64 Hz), EDA (4 Hz),
BT (4 Hz), ACC (32 Hz),
HR (1 Hz)
Feelings
PPG-DaLiA [102] 24 26.9±4.8 14/10 Walking ac i i ies PPG, ECG, ACC, GYRO Feelings
WESAD [103] 15 24-35 3/12 Seden a y ac i i ies BVP (64 Hz), ACC (32 Hz),
EDA (700 Hz), BT (700
Hz),EMG (700 Hz), BR,
ECG (700 Hz)
Feelings
DEAP [104] 32 19-37 16/16 Music ideos EEG (512 Hz) Feelings
ACC: Accele ome e , GYRO: Gy oscope, HR: Hea Ra e, MAG: Magne ome e , GPS: Global Posi ioning Sys em, EEG:
Elec oencephalog am, EOG: Elec ooculog am, EMG: Elec omyog am, BVP: Blood Volume Pulse, OS: Oxygen Sa u a ion, RS:
Respi a ion, CV: Ca dio ascula , ECG: Elec oca diog am, SCL: Skin Conduc ance Le el, EDA: Elec ode mal Ac i i y, PSG:
Polysomnog aphy, BR: B ea hing Ra e, ST: Skin Tempe a u e, BT: Body Tempe a u e, PPG: Pho ople hysmog am, UV: Ul a iole
adia ion.
Eme ging Topics
a e examined in da ase s such as ”eSEE-d” and ”CASE,” which
u ilize emo ion-inducing ilms and eco d physiological signals
like ECG and elec ode mal ac i i y (EDA). The di e si y
and ichness o wea able da a in hese da ase s p o ide a
de ailed examina ion o quali y o li e ac oss many esea ch
equi emen s.
V. CONCLUSION
In conclusion, in eg a ing DL wi h wea able echnology
o e s a p omising app oach o e alua ing QoL, excelling in
domains like physical and psychological well-being. Models
like CNN and LSTM p o ide accu a e insigh s in o daily
ac i i ies, medica ion adhe ence, and men al s a es h ough
eal- ime, objec i e da a o en missed by sel - epo s. DL’s
abili y o p ocess mul imodal senso da a enables comp ehen-
si e, dynamic, and pe sonalized QoL assessmen s. Howe e ,
challenges emain ega ding gene alizabili y, da a a iabili y,
and p i acy. Limi ed da ase s and demog aphic-speci ic s udies
hinde b oade applicabili y, while subjec i e aspec s like body
image and spi i uali y pose in eg a ion di icul ies. Real-wo ld
deploymen aces hu dles like noisy da a, ba e y cons ain s,
and p i acy conce ns.
Looking ahead, inno a ions like explainable AI, ede a ed
lea ning, and edge compu ing p omise mo e anspa en , p i-
a e, and eal- ime wea able da a p ocessing. In e disciplina y
collabo a ion is essen ial o ad ancing DL-d i en QoL e alua-
ions, pa ing he way o ans o ma i e impac s on heal hca e
and well-being.
SUPPLEMENTARY MATERIAL
The Supplemen a y Ma e ials sec ion o he manusc ip
con ains he ex ensi e li e a u e e iew pe o med as well as
indica i e ables o he selec ed s udies. Mo eo e , a com-
p ehensi e discussion on inno a ions, challenges and u u e
p ospec s can be ound a pp.14-15.The documen is a ailable
in IEEE Xplo e unde “media”.
CONFLICT OF INTEREST
The au ho s decla e no con lic o in e es .
ACKNOWLEDGMENT
The s udy was inancially suppo ed h ough he EU Ho izon
2020 p ojec CARDIOCARE (G an ag eemen ID: 945175).
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