Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

Ci a ion: Amo-Salas, J.; Oli a es-Gil, A.; Ga cía-Bus illo, Á.; Ga cía-Ga cía, D.; A naiz-González, Á.; Cubo, E. Compu e Vision o Pa kinson’s Disease E alua ion: A Su ey on Finge Tapping. Heal hca e 2024,12, 439. h ps://doi.o g/10.3390/ heal hca e12040439 Academic Edi o : Tin-Chih Toly Chen Recei ed: 22 Decembe 2023 Re ised: 1 Feb ua y 2024 Accep ed: 6 Feb ua y 2024 Published: 8 Feb ua y 2024 Copy igh : © 2024 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). heal hca e Re iew Compu e Vision o Pa kinson’s Disease E alua ion: A Su ey on Finge Tapping Ja ie Amo-Salas 1, Alicia Oli a es-Gil 1, Ál a o Ga cía-Bus illo 2, Da id Ga cía-Ga cía 1, Ál a A naiz-González 1,* and Es he Cubo 3 1Escuela Poli écnica Supe io , Depa amen o de Ingenie ía In o má ica, Uni e sidad de Bu gos, 09001 Bu gos, Spain; dga [email p o ec ed] (D.G.-G) 2Facul ad de Ciencias de la Salud, Depa amen o de Ciencias de la Salud, Uni e sidad de Bu gos, 09001 Bu gos, Spain; [email p o ec ed] 3Se icio de Neu ología, Hospi al Uni e si a io de Bu gos, 09006 Bu gos, Spain *Co espondence: [email p o ec ed] Abs ac : Pa kinson’s disease (PD) is a p og essi e neu odegene a i e diso de whose p e alence has s eadily been ising o e he yea s. Specialis neu ologis s ac oss he wo ld assess and diagnose pa ien s wi h PD, al hough he diagnos ic p ocess is ime-consuming and a ious symp oms ake yea s o appea , which means ha he diagnosis is p one o human e o . The pa ial au oma iza ion o PD assessmen and diagnosis h ough compu a ional p ocesses has he e o e been conside ed o some ime. One well-known ool o PD assessmen is inge apping (FT), which can now be assessed h ough compu e ision (CV). A i icial in elligence and ela ed ad ances o e ecen decades, mo e speci ically in he a ea o CV, ha e made i possible o de elop compu e sys ems ha can help specialis s assess and diagnose PD. The aim o his s udy is o e iew some ad ances ela ed o CV echniques and FT so as o o e insigh in o u u e esea ch lines ha echnological ad ances a e now opening up. Keywo ds: Pa kinson’s disease; inge apping; machine lea ning; compu e ision 1. In oduc ion In 2015, he Global Bu den o Disease s udy es ima ed ha neu ological diso de s a e he leading cause o disabili y wo ldwide. The incidence and p e alence o neu odegene a- i e diseases such as Pa kinson’s disease (PD) inc ease conside ably wi h age. PD is he second mos common neu odegene a i e disease wo ldwide. Acco ding o he li e a u e, he numbe o cases es ima ed be ween 1990 and 2015 doubled, a ec ing 6.2 million people wo ldwide, a igu e ha is likely o double again by 2040 [1]. PD is cha ac e ized by p og essi e p ima y mo o disabili ies ollowing he degene a- ion o he dopamine gic neu ons loca ed in he subs an ia nig a and associa ed a eas o he b ain [ 2 ]. The pa hophysiology o PD in ol es signa u e abno mali ies in se e al pa allels and la gely seg ega ed basal ganglia halamoco ical ci cui s (i.e., he mo o ci cui ). The a ailable e idence sugges s ha he a ied mo emen diso de s esul ing om dys unc- ions wi hin ha ci cui esul om he p opaga ed dis up ion o downs eam ne wo k ac i i y in he halamus, co ex, and b ains em, and neu o ansmi e s, including dopamine, ace ylcholine, no ad enaline, and se o onin [3,4]. Al hough PD is mainly associa ed wi h mo o symp oms, cha ac e ized by he p es- ence o mo o asymme y wi h b adykinesia (slowness), igidi y, es ing emo , and gai di icul ies wi h pos u al ins abili y [ 5 ], i is also accompanied by o he non-mo o symp- oms such as cogni i e impai men , beha io al dis u bances, sleep diso de s, hyposmia o au onomic dys unc ion, among o he s [ 6 , 7 ]. As a esul , PD is a highly he e ogeneous disease, bo h wi h espec o i s symp oms and i s p og ession o e ime [8]. The e is no cu e o PD, bu pha macological and non-pha macological ea men s a e a ailable, p o iding symp oma ic elie and imp o ing he quali y o li e. In ha ega d, Heal hca e 2024,12, 439. h ps://doi.o g/10.3390/heal hca e12040439 h ps://www.mdpi.com/jou nal/heal hca e Heal hca e 2024,12, 439 2 o 14 le odopa is he mos e ec i e medica ion a ailable o ea ing he mo o symp oms o PD, bu in ce ain ins ances, i can be associa ed wi h o he dopamine gic and non-dopamine gic d ugs [ 9 ]. O he non-pha macological ea men s include deep b ain s imula ion o he sub halamic nucleus o he in e nal globus pallidum and physical, occupa ional, and neu opsychological in e en ions. Ea ly diagnosis o PD has p o ound implica ions o pa ien s and hei amilies, and despi e impo an ad ances, i is s ill a challenge [ 10 ]. Recen de elopmen s include he alida ion o modi ied clinical diagnos ic c i e ia, he in oduc ion and es ing o esea ch c i e ia o p od omal Pa kinson’s disease, and he iden i ica ion o gene ic sub ypes and a g owing numbe o biological bioma ke s associa ed wi h Pa kinson’s disease isk [ 10 ]. In his ega d, he In e na ional PD and Mo emen Diso de Socie y (MDS) has published clinical c i e ia o he diagnosis o PD ha a e in ended o use in clinical esea ch and clinical p ac ice. These c i e ia include wo le els o ce ain y: clinically es ablished PD (maximizing speci ici y, bu wi h educed sensi i i y) and p obable PD (balancing sensi i i y and speci ici y) [11]. A p esen , he new MDS- e ised e sion o he Uni ied Pa kinson’s Disease Ra ing Scale (MDS-UPDRS) is used o a e he p og ess o PD. I comp ises ou pa s: he non- mo o aspec s o daily li e expe iences (Pa I); he mo o aspec s o daily li e expe iences (Pa II); he mo o examina ion (Pa III); and, inally, he mo o complica ions (Pa IV) [ 12 ]. Howe e , adminis e ing he ull scale is e y ime-consuming, so in o de o op imize ime in clinical p ac ice, se e al au ho s ha e ied o de elop sho e a ing scales o o use speci ic i ems om Pa III o he MDS-UPDRS [13,14]. Among he mo o ea u es associa ed wi h PD, b adykinesia, cha ac e ized by hy- pokinesia (i.e., educed mo emen ampli ude, hesi a ions/hal s, and sequence e ec ) has a signi ican impac on PD- ela ed disabili y. Finge apping (FT), one i em o he mo o examina ion included in he MDS-UPDRS, is a es in which he pa ien is asked o ap hei index inge on hei humb as apidly as possible, sepa a ing bo h inge s as much as possible. FT seems o be one o he mos sensi i e i ems, so i can be used o c ea e a as clinical judgmen o mo o s a us. Consequen ly, he FT es could po en ially be used as he gold s anda d o ideo-based analysis [ 15 ]. Howe e , in e p e ing objec i e b adykinesia da a, ob ained h ough kinema ic echniques, is an especially challenging ask, pa icula ly when u ilized o diagnos ic pu poses [16]. In ecen yea s, he e ha e been emendous de elopmen s in he ield o echnologies which, coupled wi h he imp o ed capabili ies o machine lea ning (ML) algo i hms, has led o inc eased esea ch ac i i y on he au oma ic moni o ing o PD mo o symp oms, he moni o ing o he hands being o pa icula in e es [ 17 ]. Recen publica ions ha e explo ed he use o AI in he diagnosis, p og ession, and assessmen s o PD mo o and non-mo o symp oms; howe e , he e is limi ed esea ch on he applica ion o AI o ideo mo ion analyses. In his e iew, he aim is o highligh de eloping uses o AI-based echnology o ideo mo ion analysis o hand mo emen s, so as o acili a e he diagnosis, managemen , and empowe men o pa ien s and o supe ise he p og ession o hei disease and hei esponse o medica ion. A e his in oduc ion, he emaining sec ions o his pape a e as ollows. Fi s ly, ela ed wo ks wi h au oma ic PD diagnosis a e e iewed in Sec ion 2. Secondly, all he pape s on compu e ision (CV) o PD diagnosis a e ca e ully summa ized in Sec ion 3. Finally, he discussion o he esul s is p esen ed in Sec ion 4, and he main conclusions and u u e lines o esea ch in Sec ions 5and 6 espec i ely. 2. Rela ed Wo ks O e he pas decade, esea che s ha e been ying o de ine a use ul and e icien me hod o PD assessmen . This assessmen could include diagnos ic o /and PD a ings in acco dance wi h UPDRS le els. As p e iously no ed, he ocus o his pape is on publica ions ha use CV and FT; ne e heless, in he cu en sec ion, some o he app oaches a e co e ed in a b ie e iew. One o he ini ial a emp s a diagnosing PD using CV includes he handw i en aces analysis [ 18 ]. In ha es , pa ien s ha e o ill ou spi als and meande s on a piece o pape . Heal hca e 2024,12, 439 3 o 14 Subsequen ly, he empla e and he d awings a e iden i ied and au oma ically spli using image p ocessing echniques; bo h we e compa ed o ea u e ex ac ion. Finally, o PD diagnosis (i.e., bina y classi ica ion), hey used adi ional classi ie s: Naï e Bayes, Op imum- Pa h Fo es , and he Suppo Vec o Machine (SVM) algo i hm wi h a Radial Basis Func ion. His o ically, ano he common app oach o PD assessmen has been he use o ex e nal wea ables [ 19 ] and senso s [ 20 ]. These de ices a e capable o eco ding mo emen - ela ed da a in e ec i e and accu a e ways, ye hey a e a ely used and a e e y expensi e. I is a e y in e es ing line o esea ch, due o he eliabili y o he da a cap u e me hods ha can ex ac solid ea u es on he basis o a pa ien ’s mo emen s. In bo h cases [ 19 , 20 ], adi ional classi ie s, such as SVMs, k -Nea es Neighbo s, and Decision T ees we e used o PD diagnos is (i.e., classi ica ion). Jeon e al. [ 19 ] esea ched he use o a w is wa ch- ype wea able de ice wi h an accele ome e and a gy oscope o cap u e PD pa ien s’ mo emen (accele a ion, angula eloci y, displacemen , and angle) and, a e he classi ica ion p ocess, he au ho s pe o med a compa ison o he UDPRS a ing assigned by wo neu ologis s. Rela ed o Moshko a e al. [ 20 ], hey ocused hei a icle on cap u ing hand mo emen signals om a LeapMo ion senso , which was placed a a dis ance o 15–30 cm om he pa ien ’s hand. Thei main aim was o pe o m PD assessmen based on h ee di e en hand mo emen s: FT, p ona ion–supina ion o he hand, and opening–closing hand mo emen s. In some s udies [ 21 – 23 ], PD se e i y has been assessed h ough ideos, hanks o he echnological ad ances wi h CV sys ems. Fo example, Zhang e al. [ 21 ] ocused on emo s, while Lu e al. [ 22 , 23 ] sough o quan i y he se e i y o PD h ough he analysis o ideos showing pa ien s pe o ming MDS-UPDRS. Zhang e al. [ 21 ] ocused hei esea ch in analyzing emo se e i y. Fo his pu pose, hey used OpenPose [24] o ex ac ing 2D skele on ea u es; a e his s ep, classi ica ion was pe o med using a g aph neu al ne wo k wi h a spa ial a en ion mechanism. They also compa ed he esul s using o he s anda d classi ie s such as decision ee, con olu ional neu al ne wo k, and SVM. Meanwhile, Lu e al. [22,23] de eloped hei own classi ie , called O dinal Focal Double- Fea u es Double-Mo ion Ne wo k. In he p e ious pape [ 22 ], hey only included gai analysis, bu in he second one [ 23 ], FT was also e alua ed. Fo gai analysis, hey used VIBE [ 25 ] ( ideo in e ence o human body pose and shape es ima ion) om ex ac ing he 3D skele on; o FT, hey used he OpenPose [24] de ec ion sys em. A mo e ecen a icle [ 17 ] mus also be highligh ed, which demons a ed he excellen co ela ion be ween da a ex ac ed using CV (mo e speci ically MediaPipe [ 26 ]) and da a cap u ed wi h hand-held accele ome e s. Tha s udy showed ha non-in usi e me hods such as CV can ex ac simila da a o physical de ices. Williams e al. [ 27 ] sough o p o e a co ela i e ela ionship when assessing whe he o no sma phone ideo eco dings could be enough o e alua ing b adikynesia. S anda d sma phone ideo eco dings o pa ien s pe o ming FT we e acked wi h DeepLabCu [ 28 ]. Th ee ea u es such as apping speed, ampli ude, and hy hm we e co ela ed wi h clinical a ings made by 22 mo emen diso de neu ologis s using he Modi ied B adykinesia Ra ing Scale (MBRS) and he Mo emen Diso de Socie y e ision o he MDS-UPDRS. Wi h a simila pu pose in mind, Jabe e al. [ 29 ] sough o show how CV can be a sui able amewo k o PD assessmen . The esea ch showed a way o cap u ing FT mo emen s using CV lib a ies (i.e., LabelImg and YOLO [ 30 ]) and ans o ming hem in o aluable me ics and ea u es ha can help wi h PD diagnos ics. Addi ionally, wi h he ise and democ a iza ion o In o ma ion Technology (IT), o he esea che s ha e app oached PD assessmen on he basis o IT in e ac ions; o example, using mobile de ices [ 31 , 32 ] and web b owse s [ 33 ]. In bo h app oaches, PD pa ien s a e in i ed o en e a gami ied si ua ion and a e p omp ed o pe o m mo emen s, ei he by apping di ec ly on he mobile phone sc een [ 31 , 32 ] o by acking mouse ope a ions and keyboa d inpu s on he web b owse [ 33 ]. Based on hy hm, accu acy, a igue, and eac ion ime, among o he ac o s, da a a e ga he ed o use as inpu o machine lea ning classi ie s pe o ming an assessmen o PD. Rega ding mobile phone usage, some o hese in es iga ions also ook pa ien oice eco dings in o conside a ion [32]. Heal hca e 2024,12, 439 4 o 14 Finally, he use o a CV amewo k o he diagnosis o mo emen diso de s [ 34 ] o speci ically PD [ 35 ] mus be men ioned. In bo h pape s, he way ha deep-lea ning-based ma ke less mo ion acking echniques can imp o e PD diagnosis and assessmen we e highligh ed. Tien e al. [ 34 ] e iewed and discussed he po en ial clinical applica ions and echnical limi a ions o hese echniques, wi h a ocus on DeepLabCu [ 28 ]. To e alua e he use o DeepLabCu o au oma ed mo emen diso de disease assessmen , hey buil a mobile ame wi h h ee synch onized came as o eco ding hand mo emen s, including heal hy con ol subjec s and mo emen diso de pa ien s wi h a ious diagnoses, such as PD and essen ial emo . In his case, au ho s sha e he u ili y o DeepLabCu , men ion- ing h ee ongoing s udies, bu wi hou p o iding addi ional de ails. On he o he hand, Sibley e al. [35] published an a icle whe ein hey e iewed he echniques, so wa e li- b a ies, and comme cial app oaches o ideo analyses o PD. Addi ionally, hey iden i ied challenges and possible solu ions associa ed wi h a ing mo o symp oms o PD using ideo. As i has been p e iously no ed, adi ional classi ie s ha e been e alua ed his o ically o app aising models’ pe o mance and o ca ying ou s a is ical compa ison wi h p e i- ous a icles. To p o ide a summa y iew, Table 1shows he classi ie s which ha e been used in he a icles men ioned in his sec ion. A e a i s insigh , decision ee, andom o es , and SVM (in i s di e en a ia ions) seem o be he mos used classi ie s ac oss he dissec ed a icles. The use o ad hoc algo i hms by some au ho s is also wo h no ing. Table 1. Summa y o he ela ed wo ks and he classi ie s used in each s udy. When mo e han one classi ie was used, he one ha achie ed he bes pe o mance is highligh ed in bold (SVM: suppo ec o machine). Re . Yea B ie Desc ip ion Classi ie s [18] 2016 Handw i en aces Naï e Bayes Op imum-Pa h Fo es SVM-R [19] 2017 T emo se e i y analysis using a w is wa ch- ype wea able de ice Decision ee SVM-L SVM-P SVM-R k-nea es neighbo s Disc iminan Analysis [31] 2018 On-sc een apping on a mobile phone (iPhone app) Logis ic eg ession Random o es Deep Neu al Ne wo k Con olu ional Neu al Ne wo k [32] 2018 PD- ela ed ac i i ies ( oice, inge apping, gai , balance, and eac ion ime) assessmen (And oid app) Machine-Lea ning algo i hm [36] [20] 2019 Hand mo emen s da a cap u ing using LeapMo ion senso Decision ee SVM k-nea es neighbo s Random o es [22] 2020 Gai analysis using ideos O dinal Focal Double-Fea u es Double-Mo ion Ne wo k [23] 2021 Gai and FT analysis using ideos O dinal Focal Double-Fea u es Double-Mo ion Ne wo k [21] 2022 T emo se e i y analysis using ideos G aph Neu al Ne wo k Decision ee Con olu ional Neu al Ne wo k SVM [33] 2023 Gami ied websi e acking keyboa d and mouse inpu s Random o es Decision ee SVM Mul ilaye pe cep on 3. Finge Tapping and Compu e Vision on Pa kinson’s Disease E alua ion In ecen yea s, sui able me hods ha e been p oposed in se e al wo ks o imp o ing he diagnosis and/o he PD a ing using CV and FT. In his sec ion, he mos in e es ing Heal hca e 2024,12, 439 5 o 14 s udies on CV and FT a e b ie ly e iewed and compa ed. To do so, a s a e-o - he-a e iew is conduc ed wi h pape s published no ea lie han 2014 (e.g., [ 37 ]). As shown in Table 2, mos o he pape s we e w i en o e he pas 4 yea s. The e a e wo main easons o his: i s o all, he imp o emen in CV- ela ed de ices (came as, sma phones, e c.) o e s use ul capabili ies a a o dable cos s; secondly, he inc easingly e ec i e pe o mance and accu acy o de ec ion and pose es ima ion lib a ies [24,26,38] also make hem e ec i e choices. All he s udies sha e common poin s (see Figu e 1): i s o all, he humb and he index inge s a e au oma ically iden i ied o pe o m ea u e ex ac ion, be o e a da ase is compiled. Then, one o mo e algo i hms a e ained o p oduce a model, which can inally be used o es ing wi h unseen ins ances/examples. Shoo ing and collec ion o ideo ins ances Landma k de ec ion by compu e ision Fea u e ex ac ion - Ampli ude - F equency/Rhy hm - Speed - Fa igue - e c. Fo each ame Cons uc ion o ain and es da ase s Machine Lea ning model T aining and alida ion Figu e 1. Gene al p ocess o he de ec ion o PD using CV and FT. Mos o he wo ks a e in ended o sol e classi ica ion p oblems: a ew o hem ace he simples p oblem (bina y classi ica ion, i.e., ei he has PD o no PD), whe eas mos o hem a e designed o p edic a class among mo e han wo alues (mul iclass classi ica ion); o ha pu pose, he common app oach is o p edic he UPDRS a ing. The dis ibu ion be ween classes is une en (bo h o bina y and mul iclass classi ica ion pape s), and a summa y o he numbe o ideos o each class is shown in Figu e 2. I mus be no ed ha UPDRS 3 and 4 le el se e i y a e especially unde ep esen ed in mos o he wo ks. The main cha ac e is ics o he esea ch pape s unde analysis a e summa ized in Table 2. Heal hca e 2024,12, 439 6 o 14 Table 2. Summa y o he main cha ac e is ics o he compu e ision (CV)- ela ed pape s e iewed in his esea ch. In he s udies wi h mo e han one classi ie , he one ha achie ed he bes pe o mance is highligh ed in bold (PD: Pa kinson’s disease, HC: heal hy con ol, AUC: a ea unde he ROC, SVM: suppo ec o machine). Re . Yea Cap u e Finge Iden i ica ion Classi ie s ML P oblem Da ase Pe o mance Measu es [37] 2014 2D OpenCV SVM-PUK Bina y Mul iclass (3) 13 PD 6 HC Accu acy [39] 2019 2D Con olu ional Neu al Ne wo k Naï e Bayes Logis ic eg ession SVM-L SVM-R Bina y 20 PD 15 HC Accu acy Sensi i i y Speci ici y AUC [40] 2019 3D Cus om-made acke s A i icial neu al ne wo k SVM Bina y 16 PD 14 HC Accu acy Sensi i i y Speci ici y [41] 2021 2D OpenPose Con olu ional Neu al Ne wo k Mul iclass (5) 157 PD 0 HC Accu acy AUC P ecision Recall F1-sco e [42] 2021 2D Single Sho Mul iBox De ec o (SSD) + OpenPose Logis ic Reg ession Naï e Bayes Random o es Bina y 22 PD 20 HC Accu acy AUC [43] 2021 2D OpenPose SVM-R Mul iclass (5) 55 PD 0 HC Weigh ed κ In aclass co . coe . [44] 2022 2D MMPose Deep Neu al Ne wo k Mul iclass (4) 300 PD 0 HC P ecision Recall F1-sco e [45] 2022 2D MediaPipe Fully Connec ed Ne wo k Mul iclass (5) 93 PD 27 HC Accu acy P ecision Recall F1-sco e [46] 2022 3D Spa ial-Tempo al Ancho - o-Join Reg ession Ne wo k (ST-A2J) k-nea es neighbo s Random o es XGBoos SVM-L SVM-R Mul iclass (5) 48 PD 11 HC Accu acy [47] 2023 3D MediaPipe k-nea es neighbo s Random Fo es XGBoos SVM Bina y 35 PD 60 HC Accu acy P ecision Recall F1-sco e Heal hca e 2024,12, 439 7 o 14 123 163 101 0 0 70 397 196 67 14 917 25 32 27 99 247 229 36 0 54 115 52 30 2 0 26 19 38 7 0 50 100 150 200 250 300 350 400 UPDRS 0 UPDRS 1 UPDRS 2 UPDRS 3 UPDRS 4 Numbe o ideos Se e i y Khan e al., 2014 Li e al., 2021 Pa k e al., 2021 Yang e al., 2022 Li e al., 2022 Guo e al., 2022 Figu e 2. Ba plo wi h he numbe o ideos o each class (se e i y o UPDRS) o he pape s ha pe o m mul iclass classi ica ion. Re e ences o he pape s p esen ed in he igu e: Khan e al. [ 37 ], Li e al. [41] , Pa k e al. [43], Yang e al. [44], Li e al. [45], and Guo e al. [46]. 3.1. Fea u e Ex ac ion As migh be expec ed, he p e e ence o deep neu al ne wo ks o CV [ 48 ] mean ha hey we e used o inge iden i ica ion in all he s udies. The mos popula ones a e lis ed below: • Mediapipe [ 26 ] is an open-sou ce amewo k, de eloped by Google, which p o ides eal- ime p ocessing o mul imedia da a, including ideo and audio. I includes se e al modules o CV asks, including pose es ima ion, ace de ec ion, hand de ec ion, and objec acking. • Openpose [ 24 ], de eloped by he Compu e Vision Cen e a he Au onomous Uni- e si y o Ba celona, was eleased in 2016. I is a eal- ime mul i-pe son human pose de ec ion lib a y wi h he capabili y o join ly de ec ing he human body, oo , hand, and acial keypoin s on single images. • MMPose [ 38 ] is an open-sou ce oolbox o pose es ima ion based on PyTo ch. I suppo s: mul i-pe son human pose es ima ion, 133 keypoin whole-body human pose es ima ion, hand pose es ima ion, and 3D human mesh eco e y. The inc easing use o Mediapipe mus be no ed in some o he mos ecen s udies ha we e e iewed [ 17 , 45 , 47 ], eplacing OpenPose [ 24 ], which was he mos widely used in p e ious yea s [41–43]. The mos common ea u es used in he esea ch pape s a e summa ized in Table 3. One o he s udies [ 41 ] was in en ionally excluded om he able, due o a lack o in o ma ion on he opic. Mo eo e , ea u es used in no mo e han one pape we e no included. A his poin , i is impo an o men ion he linguis ic disc epancies be ween he no a ions in he di e en wo ks, as well as he ag eemen s ha we e eached o he pu poses o his s udy on co ec speci ica ion o he ea u es ha appea in he able. • Ampli ude and Speed: he wo mos common ea u es o be analyzed o PD as- sessmen using FT. Ne e heless, hey a e no conside ed in qui e he same way in all wo ks, al hough he e a e no seman ic di e ences ega ding he way ha hose ea u es a e o be unde s ood in FT: –Ampli ude: dis ance be ween humb and index inge s. –Speed: ampli ude di e ence o e ime. Fo example, a common app oach is o ob ain he alues du ing he ime se ies, bu o he au ho s also compu e he mean o maximum alue, a maximum alue du ing he opening o he closing phases, minimum, s anda d de ia ion, e c. In o he wo ds, Heal hca e 2024,12, 439 8 o 14 once he ea u e is conside ed, se e al me ics could be ex ac ed, which will ob iously di e ac oss he di e en s udies. • Fa igue: his ea u e is e alua ed in ew a icles, ye he app oaches used o i s es ima ion a y. I should be no ed ha i is no a physical alue, such as ampli ude and speed. The concep i sel is simila in he di e en a icles, bu essen ial nuances in i s es ima ion we e iden i ied. Fo example: –Di e ence be ween he highes and he lowes alues o ampli ude peaks [42]. –G adien in ampli ude acco ding o ime [43]. –O he au ho s [37] e alua ed a igue on he basis o di e en measu es: *Di e ence be ween numbe o aps in wo ime slo s. *Va ia ion coe icien (VC) in apping speed. * Di e ence be ween he a e age/VCs maximum ampli ude o inge aps in wo ime slo s. *VC in he maximum ampli ude o inge aps. *Tapping accele a ion. • F equency/Rhy hm: wi hou a doub , he mos abs ac ea u e. Bo h concep s a e used indis inc ly, bu no always o ep esen ing he same concep : – In some s udies [ 17 , 27 ], i s calcula ion is based on unde aking Fas Fou ie T ans o m. – Ano he common app oach [ 37 , 39 ] is o use a ea u e called “c oss-co ela ion be ween he no malized peaks” (CCNP) o es ima ing consis ency and hy hm in apping. – Buongio no e al. [ 40 ] used he a e aged alue o he di ision be ween he ampli- ude peak eached in a single exe cise ial and he ime du a ion o he ial. Table 3. Summa y o inge apping ea u es used in he CV pape s e iewed in his esea ch. The symbol (✓) ep esen s ha he ea u e is used in he wo k. Fea u e [37] [39] [40] [27] [42] [43] [29] [44] [45] [46] [17] [47] Ampli ude ✓✓✓✓✓✓✓✓✓✓✓✓ F equency/Rhy hm ✓ ✓ ✓ ✓ ✓ ✓ Speed ✓ ✓✓✓✓✓✓✓✓ ✓ Fa igue ✓ ✓ ✓ 3.2. Classi ie s As p e iously men ioned, all he pape s ha we e e iewed had he common aim o pe o ming a classi ica ion p edic ion (bina y o mul iclass). In o he wo ds, he main a ge o he s udies was o implemen a PD p edic ion acco ding o UPDRS a ings o FT. In no mo e han a couple o s udies [ 37 , 44 ] could he i e le els o UPDRS no be p edic ed, due o he lack o enough examples du ing he aining phase. Di e en classi ie s can be ained o pe o m he classi ica ion, once ea u e ex ac ion has been comple ed, wi h SVM [ 49 ] (wi h i s di e en a ian s/ke nels) being by a he mos popula [37,39,40,43,46,47]. B oadly used, especially in ecen yea s, deep neu al ne wo ks can also be applied as classi ie s [ 40 , 44 , 45 ] (no only o inge iden i ica ion) wi h di e en con igu a ions and a ia ions. Ad hoc designs and deploymen s ha e e en been p oposed [41]. On he o he hand, mul i-classi ie s [ 50 ] (a.k.a. ensembles) a e popula , he mos widely used being andom o es (RF) [ 42 , 46 , 47 , 51 ] and XGBoos [ 46 , 47 , 52 ]. Las bu no leas , con en ional classi ie s (such as Naï e Bayes [ 39 , 42 ], k -nea es neighbo s [ 46 , 47 ], and logis ic eg ession [39,42]) ha e commonly been used as baselines. No insigh could be gi en in o which classi ie was he bes o he ask o FT classi i- ca ion wi hou u he expe imen a ion, due o he di e ences be ween he expe imen al se ups, he da ase s, he classi ica ion asks, and he classi ie s ha we e used. Heal hca e 2024,12, 439 9 o 14 3.3. Da ase s One o he main p oblems ha se e ely complica es compa isons o he p oposals is he lack o benchma k da ase s. A pa icula da ase is used in e e y single s udy, usually con aining small numbe s o indi iduals: he smalles included 11 while he la ges had 300. I mus be no ed ha i can be ex emely di icul o assess he pe o mance o he p oposals wi h such small-sized samples. Deep analysis o his opic e eals g ea a iabili y. Commonly, mos o he esea ch pa- pe s include PD pa ien s and heal hy con ols (HCs). The p oblem he e is ha , some imes, he e a e la ge di e ences ela ed o class p opo ions. I is well known ha imbalanced da ase s (i.e., when a class is unde - ep esen ed) a e challenging, as algo i hms will in a i- ably igno e he unde ep esen ed class/es. The class p opo ion is usually measu ed by means o he imbalance a io (IR) [53]. IR = Numbe o majo i y Numbe o mino i y In his way, da ase s can be u he classi ied: • Fai ly balanced da ase s ( IR < 2): he p opo ions be ween Pa kinson’s disease pa ien s and heal hy con ols a e balanced [27,39,40,42,47]. • Imbalanced da ase s ( IR > 2): he p opo ions a e su icien o ake in o accoun he imbalance p oblem [37,45,46]. In some s udies, only ideos om PD pa ien s we e aken in o conside a ion [ 17 , 41 , 43 , 44 ]. The e was no a emp o dis inguish be ween PD pa ien s and heal hy con ols, bu only he di e ences be ween pa ien s we e conside ed, e.g., o a e hem acco ding o UPDRS. 3.4. Measu es A e analyzing he a icles, simila pe o mance measu es based on ML asks we e used. B ie explana ions appea below alongside no es on hei use in he pape s ha we e e iewed. • Accu acy: by a he mos common measu e [ 37 , 39 – 42 , 45 – 47 ], he ounda ion and common unde s anding o i s meaning is wha makes accu acy so popula (e.g., he numbe o success ul ou comes di ided by he o al numbe o examples). E en a non- amilia eade could de e mine he achie emen le el by in e p e ing he accu acy pe cen age. Howe e , his measu e also has some d awbacks; a common complain abou accu acy is ha i ails when he classes a e imbalanced. • A ea Unde ROC Cu e (AUC): also conside ed a popula measu e o classi ica ion p oblems [ 39 , 41 , 42 ]. I is commonly used in ML and da a analy ics o assess he pe o mance o models a p edic ing bina y ou comes and, in con as o s anda d accu acy, i is pa icula ly use ul when p ocessing imbalanced da ase s, whe e accu a e p edic ion o mino i y classes is o high impo ance. •F1 -sco e [ 41 , 44 , 45 , 47 ] is he ha monic mean o p ecision ( he numbe o ue posi i e di ided by he p edic ed as posi i es) and ecall ( he numbe o ue posi i es di- ided by he numbe o all samples o he class o in e es ). F1 -sco e gi es he same impo ance o bo h p ecision and ecall, wha can be conside ed as i s main d aw- back [ 54 ]. None heless, in eal-wo ld p oblems usually di e en cos s a e associa ed o di e en e o s. 4. Discussion Fi s o all, as p e iously s a ed, PD assessmen is s ill a majo challenge o clinical neu ologis s. Few o he p oposed solu ions ha e been ce i ied by he Mo emen Diso de Socie y o UPDRS a ing. Despi e he ac ha his ce i ica ion is mean o p o ide a common amewo k and o objec i y a complex scena io, some oices a e skep ical o i s gene alized use [ 55 ]. Fi s ly, p e ious knowledge is usually necessa y as well as a case s udy wi h some clues on pa ien e olu ion, amily backg ound, and so on [ 39 , 42 ].