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Can Virtual Reality be used as a significant stressor for studies using ECG?

Scherz, Wilhelm Daniel; Corcoba Magaña, Víctor; Seepold, Ralf; Martínez Madrid, Natividad; Ortega Ramírez, Juan Antonio

Abstract

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.

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ScienceDi ec A ailable online a www.sciencedi ec .com P ocedia Compu e Science 176 (2020) 3255–3262 1877-0509 © 2020 The Au ho s. Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0) Pee - e iew unde esponsibili y o he scien i ic commi ee o he KES In e na ional. 10.1016/j.p ocs.2020.09.123 10.1016/j.p ocs.2020.09.123 1877-0509 © 2020 The Au ho s. Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0) Pee - e iew unde esponsibili y o he scien i ic commi ee o he KES In e na ional. A ailable online a www.sciencedi ec .com ScienceDi ec P ocedia Compu e Science 00 (2020) 000–000 www.else ie .com/loca e/p ocedia 1877-0509 © 2019 The Au ho (s). Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/) Pee - e iew unde esponsibili y o KES In e na ional. 24 h In e na ional Con e ence on Knowledge-Based and In elligen In o ma ion & Enginee ing Sys ems Can Vi ual Reali y be used as a signi ican s esso o s udies using ECG? Wilhelm Daniel Sche za,*, Vic o Co coba Magañab, Ral Seepolda,c, Na i idad Ma ínez Mad idc,d Juan An onio O egae aUbiqui ous Compu ing Lab HTWG Kons anz, Al ed-Wach el-S . 8, 78462 Kons anz, Ge many bUni e si y o O iedo, O iedo 33003, Spain cI.M. Secheno Fi s Moscow S a e Medical Uni e si y, 2-4, Bolshaya Pi ogo skaya s ., 119435 Moscow, Russian Fede a ion dReu lingen Uni e si y, Al ebu gs . 150, 72762 Reul ingen, Ge many eUni e si y o Se ille, A da. Reina Me cedes s/n, Se ille, Spain Abs ac In p e ious s udies, we used a me hod o de ec ing s ess ha was based exclusi ely on hea a e and ECG o di e en ia ion be ween such si ua ions as men al s ess, physical ac i i y, elaxa ion, and es . As a esponse o he hea o hese si ua ions, we obse ed di e en beha io in he Roo Mean Squa e o he Successi e di e ences hea bea s (RMSSD). This s udy aims o analyze Vi ual Reali y ia a i ual eali y headse as an e ec i e s esso o u u e wo ks. The alue o he Roo Mean Squa e o he Successi e Di e ences is an impo an ma ke o he pa asympa he ic e ec o on he hea and can p o ide in o ma ion abou s ess. Fo hese measu emen s, he RR in e al was collec ed using a b eas bel . In hese s udies, we can obse e he Roo Mean Squa e o he successi e di e ences hea bea s. Addi ional senso s o he analysis we e no used. We conduc ed expe imen s wi h en subjec s ha had o d i e a simula o o 25 minu es using moni o s and 25 minu es using i ual eali y headse .Be o e s a ing and a e inishing each simula ion, he subjec s had o comple e a su ey in which hey had o desc ibe hei men al s a e. The expe imen esul s show ha d i ing using i ual eali y headse has some in luence on he hea a e and RMSSD, bu i does no signi ican ly inc ease he s ess o d i ing. © 2019 The Au ho (s). Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/) Pee - e iew unde esponsibili y o KES In e na ional. * Co esponding au ho . Tel.: +49-7531-206-698; ax: +49-7531-206-8698. E-mail add ess: wsche z@h wg-kons anz.de A ailable online a www.sciencedi ec .com ScienceDi ec P ocedia Compu e Science 00 (2020) 000–000 www.else ie .com/loca e/p ocedia 1877-0509 © 2019 The Au ho (s). Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/) Pee - e iew unde esponsibili y o KES In e na ional. 24 h In e na ional Con e ence on Knowledge-Based and In elligen In o ma ion & Enginee ing Sys ems Can Vi ual Reali y be used as a signi ican s esso o s udies using ECG? Wilhelm Daniel Sche za,*, Vic o Co coba Magañab, Ral Seepolda,c, Na i idad Ma ínez Mad idc,d Juan An onio O egae aUbiqui ous Compu ing Lab HTWG Kons anz, Al ed-Wach el-S . 8, 78462 Kons anz, Ge many bUni e si y o O iedo, O iedo 33003, Spain cI.M. Secheno Fi s Moscow S a e Medical Uni e si y, 2-4, Bolshaya Pi ogo skaya s ., 119435 Moscow, Russian Fede a ion dReu lingen Uni e si y, Al ebu gs . 150, 72762 Reul ingen, Ge many eUni e si y o Se ille, A da. Reina Me cedes s/n, Se ille, Spain Abs ac In p e ious s udies, we used a me hod o de ec ing s ess ha was based exclusi ely on hea a e and ECG o di e en ia ion be ween such si ua ions as men al s ess, physical ac i i y, elaxa ion, and es . As a esponse o he hea o hese si ua ions, we obse ed di e en beha io in he Roo Mean Squa e o he Successi e di e ences hea bea s (RMSSD). This s udy aims o analyze Vi ual Reali y ia a i ual eali y headse as an e ec i e s esso o u u e wo ks. The alue o he Roo Mean Squa e o he Successi e Di e ences is an impo an ma ke o he pa asympa he ic e ec o on he hea and can p o ide in o ma ion abou s ess. Fo hese measu emen s, he RR in e al was collec ed using a b eas bel . In hese s udies, we can obse e he Roo Mean Squa e o he successi e di e ences hea bea s. Addi ional senso s o he analysis we e no used. We conduc ed expe imen s wi h en subjec s ha had o d i e a simula o o 25 minu es using moni o s and 25 minu es using i ual eali y headse .Be o e s a ing and a e inishing each simula ion, he subjec s had o comple e a su ey in which hey had o desc ibe hei men al s a e. The expe imen esul s show ha d i ing using i ual eali y headse has some in luence on he hea a e and RMSSD, bu i does no signi ican ly inc ease he s ess o d i ing. © 2019 The Au ho (s). Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/) Pee - e iew unde esponsibili y o KES In e na ional. * Co esponding au ho . Tel.: +49-7531-206-698; ax: +49-7531-206-8698. E-mail add ess: wsche z@h wg-kons anz.de 3256 Wilhelm Daniel Sche z e al. / P ocedia Compu e Science 176 (2020) 3255–3262 2 Wilhelm Daniel Sche z / P ocedia Compu e Science 00 (2020) 000–000 Keywo ds: S ess de ec ion ; ECG, d i ing ; hea a e ; ca dio ascula ; bio i al signal 1. In oduc ion Awa eness abou heal h is o g ea impo ance in mode n socie y. Collec ing bio i al da a became an essen ial pa o i , and as a esul , i changed he pe cep ion and unde s anding o s ess [1]. Nowadays, he e a e se e al app oxima ions o he es ima ion o s ess. Cu en ly, he mos common app oaches o de ec ing s ess implemen h ee di e en p inciples: The i s one uses su eys o sel - epo ques ionnai es o e alua e he men al s a e; he second app oach uses labo a o y bioma ke s like ad enaline and co isol [2], beha io pa e n analysis [3], and he la e app oach uses physiological pa ame e s like hea a e o skin conduc i i y [4] o s ess de ec ion. The app oach ha was used in his expe imen uses a educed da a se ha sepa a es be ween mo e o less s ess while using he simula ion ia moni o s o i ual eali y headse . The main challenges o de ec ing s ess belong no only o i s de ec ion i sel bu also i s sepa a ion om o he si ua ions like elaxa ion, physical ac i i y. Cu en ly, he e is a big choice o consume de ices and po able EGC de ices ha measu e hea a e and moni o ac i i y, which could be used o s ess de ec ion. Ne e heless, he challenge o sepa a ion emains. I is well known ha he e a e some long- e m e ec s o s ess on he ca dio ascula sys em [5, 6, 7], s ess a ec s he immune esponse o he body [8], i can igge panic a acks [9], and i lowe s he li e quali y [10, 11]. Addi ionally, s ess makes i di icul o cope wi h physical and men al demands [12, 13, 14], and i has a ha m ul in luence on he de elopmen o b ain s uc u es [10]. Simpli ying, s ess could be conside ed o be a nega i e in luence on heal h and li e quali y, so i is impo an o p ese e and quan i y i and educe he amoun o s ess ha we expe ience. As an addi ional complica ion, we can name he misde ec ion o physical ac i i y as s ess. By con as wi h s ess, physical ac i i y has posi i e long- e m e ec s such as change o he lipase composi ion o he body [15] and imp o emen s in he ca dio ascula sys em. Like s ess, physical ac i i y has sho - e m e ec s. As we could obse e in p e ious wo ks, many addi ional s esso s o in luence s change he hea 's beha io . As an example, elaxa ion changes he hea a e and sys olic blood p essu e [16, 17]. This makes i essen ial o ind s esso s ha we can use o collec di e en da ase s and imp o e s ess de ec ion. Fo his wo k, i is essen ial o unde s and and o de ine s ess. In his case, we de ine s ess no as he peak o a d ama ic e en bu as a eac ion o he si ua ions ha equi e a as esponse o ele a ed ac i i y o jus in unusual si ua ions. We can conside ha s ess is a pa o ou daily li e; i is a mechanism ha helps us o e alua e unknown and dange ous si ua ions and gi e as e esponses by inc easing he e iciency o ou eac ions. As an example, in ' igh o ligh ' si ua ions, s ess educes he ime o eac ing and decision-making, and as a consequence, in a dange ous si ua ion, we choose be ween con on a ion o escape [18]. D i ing is a ypical daily ac i i y o many o us. A la ge amoun o s udies shows ha d i ing e iciency and sa e y a e s ongly in luenced by emo ions and s ess le els o he d i e [19, 20, 21] . Se e al s udies p o e a s ong ela ionship be ween a ic acciden s and s ess [22, 23]. Tha is why we ha e chosen his use case o he cu en s udy, as i is a common ac i i y ha is easy and sa e o simula e. 2. Me hods and ma e ials In he ollowing sec ion, we desc ibe he p o ocol ollowed du ing he expe imen and he ma e ials used. To eco d he hea a e, ECG, and s ess, wo de ices we e used: he ches band Pola H101 and he w is band Empa ica E42. Addi ionally, o measu e su ounding condi ions such as empe a u e, humidi y, and C02 le els du ing he expe imen , we applied he Ne a mo3 senso . The en i onmen condi ions we e no used in he measu emen o s ess because he condi ions while he simula ion whe e simila and did no change much. 1 h ps://www.pola .com/de/p oduk e/accessoi es/he z equenz_senso _h10. 2 h ps://www.empa ica.com/ esea ch/e4/ 3 h ps://www.ne a mo.com/en-us/wea he /wea he s a ion/indoo -module Wilhelm Daniel Sche z e al. / P ocedia Compu e Science 176 (2020) 3255–3262 3257 2 Wilhelm Daniel Sche z / P ocedia Compu e Science 00 (2020) 000–000 Keywo ds: S ess de ec ion ; ECG, d i ing ; hea a e ; ca dio ascula ; bio i al signal 1. In oduc ion Awa eness abou heal h is o g ea impo ance in mode n socie y. Collec ing bio i al da a became an essen ial pa o i , and as a esul , i changed he pe cep ion and unde s anding o s ess [1]. Nowadays, he e a e se e al app oxima ions o he es ima ion o s ess. Cu en ly, he mos common app oaches o de ec ing s ess implemen h ee di e en p inciples: The i s one uses su eys o sel - epo ques ionnai es o e alua e he men al s a e; he second app oach uses labo a o y bioma ke s like ad enaline and co isol [2], beha io pa e n analysis [3], and he la e app oach uses physiological pa ame e s like hea a e o skin conduc i i y [4] o s ess de ec ion. The app oach ha was used in his expe imen uses a educed da a se ha sepa a es be ween mo e o less s ess while using he simula ion ia moni o s o i ual eali y headse . The main challenges o de ec ing s ess belong no only o i s de ec ion i sel bu also i s sepa a ion om o he si ua ions like elaxa ion, physical ac i i y. Cu en ly, he e is a big choice o consume de ices and po able EGC de ices ha measu e hea a e and moni o ac i i y, which could be used o s ess de ec ion. Ne e heless, he challenge o sepa a ion emains. I is well known ha he e a e some long- e m e ec s o s ess on he ca dio ascula sys em [5, 6, 7], s ess a ec s he immune esponse o he body [8], i can igge panic a acks [9], and i lowe s he li e quali y [10, 11]. Addi ionally, s ess makes i di icul o cope wi h physical and men al demands [12, 13, 14], and i has a ha m ul in luence on he de elopmen o b ain s uc u es [10]. Simpli ying, s ess could be conside ed o be a nega i e in luence on heal h and li e quali y, so i is impo an o p ese e and quan i y i and educe he amoun o s ess ha we expe ience. As an addi ional complica ion, we can name he misde ec ion o physical ac i i y as s ess. By con as wi h s ess, physical ac i i y has posi i e long- e m e ec s such as change o he lipase composi ion o he body [15] and imp o emen s in he ca dio ascula sys em. Like s ess, physical ac i i y has sho - e m e ec s. As we could obse e in p e ious wo ks, many addi ional s esso s o in luence s change he hea 's beha io . As an example, elaxa ion changes he hea a e and sys olic blood p essu e [16, 17]. This makes i essen ial o ind s esso s ha we can use o collec di e en da ase s and imp o e s ess de ec ion. Fo his wo k, i is essen ial o unde s and and o de ine s ess. In his case, we de ine s ess no as he peak o a d ama ic e en bu as a eac ion o he si ua ions ha equi e a as esponse o ele a ed ac i i y o jus in unusual si ua ions. We can conside ha s ess is a pa o ou daily li e; i is a mechanism ha helps us o e alua e unknown and dange ous si ua ions and gi e as e esponses by inc easing he e iciency o ou eac ions. As an example, in ' igh o ligh ' si ua ions, s ess educes he ime o eac ing and decision-making, and as a consequence, in a dange ous si ua ion, we choose be ween con on a ion o escape [18]. D i ing is a ypical daily ac i i y o many o us. A la ge amoun o s udies shows ha d i ing e iciency and sa e y a e s ongly in luenced by emo ions and s ess le els o he d i e [19, 20, 21] . Se e al s udies p o e a s ong ela ionship be ween a ic acciden s and s ess [22, 23]. Tha is why we ha e chosen his use case o he cu en s udy, as i is a common ac i i y ha is easy and sa e o simula e. 2. Me hods and ma e ials In he ollowing sec ion, we desc ibe he p o ocol ollowed du ing he expe imen and he ma e ials used. To eco d he hea a e, ECG, and s ess, wo de ices we e used: he ches band Pola H101 and he w is band Empa ica E42. Addi ionally, o measu e su ounding condi ions such as empe a u e, humidi y, and C02 le els du ing he expe imen , we applied he Ne a mo3 senso . The en i onmen condi ions we e no used in he measu emen o s ess because he condi ions while he simula ion whe e simila and did no change much. 1 h ps://www.pola .com/de/p oduk e/accessoi es/he z equenz_senso _h10. 2 h ps://www.empa ica.com/ esea ch/e4/ 3 h ps://www.ne a mo.com/en-us/wea he /wea he s a ion/indoo -module Wilhelm Daniel Sche z/ P ocedia Compu e Science 00 (2020) 000–000 3 2.1. Hea a e and ECG signal The hea signal's ad an age is ha i can be acqui ed in a non-in asi e way, and he Hea Ra e Va iabili y (HRV) co ela es wi h s ess. In his s udy, wo senso s we e used o measu e s ess: he ches band Pola H10 and he w is band Empa ica E4. The e a e wo domains o how he hea a e a iabili y can be analyzed: by ime domain and by equency domain. Fo he ime domain analysis, quan i ying he mean and he s anda d de i a ion o wo consecu i e hea bea s in e als has o be done. A hea bea in e al is de ined as RR In e al, and an R peak ep esen s he highes peak o a hea bea on he QRS complex. An RR in e al is he ime be ween wo hea bea s. The used measu emen s o he ime domain a e he ollowing: a e age RR in milliseconds (ms), s anda d de i a ion o consecu i e RR du ing he expe imen (SDNN), a e age hea a e (HR) in ms, s anda d de i a ion hea a e (STD HR), he oo mean squa e o successi e di e ences (RMSSD) and he pNN50 (%) ha indica es he p opo ion o consecu i e RR in e als wi h he di e ence o mo e han 50ms. This alue dec eases when s ess inc eases. Fo he equency domain analysis, we ha e o calcula e he powe o he espi a o y dependen highe equency and he lowe equency componen s o he hea a e. Fo his, we use he ollowing measu emen : LF/HF indica es he a ion o he low- equency powe (0.04-0.15 Hz) ha is modula ed by he sympa ic and pa asympa he ic ne ous sys em and he high- equency powe (0.14-0.4 Hz) ela ed o pa asympa he ic ac i i y. This alue indica es a global sympa ic agal balance [24]. High esul s o LF/HF indica e ha he sympa he ic sys em is dominan . This happens when he e is ele a ed s ess. Nowadays, he e is a ailable a wide ange o de ices ha enable he non-in asi e eco ding o he hea . One o he mos popula me hods ha a e used in wea able de ices is pho o-ple hysmog aphy. This kind o de ice measu es he blood olume pulse. The main d awback o his me hod is he sensi i i y o mo emen and he changes in he de ice's con ac p essu e. [25]. An al e na i e ha also allows he non-in asi e measu emen o he hea a e and, a he same ime, p o ides highe accu acy a e he ches bands wi h elec odes and ECG unc ion. [26] . Pola H10 ha was used o his s udy belongs o he second g oup o he de ices and o e s simila unc ionali y as an ECG Hol e [27]. 2.2. D i ing simula o Fo his s udy, we used he d i ing simula o 'Ci y Ca D i ing' [28]. The main easons o choosing his simula o we e he ollowing: ealis ic d i ing expe ience based on ad anced ca physics, eal a ic ules, adjus able a ic condi ions, ealis ic pedes ian and ca simula ion, and high-quali y g aphics simula o . Fo his expe imen , we ha e chosen he scene 'Old dis ic ' because i cha ac e izes wi h na ow s ee s, simple c ossing, clea a ic pa e ns. The so wa e simula o was execu ed on a d i ing simula o wi h cha ac e is ics, as shown in Table 1. Fo d i ing wi hou i ual Reali y, h ee 27-inch sc eens we e used. To ope a e simula ion and o p o ide a ealis ic d i ing expe ience, we used he s ee ing wheel Logi ech G24 wi h gea box and pedals. This s ee ing wheel also o e s o ce eedback. This allows us o achie e an imme si e expe ience in he i ual en i onmen . Table 1. Speci ica ions o he PC in which he d i ing simula o is un Model Alienwa e A ea-51 R4 P ocesso In el Co e i7-7800X Chipse In el X299 PCH Memo y 16 GB DDR4 2666 MHz GPU 2 X Ge o ce 1080 TI SLI S o age 128 GB SanDisk M.2 SSD The d i ing pe o mance was eco ded wi h a specially de eloped p og am ha cap u es he o a ion o he s ee ing wheel, he p essu e applied on he pedals, and he numbe o a ic in ac ions he es subjec s ealized. 3258 Wilhelm Daniel Sche z e al. / P ocedia Compu e Science 176 (2020) 3255–3262 4 Wilhelm Daniel Sche z / P ocedia Compu e Science 00 (2020) 000–000 2.3. Music empo Music can in luence human beha io . Se e al s udies show ha e.g., as music educes he amoun o ime ha cus ome s spend in supe ma ke s; in pubs, i makes people d ink hei be e ages as e [29, 30] o while d i ing as music can inc ease he eadiness o d i e as e [31]. Fo his s udy, he pa icipan s lis ened o he music using noise-canceling headse s, inc easing he imme sion in o a simula ion, and educing ex e nal in luences. Each pa icipan could con igu e he olume indi idually acco ding o hei pe sonal p e e ences. Two playlis s we e c ea ed o he pa icipan s. The i s playlis consis s o low empo composi ions (65-71 bpm), and he second playlis included songs wi h a as e empo (155-188 bpm). The audio was ep oduced wi h a quali y o 320 bps. Adi ional o he music, he d i e hea s he sounds o he i ual en i onmen o he d i ing simula o . 2.4. Su ey Be o e s a ing o d i e, an ini ial su ey had o be comple ed by he pa icipan s, as well as he second su ey a e d i ing. The i s su ey gi es in o ma ion abou he emo ional s a e, physical s a e, and he expe iences o he pa icipan s. The inal su ey is aimed o collec he da a abou he emo ional and physiological s a e o he pa icipan s a e comple ion o he d i ing exe cise and o obse e i he d i ing ask had any in luence on he d i e . Addi ional ques ions in he inal su ey e e o he ealism o he simula ion, own pe cep ion o he d i ing pe o mance, and he en i onmen pe cep ions such as empe a u e and noise. These addi ional ques ions allow us o compa e hei subjec i e pe cep ion o he si ua ion wi h he measu ed da a and o ind ou whe he he simula o is ealis ic enough and usable as a ool o collec ing da a. 2.5. P ocedu e desc ip ion The s udy is composed o 6 pa s. Fi s , he pa icipan answe s he su ey and places he ches band and w i s band. Then, he pa icipan si s on a sea and places he headse and s a s o lis en o Moza 's Sona a o Two Pianos in D majo . This i s phase is used o elax he pa icipan and s abilize he senso s and de ices. A e his, he pa icipan s a s d i ing o 25 minu es, and he hea signal, en i onmen cha ac e is ics, and d i ing a e eco ded. As soon as he pa icipan s a s d i ing, he sys em p oposes a ou e ha he has o d i e. The p oposed ou e always has a leng h o 5 km. The amoun o a ic is always he same and compa able. A coun e s a s a 20 poin s, o each in ac ion a poin is aken, and i he coun e eaches 0 poin s, he ou e has o be es a ed. A e 25 minu es, he pa icipan can es o 1-2 minu es. A e he pa icipan has se up com o ably and s a ed he VR, he VR headse a new ou e is ini ialized wi h he segmen s o 5 km and 25 minu es. A e his, he pa icipan can es 1-2 minu es and has o answe he inal su ey. 3. Resul s A o al o 10 d i e s wi h an a e age age o 25.4 (max: 33, min: 20; s d. de .: 3.47) and d i ing expe ience o 7 o 8 yea s (max 15, min: 2, s d. de : 3.62) pa icipa ed in he expe imen . Fi s , he pa icipan pu on he senso s, comple ed he ini ial su ey, and hen lis ened o Moza 's Sona a o Two Pianos in D majo using headphones. This ack has been selec ed because i imp o es men al unc ion [32]. The objec i e o his phase is o be elaxed be o e he d i ing es and o s abilize he senso s. Pa icipan s d o e o 25 minu es. The d i e s had o comple e he ou es p oposed by he GPS o he d i ing simula o . Each ou e has a leng h o 5 km, and i s le el o di icul y is compa able because he concen a ion o ehicles and pedes ians is he same in all cases. The d i ing simula o assigns poin s o he pa icipan a he beginning o he ou e. Each ime an in ac ion is commi ed, poin s a e deduc ed. When he sco e is ze o, he o she mus epea he ou e. This allows he pa icipan o ocus on he d i ing ask as i in a eal en i onmen [33]. The d i ing ime is 25 minu es o ensu e ha he e is enough ime o he s ess da a o be alid [34]. A e wa d, he pa icipan s elaxed o 5 minu es and s a ed he same es again, bu in his case, using i ual Reali y. Wilhelm Daniel Sche z e al. / P ocedia Compu e Science 176 (2020) 3255–3262 3259 4 Wilhelm Daniel Sche z / P ocedia Compu e Science 00 (2020) 000–000 2.3. Music empo Music can in luence human beha io . Se e al s udies show ha e.g., as music educes he amoun o ime ha cus ome s spend in supe ma ke s; in pubs, i makes people d ink hei be e ages as e [29, 30] o while d i ing as music can inc ease he eadiness o d i e as e [31]. Fo his s udy, he pa icipan s lis ened o he music using noise-canceling headse s, inc easing he imme sion in o a simula ion, and educing ex e nal in luences. Each pa icipan could con igu e he olume indi idually acco ding o hei pe sonal p e e ences. Two playlis s we e c ea ed o he pa icipan s. The i s playlis consis s o low empo composi ions (65-71 bpm), and he second playlis included songs wi h a as e empo (155-188 bpm). The audio was ep oduced wi h a quali y o 320 bps. Adi ional o he music, he d i e hea s he sounds o he i ual en i onmen o he d i ing simula o . 2.4. Su ey Be o e s a ing o d i e, an ini ial su ey had o be comple ed by he pa icipan s, as well as he second su ey a e d i ing. The i s su ey gi es in o ma ion abou he emo ional s a e, physical s a e, and he expe iences o he pa icipan s. The inal su ey is aimed o collec he da a abou he emo ional and physiological s a e o he pa icipan s a e comple ion o he d i ing exe cise and o obse e i he d i ing ask had any in luence on he d i e . Addi ional ques ions in he inal su ey e e o he ealism o he simula ion, own pe cep ion o he d i ing pe o mance, and he en i onmen pe cep ions such as empe a u e and noise. These addi ional ques ions allow us o compa e hei subjec i e pe cep ion o he si ua ion wi h he measu ed da a and o ind ou whe he he simula o is ealis ic enough and usable as a ool o collec ing da a. 2.5. P ocedu e desc ip ion The s udy is composed o 6 pa s. Fi s , he pa icipan answe s he su ey and places he ches band and w i s band. Then, he pa icipan si s on a sea and places he headse and s a s o lis en o Moza 's Sona a o Two Pianos in D majo . This i s phase is used o elax he pa icipan and s abilize he senso s and de ices. A e his, he pa icipan s a s d i ing o 25 minu es, and he hea signal, en i onmen cha ac e is ics, and d i ing a e eco ded. As soon as he pa icipan s a s d i ing, he sys em p oposes a ou e ha he has o d i e. The p oposed ou e always has a leng h o 5 km. The amoun o a ic is always he same and compa able. A coun e s a s a 20 poin s, o each in ac ion a poin is aken, and i he coun e eaches 0 poin s, he ou e has o be es a ed. A e 25 minu es, he pa icipan can es o 1-2 minu es. A e he pa icipan has se up com o ably and s a ed he VR, he VR headse a new ou e is ini ialized wi h he segmen s o 5 km and 25 minu es. A e his, he pa icipan can es 1-2 minu es and has o answe he inal su ey. 3. Resul s A o al o 10 d i e s wi h an a e age age o 25.4 (max: 33, min: 20; s d. de .: 3.47) and d i ing expe ience o 7 o 8 yea s (max 15, min: 2, s d. de : 3.62) pa icipa ed in he expe imen . Fi s , he pa icipan pu on he senso s, comple ed he ini ial su ey, and hen lis ened o Moza 's Sona a o Two Pianos in D majo using headphones. This ack has been selec ed because i imp o es men al unc ion [32]. The objec i e o his phase is o be elaxed be o e he d i ing es and o s abilize he senso s. Pa icipan s d o e o 25 minu es. The d i e s had o comple e he ou es p oposed by he GPS o he d i ing simula o . Each ou e has a leng h o 5 km, and i s le el o di icul y is compa able because he concen a ion o ehicles and pedes ians is he same in all cases. The d i ing simula o assigns poin s o he pa icipan a he beginning o he ou e. Each ime an in ac ion is commi ed, poin s a e deduc ed. When he sco e is ze o, he o she mus epea he ou e. This allows he pa icipan o ocus on he d i ing ask as i in a eal en i onmen [33]. The d i ing ime is 25 minu es o ensu e ha he e is enough ime o he s ess da a o be alid [34]. A e wa d, he pa icipan s elaxed o 5 minu es and s a ed he same es again, bu in his case, using i ual Reali y. Wilhelm Daniel Sche z/ P ocedia Compu e Science 00 (2020) 000–000 5 We ha e used he Sympa he ic Tone Index (SNS) [35] o compa e he s ess su e ed by he pa icipan when hey a e d i ing using he d i ing simula o wi h and wi hou i ual Reali y. SNS index is calcula ed using Mean HR (bpm), Bae sky’s s ess index, and LF powe no mal uni (n.u.). Figu e 1 cap u es he alues ob ained by he pa icipan s wi hou and wi h i ual Reali y. The esul s show ha he e a e no signi ican di e ences in he s ess le el when d i e s do no use i ual eali y (a e age SNS index = 0.898, SD = 1.23) wi h when hey do use i (a e age SNS index = 0.784, SD = 1.16), (9) = 1.210, p> 0.05. An SNS index alue o ze o means ha he pa ame e s e lec ing sympa he ic ac i i y a e, on a e age equal o he no mal popula ion a e age. Non-ze o SNS index alues desc ibe how many SDs below (nega i e alues) o abo e (posi i e alues) he no mal popula ion a e age he pa ame e alues a e. Fig. 1. Compa ison o Sympa he ic Tone Index (SNS). Table 2 shows he esul s o he analysis o hea a e a iabili y in ime and equency. We can obse e ha he pa icipan s who mani es ed a high le el o s ess du ing he d i ing es wi hou i ual Reali y also p esen ed i when hey used i ual Reali y. This is he case o d i e s 3, 5, 6, 7, and 10. Figu e 2 cap u es he esul s o he su ey comple ed by he pa icipan s be o e s a ing he d i ing es . Pa icipan s we e asked abou he le el o s ess hey no mally eel when d i ing in a eal en i onmen and he quali y o sleep he las nigh . We ha e used a Like ype scale whe e ‘1’ has a e y posi i e meaning ( he d i e says ha he does no su e s ess when d i ing o he slep e y well he nigh be o e) and ‘5’ is conside ed e y bad ( he d i e su e s a lo o s ess when d i ing in he eal en i onmen o did no sleep well he las nigh ). The esponses ha e been g ouped acco ding o he SNS index ob ained when using he simula o . The e a e wo g oups, hose ha ob ain an SNS index less han o equal o 0.76 ( elaxed d i e s) and hose ha ob ain an SNS index highe han 0.76 (s essed d i e s). In bo h cases, we a e conside ing he alues ob ained wi h and wi hou i ual Reali y. The da a indica es ha he e is a signi ican di e ence be ween bo h g oups F (9) = 24.20, p <0.05 second he ANOVA es when we conside he s ess ques ion. The d i e s who eel he mos s ess when d i ing in he eal en i onmen a e he ones who show he mos s ess in he i ual en i onmen , using i ual Reali y and wi hou i . In he case o he ques ion abou sleep quali y, he d i e s who p esen ed he highes s ess le el du ing he d i ing es we e he ones who slep he wo s he nigh be o e. The di e ence be ween he wo g oups is signi ican . The ANOVA es esul is F (9) = 12.25, p <0.05. -2,00 -1,00 0,00 1,00 2,00 3,00 4,00 12345678910 SNS Index D i e SNS Index (Wi hou i ual eali y) SNS Index (Wi h i ual eali y) 3260 Wilhelm Daniel Sche z e al. / P ocedia Compu e Science 176 (2020) 3255–3262 6 Wilhelm Daniel Sche z / P ocedia Compu e Science 00 (2020) 000–000 Table 2. Hea Ra e Analysis D i e Vi ual Reali y A e age RR (ms) SDNN (ms) A e age HR (bpm) STD HR (bpm) RMSSD (ms) pNN50 LF/HF 1 No 813.11 23.90 73.79 2.21 24.72 4.10 3.36 Yes 743.71 42.41 80.67 4.72 31.80 10.56 2.48 2 No 860.17 32.67 69.75 2.66 24.38 3.98 3.96 Yes 862.86 30.54 69.53 2.47 22.49 2.57 4.76 3 No 674.51 51.95 88.95 6.85 32.15 8.72 6.29 Yes 686.20 61.92 87.48 7.86 40.04 13.53 5.04 4 No 813.95 43.33 73.71 4.02 35.54 13.96 3.35 Yes 884.46 58.33 67.83 4.57 46.81 22.78 2.78 5 No 625.47 29.776 95.92 4.57 19.29 1.88 2.93 Yes 650.04 30.624 92.30 4.31 20.15 1.53 2.38 6 No 693.55 28.961 86.51 3.63 18.07 1.01 7.62 Yes 697.13 37.552 86.06 4.58 20.12 2.65 8.78 7 No 700.52 55.332 85.65 6.80 47.19 16.23 1.78 Yes 740.45 40.64 81.03 4.36 33.94 13.20 2.05 8 No 1175.90 77.084 51.02 3.37 81.18 48.88 2.11 Yes 1133.00 82.77 52.91 3.92 83.59 50.36 2.17 9 No 804.11 44.07 74.61 4.11 32.63 9.91 5.93 Yes 769.06 68.41 78.01 6.87 48.95 19.22 5.27 10 No 729.06 28.20 82.29 3.19 19.32 1.09 3.56 Yes 743.37 28.34 80.71 3.09 20.15 2.20 3.36 Fig. 2. Resul s o he su ey g ouped by SNS index. 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 SNS index <= 0.76 SNS index > 0.75 Like Scale (1: Ve y good, 5: Ve y bad) Sleep Quali y be o e he d i ing expe imen S ess in a eal d i ing en i onmen Wilhelm Daniel Sche z e al. / P ocedia Compu e Science 176 (2020) 3255–3262 3261 6 Wilhelm Daniel Sche z / P ocedia Compu e Science 00 (2020) 000–000 Table 2. Hea Ra e Analysis D i e Vi ual Reali y A e age RR (ms) SDNN (ms) A e age HR (bpm) STD HR (bpm) RMSSD (ms) pNN50 LF/HF 1 No 813.11 23.90 73.79 2.21 24.72 4.10 3.36 Yes 743.71 42.41 80.67 4.72 31.80 10.56 2.48 2 No 860.17 32.67 69.75 2.66 24.38 3.98 3.96 Yes 862.86 30.54 69.53 2.47 22.49 2.57 4.76 3 No 674.51 51.95 88.95 6.85 32.15 8.72 6.29 Yes 686.20 61.92 87.48 7.86 40.04 13.53 5.04 4 No 813.95 43.33 73.71 4.02 35.54 13.96 3.35 Yes 884.46 58.33 67.83 4.57 46.81 22.78 2.78 5 No 625.47 29.776 95.92 4.57 19.29 1.88 2.93 Yes 650.04 30.624 92.30 4.31 20.15 1.53 2.38 6 No 693.55 28.961 86.51 3.63 18.07 1.01 7.62 Yes 697.13 37.552 86.06 4.58 20.12 2.65 8.78 7 No 700.52 55.332 85.65 6.80 47.19 16.23 1.78 Yes 740.45 40.64 81.03 4.36 33.94 13.20 2.05 8 No 1175.90 77.084 51.02 3.37 81.18 48.88 2.11 Yes 1133.00 82.77 52.91 3.92 83.59 50.36 2.17 9 No 804.11 44.07 74.61 4.11 32.63 9.91 5.93 Yes 769.06 68.41 78.01 6.87 48.95 19.22 5.27 10 No 729.06 28.20 82.29 3.19 19.32 1.09 3.56 Yes 743.37 28.34 80.71 3.09 20.15 2.20 3.36 Fig. 2. Resul s o he su ey g ouped by SNS index. 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 SNS index <= 0.76 SNS index > 0.75 Like Scale (1: Ve y good, 5: Ve y bad) Sleep Quali y be o e he d i ing expe imen S ess in a eal d i ing en i onmen Wilhelm Daniel Sche z/ P ocedia Compu e Science 00 (2020) 000–000 7 In conclusion, i ual Reali y did no cause addi ional s ess when he pa icipan s d o e in he d i ing simula o . The main ad an age o his echnology is ha he simula ion seems mo e ealis ic. Howe e , i s main d awback is ha i causes dizziness. Pa icipan s illed ou a su ey whe e hey e alua ed using a Like scale i hey el dizzy a e comple ing he d i ing es . On his scale, '1' mean ha hey we e no dizzy and '5' ha hey we e e y dizzy. The mean alue was 3.1 ± 0.73 o d i e s who used i ual Reali y. In he case o 21 d i e s who ca ied ou he same es bu wi hou using i ual Reali y, he mean alue was 1.33 ± 0.96. The e o e, i ual Reali y can be a headse lea ning ool o d i e s o imp o e d i ing om an ecological and sa e y poin o iew. Howe e , i ual eali y de ices mus imp o e o a oid dizziness ha discou ages hei use and educes aining ime. 4. Conclusions and u u e wo k In p e ious s udies, we collec ed da a in ou di e en si ua ions, and we ied o desc ibe s ess using hea signals de ec ion. In his s udy, we only obse ed he hea signals while d i ing a simula o wi h and wi hou VR. The aim was o analyses he use o i ual eali y headse as a possible s esso . Fo his, we compa ed he HRV o 10 pa icipan s while d i ing wi h and wi hou i ual Reali y. The ob ained da a showed ha he pa icipan s had a sligh ly highe hea a e and o he physiological pa ame e s, bu a p oo ha VR induces mo e s ess could no be obse ed in such small g oup al hough we could obse e some s ess esponse and dizziness by some pa icipan s while using he VR. 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