Figure 1. EVICURES Project structure.
Table 1. Statements of indoor condition.
Figure 3. Group evaluating the bathroom in CAVE.
In the EVICURES project, some interactivity - behaviour - was added to the
model with the visualization software. The viewer was prevented from flying in the
model by restricting the viewer - the avatar - to the floor level, so as to make the
navigation easier and more natural. The avatar was also restricted to being within
the walls of the model, and so was unable to exit the outer limits of the 3D model.
Sliding doors were set to open automatically in front of the avatar and to close
behind the viewer. Medical ceiling-supply joints could be controlled with a joypad -
a wireless game controller. Mirroring of objects provided the avatar with a visual
cue that the viewer is in front of a mirror. Pre-set camera positions were used to
adjust the corner of the bathroom in the computer model to be set exactly to the
corner of the real CAVE - in order to extinguish the CAVE corner from the viewer
and to improve the immersion.
Figure 4. A staff member checking visibility from nursing station to bedside.
The 3D computer models viewed for the EVICURES project attendees were
made to appear realistic. Correct parameters were set for illuminating lights in the
scene according to the physical properties of the light fixtures and this lighting model
was applied to the walls, floors and ceilings of the scene using the render-to-texture
method. This operation, also known as ‘texture baking’, renders the effects of lights
and shadows to the surfaces of the model, so that real time lighting is not required.
In real-time computer graphics this has a remarkable impact on performance. The
SeAMK cave is presented at http://www.seamk.fi/vriab.
Table 3. The collaborative design process.
The design of the care rooms was completed in three separate phases in spring
2015. After each phase the modifications to the rooms were executed in
accordance with feedback. In addition, each visitor was asked to complete a
questionnaire in which they could analyse the properties of the spaces more
systematically and evaluate the virtual environment experience. The total number
of visitors in CAVE was 238. There were altogether 47 multi-occupational groups,
and all the visits were recorded and videotaped. Table 3 presents the collaborative
design process.
Figure 5. The first model of intensive care rooms.
‘igure 6. The second model of intensive care rooms.
Figure 7. The third model of intensive care rooms.
Figure 8. The group of nurses evaluating the bathroom.
Allin all, it was expected that sense of presence would be stronger in the richer
and more vivid virtual presentation of the care environments. Since in the
EVICURES project, the richness and vividness of the virtual environment will
increase across test phases, presence experiences should also become stronger
over time. Preliminary results suggest that this seemed to be in fact what
happened in the test sessions: the mean SP ratings increase across the three
phases, and also PA scores in the third phase were higher than in the preceding
two phases (Figure 9). Participants’ subjective comments also support these
findings. According to a one-way ANOVA, the PA scores of the three phases
differed significantly (p < 0.05) from each other, but the differences between the
SL scores were not Statistically significant. The SL scores were also clearly higher
than the PA scores.
Figure 9. Presence (self-location, perceived action) scores in the three test
phases.
Figure 10. Non-player characters.
are stationary. The user can use the menu to add or remove these characters as
they see fit.
Figure 12. The Game in Android Tablet.
Finally three versions of the Game were made available: Windows (with Kinect 2),
Mac, and Android. The game was used near the end of the project among
personnel for the purposes of checking and discussion. In subsequent projects,
the game development could be done at an earlier stage so as to be available to
support the (non-CAVE) discussions.
Figure 11. Map mode.
Figure 13. Conceptual model of linkages between antecedents, indoor
environmental satisfaction and indicators of wellbeing. All the factors shown with a
brown background are measured in our study.
Figure 14. Distribution of responses regarding environmental satisfaction.
Figure 15. Average environmental satisfaction across the factors measured in the questionnaire.
Figure 16. Average level of satisfaction with various aspects of work.
with diverse aspects of work, such as job security, opportunities for personal
growth, amount of salary, supervision quality, and human relations. As can be
seen in Figure 16, the respondents were most satisfied with the amount of job
security and challenge, the chance to help other people, and human relations in
general. Respondents were least satisfied with the fairness and amount of salary
they receive, the amount of support and guidance received from the supervisor,
and the overall quality of the supervision.
Figure 17. Estimated average time spent on the execution of work tasks and on
resumption from interruptions. See text for details.
Figure 18. Estimated amount of multitasking in work.
Figure 19. Estimated number of tasks performed when multitasking.
Figure 20. Number of participants distracted by sounds.
Figure 21. Some significant correlations between variables (solid lines) and
correlations that approached significance (dashed lines). +/- sign shows whether
the correlation is positive or negative.
satisfaction and job satisfaction. Further, a significant correlation between
environmental satisfaction and some features of multitasking and interruption
management were also found. For example, those who are more satisfied with
their environmental conditions multitask to a lesser degree than those who are
less satisfied. One possibility is that the less-than-optimal environmental
conditions force the employees to multitask to a larger extent.
Participants’ average heart rate was 76/min (61-90), at rest 52/min (44-57) and
peak rate 140/min (98-192). The portion of time being stressed was 50% and for
recovering 26% per day. According to Firstbeat’s database, the recovery
recommendation is 30% per day at least. Recovering during leisure time was good
or fair (61% of all recovering). Mean recovery times when awake during the day off
were 55 minutes and during working time 42 minutes (Figures 22 and 23).
Recovering at work was mostly weak (60%).
Figure 23. Recovering at work.
Figure 22. Recovering through leisure.
Figure 25. Typical skin temperature measurement locations (n=24).
The most common locations for skin temperature measurements are presented in
Figure 25. By adopting these measurement and monitoring locations, itis possible
to compare the obtained results with previous similar measurements.
Figure 24. Thermal sensation and thermal comfort scales.
Figure 26. Anatomy model of Human Thermal Model (HTM).
HTM is based on the true anatomy and physiology of a human body, and it
estimates spatial and temporal temperature levels of body tissues. For the model,
the body is divided into sixteen different body parts, such as the head, neck, upper
arms, lower arms, hands, lower legs, etc. Each body part is further sub-divided
typically into four realistic tissue layers (bone, muscle, fat, and skin) in concentric
cylinders (Figure 26). The exact amounts of different tissue types on a body part
level are based on typical tissue distributions given in previous studies (Smith
1991, Tuomaala et al. 2013), and on the individual boundary conditions presented
above. Such an approach enables a realistic anatomy description for any
individual, and HTM can be used to analyse the thermal wellbeing of different
human groups, such as typical personnel and patients.
When estimating human thermal sensation and thermal comfort, the influencing
boundary conditions are usually divided into two groups of parameters: external
and internal parameters (Figure 27). External parameters are related to the
Surrounding space, such as air temperature, surface temperatures, air velocity and
humidity. Internal (or personal) parameters are related to the human being, i.e.
clothing and metabolic rate. Furthermore, the metabolic rate depends on individual
anatomy (i.e. amounts of different tissue types) and activity level.
Figure 27. Space-related and occupant-related boundary conditions influencing
human thermal sensation and thermal comfort.
Figure 28. Simulation of tissue temperature levels, thermal sensation and comfort
index values, as well as the level of potential hypothermia of an occupant
spending his/her time in the studied space conditions.
Figure 29. Questionnaire form for estimating the thermal perception of occupants
at the Seinajoki Central Hospital (Appendix B).
Figure 30. Wired skin temperature measurement arrangement of a sitting person
at the Seinajoki Central Hospital.
Figure 32. BodyExplorer measurement arrangements for the definition of
individual body composition data required by Human Thermal Model calculations.
Individual body composition data is needed as a boundary condition for Human
Thermal Model calculations. The relevant tissue type masses were measured by
BodyE xplorer 2.0.
Figure 31. Example of monitored space air temperature data from the Intensive
Care Unit at Seinajoki Central Hospital.
Figure 33. Questionnaire data on perceived thermal sensation from the Intensive
Care Unit at Seinajoki Central Hospital.
to value -1) and -0.64, respectively. However, there seem to be huge variations
between individual votes: thermal sensation votes range from -4.0 to +3.6, and
thermal comfort voles range from -4.0 to 4.0.
Figure 34. Questionnaire data on perceived thermal comfort from the Intensiv
Care Unit at Seinajoki Central Hospital.
Figure 35. Perceived thermal sensation index values by subject number 1: optima
temperature level of 26.8 °C according to linear regression.
(corresponding to thermal neutrality) is obtained at a temperature level of 24.7 °C
for the whole population. However, there are significant individual variations
between individuals, which becomes obvious when presenting correlations
between temperature and perceived thermal sensation for individuals (Subjects 1,
5, and 10) with different body compositions (BF = body fat and BMI = body-mass-
index) presented in Figures 35, 36 and 37.
Figure 36. Perceived thermal sensation index values by subject number 5: optima
temperature level of 22.8 °C according to linear regression.
Figure 37. Perceived thermal sensation index values by subject number 10
optimal temperature level of 20.1 °C according to linear regression.
Figure 38. Measured skin temperatures from 10 fixed body locations, and thermal
sensation and thermal comfort vote at the end of test period of the test subject 1.
Figure 39. Measured skin temperatures from 10 fixed body locations, and thermal
sensation and thermal comfort vote at the end of test period of the test subject 2.
Figure 40. Measured skin temperatures from 10 fixed body locations, and thermal
sensation and thermal comfort vote at the end of test period of the test subject 3.
Figure 41. Measured skin temperatures from 10 fixed body locations, and thermal
sensation and thermal comfort vote at the end of test period of test subject 4.
Figure 42. Comparison of measured skin temperatures from 10 fixed body
locations of four test subjects, where test subjects 1 and 4 perceived the thermal
environment to be too cool and subjects 2 and 3 too warm.
Figure 43. BodyExplorer measurement results (BF refers to Percentage of Body
Fat).
Figure 44. Large population correlation between body-mass-index and percentage
of fat (Jackson et al. 2002), and adopted body compositions for HTM calculations
shown by coloured markers.
Figure 45. Impact of operative temperature on overall thermal sensation of male
occupants with different muscularity index values (1, 0.5, and 0).
Figure 46. Impact of operative temperature on overall thermal sensation of femal
occupants with different proportions of fat tissue.
Figure 47. The Foresight Framework Model as a tool in the workshops.
Figure 48. The critical points in the patient's care pathway in intensive an
intermediate care.
Figure 49. Services List 1.
Next on the list with very high scores are the need to adjust the indoor air
(temperature and quality).
Figure 50. Services List 2.
In addition to the categorized questions there were a couple of open questions
with free textbox. The most important open question was about what devices in
the room you would like to see used / automated, if possible.
The ton three devices mentioned bv the respondents were:
Figure 52. D6TK ventilation (building automation graphics of the current situation).
Table 4. Measured equivalent and maximum sound levels.
Figure 54. Cooling simulation with Granlund’s RIUSKA software.
Figure 55. Heating simulation with Granlund’s RIUSKA software.
Figure 56. Active chilled beams to be installed in the ICU rooms.
The ICU rooms were designed to have active chilled beams of the PARASOL
type. These beams feature both cooling and heating radiators. The radiators are
easy to clean (See below for an image series of radiator cleaning).
Figure 57. The design values of the chilled beams in the rooms were determined
with the ProSelect software.
Kuinka samaa mielta olen vaitteen kanssa?
Tassa osassa voit kertoa, millaiselta tyosi sinusta tuntuu. Alla on vaitteita tydsta.
Sinun tulee ilmaista omat henkilokohtaiset tuntemuksesi tydstasi merkitsemalla,
kuinka samaa mielta olet kunkin vaitteen kanssa.
Kuinka tyytyvainen olet tahan tyosi piirteeseen?
Kuinka usein sinusta on tuntunut talta toissa ollessasi?
146. Haittaavatko tyOpisteeseesi kuuluvat aanet keskittymistasi?
147. Mitka tydpisteeseesi kuuluvat aanet haittaavat keskittymistasi? J os mikaan
aani ei hdiritse, siirry kohtaan 148.
Appendix B: Questionnaire form for estimating
thermal perception of occupants in Seinajoki
Central Hospital
10. Potilashuoneen ilman laadun saataminen potilaalle sopivaksi (tunnetusti yks
huonon ilmanlaadun merkki on lisaantynyt hiilidioksidin maara ilmassa):
11. Huonetilojen ilman laadun saataminen hoitajalle sopivaksi (tunnetusti yks'
huonon ilmanlaadun merkki on lisdantynyt hiilidioksidin maara ilmassa):
Huonetilojen lampotilan ja kosteuden saataminen hoitajalle sopivaksi:
7. Hoitohenkilokunnan kutsuminen:
Minkalaisia asioita haluaisit, mikali se olisi teknisesti mahdollista, saada
‘toimimaan’ automaattisesti, siten etta jokin sensori ensin tunnistaa ihmisen
yleensa tai sinut henkilokohtaisesti, ja sen jalkeen automaatio tekee
puolestasi jonkin toiminnon? Muutamassa kohdassa asiaa tarkastellaan
erikseen potilaan ja hoitajan nakokulmasta.
17. Potilashuoneen lasiseinien lapinakyvyyden saataminen (potilas saataa):
18. Potilashuoneen lasiseinien lapinakyvyyden saataminen (hoitaja saataa):
15. Vesihanan veden maaran ja/tai lampotilan saataminen:
16. Potilashuoneen oven avaaminen kokonaan tai osittain:
13. Eri huonetilojen valaistuksen saataminen hoitajalle sopivaksi:
14. Potilashuoneen vesihanan avaaminen/sulkeminen:
19. Tietokoneen avaaminen:
20. Tehohoidossa kaytettavien laitteiden saataminen:
b. Ruokavaliotiedot:
25. Automaattinen tyOajanseuranta:
21. Jos vastasit edelliseen kohtaan ‘ehka kylla’ tai ‘ehdottomasti kylla’, niin mite
laitetta/laitteita haluaisit saataa automaattisesti?
24. Automaattinen kulunvalvonta:
c. Tieto eristystarpeesta ja/tai tarttuvista taudeista (esim. hepatiitti):
d. Potilaan toiveet vierailijoista tai omien terveystietojen antamisesta laheisill
henkildille:
e. Tieto apuvalineiden tarpeesta (silmalasit, kavelykeppi, pyoratuoli ym.):
33. Alyvaatteeseen (esim. T-paita) johon on kiinnitetty tunnistin:
31. Henkilokorttiin tai kulkuavaimeen (‘laku’):
32. Alykoruun tai -rannekkeeseen:
28. Hoitotilanteeseen mukautuva paatoksenteon tuki:
29. Muu mika?
34. PIN-koodiin:
35. Kasvojentunnistukseen:
Kiitos paljon kyselyyn vastaamisesta!
37. Muuhun, mihin?
a eis bea
Kyselylla selvitettiin henkildkunnan tyytyvaisyytta (indoor environmental
satisfaction) ja hyvinvointia seka mitattiin fyysista stressia. Tyopajoissa
suunniteltiin uusia tehohoidon tyétapoja (Foresight Framework and Pathfinders
methods). Kyselyilla ja mittauksilla selvitettiin yksildllista lampoviihtyvyytta (Human
Thermal Model). Tuloksena saatiin l[ampdtilojen raja-arvot lampdétekniseen
suunnitteluun.
