# Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach
## 4 Data Analysis and Findings
### Same Analyses with PMV  
Until now the physiological data was compared against operative temperature. However, this naturally does not reflect the other elements of heat-balance models, or the impact of the adaptive behaviour. In this section, PMV and PPD values were calculated based on the clo values that were observed and recorded throughout the experiments and relative humidity values measured by the climate logger. Throughout the experiment, the relative humidity inside the Sense Lab during occupied time ranged between 20.18% and 46.41% (mean \(\pm\) STD is 31.67 \(\pm\) 7.5%). The metabolic rate was fixed at 1.1 met for light work on computer, and the air velocity was assumed as 0.05 m/s since the Sense Lab is entirely isolated from the outside and the windows at the facade were kept closed during the experiment. The PMV and PPD calculations were done using bythermalcomfort library [59] on Python. Figure 13 shows the PMV ranges for each participant, in respective test days.  
Since air velocity and metabolic rate was set to a constant and operative temperature was kept in a tight range (see Table 2), parameters affecting the PMV were relative humidity and clo values. The variation in RH was quite low throughout the data collection period therefore suggesting that changes in the clothing values affecting the PMV ranges.  
The joint effect of temperature and humidity on PMV was investigated using a partial least-squares linear regression model [60]. The model was fitted to the temperature, humidity parameters estimating the involvement of each parameter in the regressive response outcome \(PMV_{rgr}\),  
\[PMV_{rgr}=\beta_{0}+\beta_{1}\cdot temprature+\beta_{2}\cdot humidity+e \tag{3}\]  
where \(\beta_{0}\) is the intercept, \(\beta_{1}\), \(\beta_{2}\) are the model coefficients of temperature, humidity respectively and e is the residual (unexplained variance in \(PMV_{rgr}\)). The regressive response outcome \(PMV_{rgr}\) contains the combined involvement of temperature and humidity on PMV. The regression model parameters were estimated as,  
\[PMV_{rgr}=-7.65+0.30\cdot temprature-0.004\cdot humidity+e \tag{4}\]  
For estimating the significance, the regression model was fitted on each participant in a sliding window of 60 s and step of 60 s. Due to the highly correlated time series, the subject's \(p\)-value was adjusted by dividing by the number of samples. The total combined \(p\)-value was estimated as the mean values of the individual \(p\)-values leading to a \(p<0.001\) for temperature and a \(p=0.04\) for humidity. Therefore, the temperature was the most significant parameter affecting positively PMV following by humidity which has a marginal significant negative effect.  
The results (Figure 14) therefore, do not show a significant difference in findings in comparison to the previous plots with temperature (Figure 11) as expected. When compared, afternoon and morning clusters present a larger range of tonic values for the same PMV value, similarly to the operative temperature.  
Figure 13: PMV boxplots of each participant.