## 2 Materials and Methods
### Development of General Thermal Comfort Predictive Model
#### 2.1.3 Thermal Comfort and Variable Screening Experimental Protocol  
The main objective of this stage was to screen the most suitable variables to predict the cyclist's under-helmet thermal comfort which can also be easily measured so as to be combatable for smart helmet application. During the course of these experiments, each experimental trial lasted 80 min and was divided into four consecutive timeslots of 20 min each. At each timeslot, a combination of  
Figure 2: Schematic representation showing the used bicycle fixed inside a customised wind tunnel and placed within a climate chamber (**left**) and a photograph of a test subject riding the bike within the wind tunnel (**right**). changes in the environmental variables, namely, relative air velocity imposed by the fan (\(v\)), ambient air temperature (\(T_{a}\)), thermal resistance of the scalp (\(R_{h}\)) and the delivered cycling power (\(P\)), was applied. The quantification of the scalp thermal resistance (\(R_{h}\)) was developed based on computational fluid dynamic (CFD) simulation for a bare head [24]. The thermal resistance was quantified (see Table 1) for the following cases: no-helmet wearing, where \(R_{h}\) was 0; wearing helmet; and wearing helmet with helmet fast (aeroshell). The applied combinations of the different variables with their different levels (low, mid and high) are shown in Table 1.  
During the course of each trial, the heart rate (\(H_{R}\)), in bpm, of the test subject was measured and logged with a validated heart rate belt sensor (Zephyr\({}^{\text{TM}}\) bioharness Bt) in combination with a built-in optical heart rate sensor (PPG, Lifebeam) in the bicycle helmet (Lazer Z1 and Lazer Z1 fast = Lazer Z1 + aeroshell). The temperatures of the subject's forehead, neck, inside of the ear and the air under the bicycle helmet (at front, back, right and left) were continuously measured using calibrated thermocouples (type-T) with a sampling frequency of 1 Hz. During the experiment, all test subjects were verbally asked about their thermal comfort every 5 min from the start (minute 0) until the end (minute 80) based on the thermal comfort scale introduced by [PERSON] et al. [10]. For convenience, the cold thermal sensation votes were excluded, as shown in Table 2, as the present work only focused on discomfort perception due to high temperatures. The experimental design was done using JMP Pro software. A preliminary screening experiment was set up to investigate the contribution of the different variables that, potentially, have an effect on the thermal sensation and thermal comfort under the bicycle helmet. Therefore, each subject was subjected to a combination of different levels of environmental conditions during the experiment. The experiment was designed to investigate the main effects of the defined environmental input variables, the two-variable interactions between these variables and, due to the particular interest in the effect of a bicycle helmet, the quadratic effect of \(R_{h}\), which can be mathematically expressed as follows:  
\[T_{\text{C}}\ =\ \beta_{T_{a}}T_{a}+\beta_{R_{h}}R_{h}+\beta_{P}P+\beta_{v}v+ \beta_{Pv}Pv+\beta_{PT_{a}}PT_{a}+\beta_{PR_{h}}PR_{h}+\beta_{R_{h}T_{a}}R_{h} T_{a}+\beta_{R_{h}{}^{2}}{R_{h}}^{2}, \tag{1}\]  
where \(T_{\text{C}}\) is the thermal comfort and \(\beta_{i}\) is the weighting factor for each variable or variable combination (\(i\)). The inclusion of the quadratic effect, which is the interaction effect of the variable with itself, was necessary to generate an experiment that has multiple levels of \(R_{h}\), so that analysis of a dynamic response due to the bicycle helmet was possible. With the help of the JMP Pro\({}^{\text{\textregistered}}\) software, different combinations (referred to as runs) of the input variables were generated. In general, each participant (test subject) was subjected to four runs (combinations) of the generated ones. Table 3 shows the