## 3 Results
### Testing the SmartHelmet Prototype and Validation of the Developed General Model  
In Figure 9, the average ratings of perceived exertion (\(RPE\)), thermal comfort (\(T_{C}\)) and thermal sensation (\(T_{S}\)) values at the start and end times of the TT are presented for all seven test subjects. The average values (\(\pm\)standard deviation) of all used subjective ratings showed a significant (\(p<0.05\)) increase at the end of the TT (\(RPE=17.6\pm 0.5\), \(T_{C}=2.6\pm 0.5\) and \(T_{S}=4.4\pm 0.6\)) compared with their values at the start of the trial.  
Figure 10 shows the real-time measured average temperatures (\(\overline{T}_{h}\)) under the helmet, average temperature difference (\(\Delta T\)) between the average temperature under the helmet and the ambient air  
Figure 9: Average values of ratings of perceived exertion (RPE), thermal comfort (\(T_{C}\)) and thermal sensation (\(T_{S}\)) between the start (PRE) and end (POST) times of the TT (* indicates a significant difference of \(p<0.05\)).  
temperature and the average heart rate (\(H_{R}\)) obtained during the TT from all seven test subjects using the developed prototype smart helmet.  
The developed offline liner regression model (3) was used to estimate the thermal comfort (\(T_{C}\)) of all seven test subjects based on the measurements acquired from the SmartHelmet prototype and for comparison with the thermal comfort subjective rating. The model was able to estimate the thermal comfort from all test subjects and revealed an average \(R^{2}\) of 0.84 (\(\pm\)0.03). Model (3) was able to predict the cyclist's thermal comfort under the helmet and had a maximum MAPE of 10%. However, by retuning the model parameters using the data obtained from the TT experiment, the maximum MAPE was reduced to 7.8%.  
The main advantage of the proposed model is that it is a conceptually simple yet very effective tool to explore linear relationships between a response variable (output) and a set of explanatory variables (input variables), which can be easily used for wearable technology such as the SmartHelmet. On the other hand, the disadvantage of such a model is the absence of the time component; in other words, the model is not able to explain the transient response of the output. Additionally, in practice, many factors can affect and change the relationship represented by the proposed model. These factors include helmet-related factors (e.g., helmet weight), other environmental conditions (e.g., wind direction) and personal-related factors, which were not included in the model (e.g., the surface area and contour of the cyclist's head). Hence, it is clear that such general models need to be adapted to new data (personal data) and different conditions for better performance. With the help of wearable sensing technologies  
Figure 10: **(a)** Average temperature (\(\overline{T}_{h}\)) beneath the helmet, (b) average temperature difference (\(\Delta T\)) between the average temperature and the ambient air temperature and (c) average heart rate (\(H_{R}\)) obtained during the TT from all test subjects.  
(SmartHelmet) and streaming modelling algorithms, an adaptive personalised model can be developed for real-time monitoring of a cyclist's head thermal comfort.  
In the following subsection, we introduce the framework of online model adaptation and personalisation (streaming algorithm) based on the easily measured variables obtained from the wearable sensors impeded in the SmartHelmet.