3.2. Development of Offline (General) Thermal Comfort Model
Figure 7 shows the acquired measurements from test subject 1, including environment-related variables, namely, ambient temperature (
), air velocity (
), helmet thermal resistance and applied power level (
) and the applied mechanical work rate (
). Bioresponse-related variables, including heart rate (
), the temperature difference
() between the average temperature beneath the helmet and the ambient air temperature, the temperature difference (
) between the ear temperature and the ambient air temperature as well the thermal comfort (
), were considered.
The graphs show the environmental variables (left graphs), including the ambient air temperature (, °C), fan set-points (, 1 = 4 ms−1), the helmet wearing level (0 = no helmet, 0.5 = helmet and 1 = helmet + aeroshell) and the applied mechanical work rate (power) level (, W). The measured variables related to the bioresponses of the test subject (right graphs) were heart rate (, bpm), the temperature difference (, °C) between the average temperature beneath the helmet and the ambient air temperature, the temperature difference (, °C) between the ear temperature and the ambient air temperature and the thermal comfort (red line) and sensation (blue line) scores.
To investigate the effect of the different inputs on thermal comfort, different linear regression models (general models) were identified to estimate and predict the perceived under-helmet thermal comfort
(output) using continuously measured variables (inputs), including the aforementioned environmental and bioresponse-related variables. The most suitable combination of input variables was selected by retaining only the input variables with a significant (
p < 0.05) effect on thermal comfort. Additionally, the best model structure was selected based on two main selection criteria, namely, the goodness of fit (
) and Akaike information criterion (AIC). The results showed that the most suitable LR model structure, with the highest goodness of fit (average
= 0.87 ± 0.05) and lowest Akaike information criterion (average AIC = 138 ± 12), to predict the thermal comfort for all test subjects was as follows:
The average parameter estimates,
t-ratio and
p-value of
P > |t| for each selected input variable are given in
Table 5. The results showed that the main effect of the thermal resistance
was not significant (
p > 0.05); however, the variable interaction of
with
showed a significant (
p = 0.015) effect on the prediction of the under-helmet thermal comfort.
To understand the interaction effect of
and
on the prediction of thermal comfort, a prediction trace analysis of the model [
27] was employed using prediction the JMP
® profiler tool [
28], as visualised in
Figure 8. For convenience of this analysis, the values of each input variable were scaled (normalised) in such a way to lie in the closed interval [−1, +1], where -1 indicates the variable’s low level and +1 indicates its high level (
Figure 8). The scaling of each variable value
was done according to the following formula:
where
is the scaled variable value at time instance
,
is the midpoint (
) and
and
are the particular lower and upper limits of input variable
, respectively. The term
(
) is half of the range of the interval.
The prediction trace analysis [
28] of the developed model (2) was based on computing the predicted response as one variable was changing while the others were held constant at certain values. The results showed that the effect of
was dependent on the level of
. At a low level (−1) of ambient air temperature (
= 20 °C), for a change in thermal resistance
from a low level (−1) (i.e., no-bicycle helmet) to a high level (1) (i.e., using the Lazer-Z1 Fast), the predicted thermal comfort scale (
Table 2) decreased by 0.5 thermal comfort units but was perceived as comfortable. However, at a high level (1) of ambient air temperature (
= 30 °C), the comfort level increased by 0.5 thermal comfort units. This information is important for actively controlling under-helmet thermal comfort, which can be done by manipulating the helmet thermal resistance via, for instance, opening/closing some of the helmet’s holes.
As expected, the heart rate () of the test subjects was found to be highly correlated (Pearson’s correlation coefficient, = 0.85) with the power (). Additionally, the heart rate was significantly correlated ( = 0.68) with the recorded thermal comfort for all 15 test subjects.
As expected, the temperature difference () between the average air temperature beneath the helmet () and the ambient air temperature () was correlated with both relative air velocity () and helmet thermal resistance (), with = 0.82 and 0.78, respectively.
By employing both heart rate (
) and the temperature difference (
) as input variables to the linear regression model, the best model structure that gave the highest average goodness of fit (with average
= 0.89 ± 0.04) and lowest Akaike information criterion (average AIC = 123 ± 7) was as follows:
where
and
is the average air temperature under the helmet, which is calculated from the four temperature sensors located under the helmet. It can be noticed that the structure of model (3) is more compact, consisting of three input variables, compared with the structure of model (2), which consisted of five input variables. Model (3) showed better prediction performance for the thermal comfort level than model (2), which had maximum mean absolute percentage errors (MAPEs) of 8.4% and 11%, respectively. The MAPE is given by
where
is the number of data points and
is the predicted thermal comfort.
It can be noticed that both the mechanical work rate (
) and air velocity (
) disappeared from the compact model (3). The heart rate (
HR) variable included in the compact model (3) directly linked to the applied mechanical work rate (
), hence the effect of
, included in model (2), translated by the bioresponse represented by
HR (e.g., [
29]) included in model (3). According to Newton’s law of cooling, temperature difference (
) is the driving force for the convective heat transfer (
) between the cyclist’s head and the ambient air. The heat flux (
) is proportional to
and the convective heat transfer coefficient (
) links both variables as follows:
The heat transfer coefficient (,W∙m2∙°C) is a combination of the heat transfer coefficient of the air () and that of the helmet (); hence,
The heat transfer coefficients of the air () and the bicycle helmet () are dependent on air velocity (). Hence, it is clear that the effect of is inherently connected to the effect of both and helmet thermal resistance ().
It can be concluded from the presented results that the input variables included in model (3), namely, temperature difference (), heart rate (HR) of the cyclist and the interaction variable between ambient temperature () and helmet thermal resistance (), were suitable enough to estimate the cyclist’s thermal comfort () under the bicycle helmet. These selected variables were the basis for developing a reduced-order personalised model for real-time monitoring of a cyclist’s thermal comfort under the helmet. Additionally, from a practical point of view, these three variables were suitable to be measured using integrated sensors in the cyclist’s helmet, as is shown in the following subsection.
3.3. Testing the SmartHelmet Prototype and Validation of the Developed General Model
In
Figure 9, the average ratings of perceived exertion (
), thermal comfort (
) and thermal sensation (
) values at the start and end times of the TT are presented for all seven test subjects. The average values (±standard deviation) of all used subjective ratings showed a significant (
p < 0.05) increase at the end of the TT (
= 17.6 ± 0.5,
= 2.6 ± 0.5 and
= 4.4 ± 0.6) compared with their values at the start of the trial.
Figure 10 shows the real-time measured average temperatures (
) under the helmet, average temperature difference (
) between the average temperature under the helmet and the ambient air temperature and the average heart rate (
HR) 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 () 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 of 0.84 (±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 (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.