# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 5 Results
### RMSE Calculated for Each Action  
For the second scenario, RMSE is calculated based on each action (Eating, Using laptop, Standing, Sitting, Laying). Tables 4 and 5 present comparisons between the fixed and adaptive (dynamic) models using the RMSE for each individual action. Figure 9: RMSE results comparing the adaptive (dynamic) model with a ground truth for various sitting activities for both PMV and temperature. Also shown are ’fixed’ results where a single PMV is assumed depending on whether the person is sitting or standing. The average RMSE for the whole process period is also calculated and included at the ends of Tables 4 and 5. These values help to provide an overall idea regarding each model. It can be seen from the results that are shown in Figure 9 and Table 5 that the adaptive (dynamic) control improves the temperature adjustment settings with respect to thermal comfort levels. The adaptive control produced RMSE values that are close to the ground-truth in comparison to the fixed models. Other RMSE calculations are also considered under the same scenarios, but using PMV measurements to provide a comparison between the models based on the PMV thermal comfort levels. Table 4 includes a comparison between the fixed and adaptive models in terms of average RMSE calculation. These are based on each action period and the whole process duration using PMV measurements. The adaptive control appears to improve the results of the PMV calculations. In turn, this also helps to obtain more accurate thermal comfort levels, based on an occupant's actions. It has produced low RMSE readings that can be considered very close to the ground-truth. The fixed model, also shown in Table 4, appears to provide reduced performance in comparison to the adaptive model. A fixed model for sitting and standing has also been considered. Splitting the fixed model into these two gross poses also appears to not be as optimal as the time-dynamic approach of continuously recognising actions. calculation of the PMV index. The metabolic rate can have a great impact on the thermal comfort level. A deep learning-based action recognition system was proposed to recognise actions of a person and then identify the relevant metabolic rate for the recognised actions. This enables the extraction of a relatively accurate metabolic rate and, consequently, an accurate thermal comfort level. A novel adaptive methodology for thermal control based on occupant context has also been provided by integrating the computer vision for action recognition with the thermal comfort model using fuzzy logic. This integration provides a way to adaptively control occupant's thermal comfort without a need to attach a sensor on an occupant. Pre-set rules of the fuzzy temperature control are designed a priori and included in the experiments that followed, i.e in terms of fixed, ground-truth, and classified states. The fuzzy temperature control was used to adjust the thermal settings according to the occupant's actions and their personalised comfort for a number of different scenarios. A comparison between the fixed and adaptive (dynamic) models has been presented. Quantitative comparisons that are based on RMS error values for both PMV and temperature have been presented. The achieved results are compared with the results for the case of using one or two fixed metabolic rates. The considered actions in these assessments included eating, using a laptop, standing, sitting, and laying. The results have helped to demonstrate the good performance of the adaptive (proposed) model. Conceptualization, D.N. and A.I.A. ; Data curation, A.I.A. ; Formal analysis, J.C.; Investigation, M.A.-F.; Methodology, M.A.-F.; Software, M.A.-F.; Supervision, J.C. and D.N. ; Writing-original draft, M.A.-F.; Writing-review & editing, M.A.-F. and J.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data of the computer vision system presented in this study are publicly available.