Facial skin temperature and its relationship with overall thermal sensation during winter in Changsha, China

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Thermal sensation prediction model for high-speed train occupants based on skin temperatures and skin wettedness

Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference (ΔTd), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects' thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers' comfort requirements.

A Comparative Study of Forehead Temperature and Core Body Temperature under Varying Ambient Temperature Conditions

When the ambient temperature, in which a person is situated, fluctuates, the body's surface temperature will alter proportionally. However, the body's core temperature will remain relatively steady. Consequently, using body surface temperature to characterize the core body temperature of the human body in varied situations is still highly inaccurate. This research aims to investigate and establish the link between human body surface temperature and core body temperature in a variety of ambient conditions, as well as the associated conversion curves.

METHODS

Plan an experiment to measure temperature over a thousand times in order to get the corresponding data for human forehead, axillary, and oral temperatures at varying ambient temperatures (14-32 °C). Utilize the axillary and oral temperatures as the core body temperature standards or the control group to investigate the new approach's accuracy, sensitivity, and specificity for detecting fever/non-fever conditions and the forehead temperature as the experimental group. Analyze the statistical connection, data correlation, and agreement between the forehead temperature and the core body temperature.

RESULTS

A total of 1080 tests measuring body temperature were conducted on healthy adults. The average axillary temperature was (36.7 ± 0.41) °C, the average oral temperature was (36.7 ± 0.33) °C, and the average forehead temperature was (36.2 ± 0.30) °C as a result of the shift in ambient temperature. The forehead temperature was 0.5 °C lower than the average of the axillary and oral temperatures. The Pearson correlation coefficient between axillary and oral temperatures was 0.41 (95% CI, 0.28-0.52), between axillary and forehead temperatures was 0.07 (95% CI, -0.07-0.22), and between oral and forehead temperatures was 0.26 (95% CI, 0.11-0.39). The mean differences between the axillary temperature and the oral temperature, the oral temperature and the forehead temperature, and the axillary temperature and the forehead temperature were -0.08 °C, 0.49 °C, and 0.42 °C, respectively, according to a Bland-Altman analysis. Finally, the regression analysis revealed that there was a linear association between the axillary temperature and the forehead temperature, as well as the oral temperature and the forehead temperature due to the change in ambient temperature.

CONCLUSION

The changes in ambient temperature have a substantial impact on the temperature of the forehead. There are significant differences between the forehead and axillary temperatures, as well as the forehead and oral temperatures, when the ambient temperature is low. As the ambient temperature rises, the forehead temperature tends to progressively converge with the axillary and oral temperatures. In clinical or daily applications, it is not advised to utilize the forehead temperature derived from an uncorrected infrared thermometer as the foundation for a body temperature screening in public venues such as hospital outpatient clinics, shopping malls, airports, and train stations.