Thermal Comfort Model Established by Using Machine Learning Strategies Based on Physiological Parameters in Hot and Cold Environments

Table 6

Comparison of related research results.

Authors	Thermal comfort index	Input parameters	Algorithms (accuracy)
Wang et al. [23]	TSV	Air temperature; Air velocity; CO2 concentration; Illuminance; Health condition; Living time in aged-care homes; Local skin temperatures	RF (0.77)
Liu et al. [4]	TSV	Skin temperatures (head, face, abdomen, thorax, upper arm, lower arm, hand, upper leg, lower leg, feet)	SVM (0.92)
Chaudhuri et al. [24]	TSV; TCV	Hand skin temperature; Hand skin conductance; Pulse rate; Blood oxygen saturation; Blood pressure; Humidity sensation; Airflow sensation	RF (0.93/0.94)
This research	TSV	Front head temperature; SBF; Skin sweat	RF (0.89); SVM (0.678); NN (0.73)