Innovative Metabolic Rate Sensing Approach for Probing Human Thermal Comfort

The human metabolic rate has attracted increasing interest as it is the most critical parameter in thermal comfort evaluation, a challenging field, while it is always determined imprecisely. The main issue hampering metabolic rate portable measurement is a lack of reliable methods. Current measuring solutions are unsatisfactory because nonportable bulky size systems and disturbance masks are required. This paper proposes a novel metabolic rate measurement model, which we believe is the first of its kind, to accurately identify and predict human metabolism values via wearable technology. Based on a newly developed theory, the designed wearable metabolic rate sensor was fabricated to measure key parameters: heart rate, heat loss, and skin resistance. Together with the body muscle rate, the new final linear metabolic rate model showed easy prediction capability. Eight volunteers were invited for the experiment under three conditions under four activity intensity states. First, the results significantly verify that a linear relationship exists between the metabolic rate tested by the Quark CPET instrument and our proposed model, with a high coefficient of determination ( R 2 ≈ 0.90). The correlation model is worth mentioning because it coincides with our hypothesis, with at least 95% overall accuracy and less than 2% uncertainty under each condition. Second, the most remarkable finding is that our model is exceedingly suitable ( R 2 ≈ 0.90) for the same person, regardless of the experimental temperature. Finally, validation is conducted in a wider metabolic range, further strengthening confidence in our metabolic rate estimation approach. In summary, based on an innovative methodology, our novel metabolic rate sensor is wearable, comfortable, real-time achievable, and miniaturized compared with the existing equipment. This paper sheds new light on human metabolic rate measurement and prediction. Furthermore, our approach and designed sensor can be applied to evaluate indoor thermal comfort precisely, thus leading to reduced energy consumption.