Affiliations: [a] School of Electronics and Information, Nanjing Vocational College of Information Technology, Nanjing, Jiangsu, China | [b] Electronic Information Engineering R&D Center of Jiangsu Province, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Bao-Ming Yu, School of Electronics and Information, Nanjing Vocational College of Information Technology, Nanjing, Jiangsu 210023, China. E-mail: [email protected].
Abstract: The body-temperature is the most significant vital signs of human and animals. It is easily imaginable that measurement of body-temperature of animals will be much more difficult than that of human being due to lack of endurance or fear of measuring instrument. Infrared temperature measurement device may be a solution, however coverage of hair and fur may incur a large error. To address this issue, a rapidly executed algorithm is developed for prediction of steady state body temperature, which needs only a few one-tenth of measurement duration that the currently popular machine learning-based approach usually requires. Let a cubic function c(t) fit the sampled temperature data which are generated by the measurement within a significantly short duration from tn-k to tn,k>0. Then let a quadratic function f(t)=a2t2+a2t+a as a prediction function go through the point (tn,c(tn)) and share the same slope of sn thereat. Finally try to find a next point (tn+m,f(tn+m)), m>0, where the slope satisfies sn+m=sn/2 and m depends strongly on sn through an empirical formula. Accordingly, f(t) can be determined by (tn,c(tn)), sn and sn+m. Experiments indicate that the maximum of f(t) approaches well the steady state temperature of the measured subject with a quite small error.
