Estimation of core temperature based on a human thermal model using a wearable sensor

2015 ◽  
Author(s):  
Akira Uchiyama ◽  
Takashi Hamatani ◽  
Teruo Higashino
Keyword(s):  
Core Temperature ◽  
Thermal Model ◽  
Wearable Sensor ◽  
Human Thermal Model

scholarly journals Efficacy of forced-air and inhalation rewarming by using a human model for severe hypothermia

1997 ◽  
Vol 83 (5) ◽  
pp. 1635-1640 ◽  
Author(s):  
M. S. L. Goheen ◽  
M. B. Ducharme ◽  
G. P. Kenny ◽  
C. E. Johnston ◽  
John Frim ◽  
...  
Keyword(s):  
Ambient Temperature ◽  
Core Temperature ◽  
Cumulative Dose ◽  
Thermal Model ◽  
Human Model ◽  
The Core ◽  
Severe Hypothermia ◽  
Forced Air Warming ◽  
Forced Air

Goheen, M. S. L., M. B. Ducharme, G. P. Kenny, C. E. Johnston, John Frim, Gerald K. Bristow, and Gordon G. Giesbrecht.Efficacy of forced-air and inhalation rewarming by using a human model for severe hypothermia. J. Appl. Physiol. 83(5): 1635–1640, 1997.—We recently developed a nonshivering human model for severe hypothermia by using meperidine to inhibit shivering in mildly hypothermic subjects. This thermal model was used to evaluate warming techniques. On three occasions, eight subjects were immersed for ∼25 min in 9°C water. Meperidine (1.5 mg/kg) was injected before the subjects exited the water. Subjects were then removed, insulated, and rewarmed in an ambient temperature of −20°C with either 1) spontaneous rewarming (control), 2) inhalation rewarming with saturated air at ∼43°C, or 3) forced-air warming. Additional meperidine (to a maximum cumulative dose of 2.5 mg/kg) was given to maintain shivering inhibition. The core temperature afterdrop was 30–40% less during forced-air warming (0.9°C) than during control (1.4°C) and inhalation rewarming (1.2°C) ( P< 0.05). Rewarming rate was 6- to 10-fold greater during forced-air warming (2.40°C/h) than during control (0.41°C/h) and inhalation rewarming (0.23°C/h) ( P< 0.05). In nonshivering hypothermic subjects, forced-air warming provided a rewarming advantage, but inhalation rewarming did not.

Optimization of a neuro-human thermal model using a genetic algorithm

2020 ◽  
pp. 1420326X2097519
Author(s):  
Mohamad El Kadri ◽  
Fabrice De Oliveira ◽  
Christian Inard ◽  
François Demouge
Keyword(s):  
Genetic Algorithm ◽  
Thermal Model ◽  
Absolute Error ◽  
Maximum Error ◽  
Male Students ◽  
Local Skin ◽  
Active Systems ◽  
Physiological Variables ◽  
Human Thermal Model ◽  
Skin Temperatures

A neuro-human thermal model was optimized to increase the prediction accuracy of the physiological variables of a group of 15 healthy male students exposed to transient environmental conditions. The effect of both the passive and active systems parameters was studied using a sensitivity analysis, and the parameters that had the most influence on the neuro-human thermal model outputs were established. A genetic algorithm was then used to optimize the model in order to determine the parameters that corresponded to the studied population. The results showed that the optimization increased the precision of the neuro-human thermal model. The mean absolute error and the maximum error between the experimental data and the numerical results for mean skin temperature were 0.13°C and 0.56°C, respectively, and we obtained 0.03°C and 0.11°C, respectively, for rectal temperature. These results show that the neuro-human thermal model can be accurately adjusted for the rectal, mean and local skin temperatures of a targeted population by using a genetic algorithm to determine the values of the parameters that correspond to this population.

New neurophysiological human thermal model based on thermoreceptor responses

2020 ◽  
Vol 64 (12) ◽  
pp. 2007-2017 ◽  
Author(s):  
Mohamad El Kadri ◽  
Fabrice De Oliveira ◽  
Christian Inard ◽  
François Demouge
Keyword(s):  
Thermal Model ◽  
Model Based ◽  
Human Thermal Model
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