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Thermal comfort is an essential environmental factor related to quality of life and work effectiveness. We assessed the feasibility of wrist skin temperature monitoring for estimating subjective thermal sensation. We invented a wrist band that simultaneously monitors skin temperatures from the wrist (i.e., the radial artery and ulnar artery regions, and upper wrist) and the fingertip. Skin temperatures from eight healthy subjects were acquired while thermal sensation varied. To develop a thermal sensation estimation model, the mean skin temperature, temperature gradient, time differential of the temperatures, and average power of frequency band were calculated. A thermal sensation estimation model using temperatures of the fingertip and wrist showed the highest accuracy (mean root mean square error [RMSE]: 1.26 ± 0.31). An estimation model based on the three wrist skin temperatures showed a slightly better result to the model that used a single fingertip skin temperature (mean RMSE: 1.39 ± 0.18). When a personalized thermal sensation estimation model based on three wrist skin temperatures was used, the mean RMSE was 1.06 ± 0.29, and the correlation coefficient was 0.89. Thermal sensation estimation technology based on wrist skin temperatures, and combined with wearable devices may facilitate intelligent control of one’s thermal environment.

The rate of increase in the number of aging population in Korea is very rapid among OECD-member countries. And fall accident is one of the most common factors that threaten the health of the elderly. Therefore, it is needed to develop a fall detection system for the elderly. Most fall detection systems use accelerometers attached on the torso. And in various studies, it was verified that these systems have high sensitivity and high specificity. However, the elderly would feel uncomfortable when banding a sensor on the chest every day. Therefore, in this study, we attached an accelerometer on the shoes to detect fall in the elderly. This prototype system would be improved as a smaller, low-power system in the next study. Also, applying energy harvesting device to this shoe system is being developed to reduce the weight of battery.

Core body temperature is a reliable marker for circadian rhythm. As characteristics of the circadian body temperature rhythm change during diverse health problems, such as sleep disorder and depression, body temperature monitoring is often used in clinical diagnosis and treatment. However, the use of current thermometers in circadian rhythm monitoring is impractical in daily life. As heart rate is a physiological signal relevant to thermoregulation, we investigated the feasibility of heart rate monitoring in estimating circadian body temperature rhythm. Various heart rate parameters and core body temperature were simultaneously acquired in 21 healthy, ambulatory subjects during their routine life. The performance of regression analysis and the extended Kalman filter on daily body temperature and circadian indicator (mesor, amplitude, and acrophase) estimation were evaluated. For daily body temperature estimation, mean R-R interval (RRI), mean heart rate (MHR), or normalized MHR provided a mean root mean square error of approximately 0.40 °C in both techniques. The mesor estimation regression analysis showed better performance than the extended Kalman filter. However, the extended Kalman filter, combined with RRI or MHR, provided better accuracy in terms of amplitude and acrophase estimation. We suggest that this noninvasive and convenient method for estimating the circadian body temperature rhythm could reduce discomfort during body temperature monitoring in daily life. This, in turn, could facilitate more clinical studies based on circadian body temperature rhythm.

Deep body temperature is an important indicator that reflects human being's overall physiological states. Existing deep body temperature monitoring systems are too invasive to apply to awake patients for a long time. Therefore, we proposed a nonintrusive deep body temperature measuring system. To estimate deep body temperature nonintrusively, a dual-heat-flux probe and double-sensor probes were embedded in a neck pillow. When a patient uses the neck pillow to rest, the deep body temperature can be assessed using one of the thermometer probes embedded in the neck pillow. We could estimate deep body temperature in 3 different sleep positions. Also, to reduce the initial response time of dual-heat-flux thermometer which measures body temperature in supine position, we employed the curve-fitting method to one subject. And thereby, we could obtain the deep body temperature in a minute. This result shows the possibility that the system can be used as practical temperature monitoring system with appropriate curve-fitting model. In the next study, we would try to establish a general fitting model that can be applied to all of the subjects. In addition, we are planning to extract meaningful health information such as sleep structure analysis from deep body temperature data which are acquired from this system.

Objectives: This paper introduces a mathematical model that can estimate deep brain temperature during therapeutic hypothermia (TH) based on a double sensor method (DSM). Although the cerebral temperature is more important than the non-cerebral core temperature during TH, pulmonary artery (PA), rectal, and esophageal measurements (i.e. the typical core temperature measurement locations) have all been used for target temperature management. This is because there is no safe means of measuring the exact brain temperature. Approach: We applied a double sensor thermometer to the subject's forehead to measure the cerebral temperature non-invasively. Invasive and non-invasive brain temperature readings were acquired for 11 pigs, seven of which were used to develop an optimal model using jackknife resampling and four of which were used to test the model. Main results: The logit model exhibited the best performance of 0.134 °C root mean square error and a 0.993 Lin's concordance correlation coefficient (CCC). Each test dataset had acceptable results in that each 95% limit of agreement was within the range of clinical acceptance of [−0.5 °C, 0.5 °C]. Three of the four datasets yielded an 'almost perfect' score for Lin's CCC. Significance: Only a small number of studies have compared invasively and non-invasively measured brain temperatures, while most previous studies have concentrated on comparison with the core temperature. Furthermore, the possibility of measuring the exact brain temperature safely during TH using a DSM is shown in this work.

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