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.
This study proposes a human authentication framework based on electrocardiogram signals that are robust to dynamic cardiac morphological conditions. The proposed method incorporates a stationary wavelet transform, an infinite feature selection, and a linear discriminant analysis. Evaluation experiments were conducted under three modulated situations: temporal variation, postural variation, and heart rate variation when exercising. Compared with three state-of-the-art methods, the performance of the proposed method was shown to be better overall, with an equal error rate (EER) of 1.48% under timevarying situations, 1.74% under posture changes, and 5.47% after exercise. These results indicate that the proposed method achieves a highly increased performance compared with state-of-the-art techniques. Further evaluation of the identification performance of the proposed method on two public databases shows that it performs better than previously proposed methods.
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