## 2 Materials and Methods
### Data Analysis  
The research team exported the data for each participant from the respective core temperature measuring devices to test the hypotheses of this study. The devices had different sampling frequency. Core temperature data were extracted from the CorTemp(r) data recorder (0.1 Hz sampling frequency), SS sensor (0.1 Hz sampling frequency), and Zephyr sensor (1 Hz sampling frequency). Thus, all the data were resampled to 30 s datapoints for ease of data comparison. The 30 s datapoints were then imported to IBM SPSS statistical tool [32] to verify that the data met the assumption for a univariate statistical analysis method. The core temperature measurement from each of the wearable sensors were independent of each other and they had no systematic relationship. The participants' core temperature data were measured on a continuous scale. The temperature data were subjected to a Shapiro-Wilk normality test that returned a \(p\)-value \(<\) 0.05, which showed that the data violated the assumption of normality. A scatterplot was utilized to visualize the data, allowing for a clear identification of any data points that deviated significantly from the overall pattern or distribution. To further ensure the integrity of the dataset, the interquartile range (IQR) method was employed. This method assisted in identifying data points that exhibit unusual distances from the median of the dataset, indicating their potential as significant outliers. In this study, three such data points were identified and subsequently eliminated from the analysis. The occurrence of these significant outliers can be attributed to factors such as participants' sweat, which may have affected the contact between the sensor and the participants' skin. Additionally, participants rubbing their upper arm on the Zephyr sensor, which was positioned around their chest, as they curled the dumbbell could have also influenced the sensor-skin contact. By carefully identifying and eliminating these outliers, we aimed to minimize any potential distortions or biases that could arise from irregular data points. If these outliers were not eliminated, they could introduce considerable errors and bias which would undermine the accurate evaluation of the device's performance. Consequently, this can result in inaccurate assessments, misleading interpretations, reduced device reliability, and pose potential risks to the health and safety of individuals. The Spearman rank-order correlation coefficient (Spearman's correlation, \(\rho\)) was conducted to test the strength of the relationship between the ICTS and the SS as well as the ICTS and the Zephyr wearable sensor. A one-sample Wilcoxon signed-rank test was conducted to test the agreement between the data from the ICTS and the other wearable sensors. To describe the level of agreement among the devices, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Participant** & **Mean** & **Median** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline Age (years) & 21.7 & 20 & 3.9 & 18.0 & 29.0 \\ Height (inches) & 70.5 & 70 & 3.2 & 65.1 & 76.4 \\ Weight (pounds) & 160.4 & 166.4 & 25.5 & 112.8 & 201.6 \\ BMI & 22.7 & 23.2 & 3.3 & 17.2 & 29.0 \\ MVC (pounds) & 43.2 & 42.5 & 12.5 & 26.5 & 72.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of participants’ physiological information. a Bland-Altman analysis was conducted for all the recording from each participant at 30 s epochs. Linear regression was used to assess the significance of the bias between the ICTS and the other wearable sensors. The root mean squared error (RMSE) and the mean absolute error (MAE) both evaluate the similarity of the data generated by the wearable sensors and the ICTS. It is noteworthy that low RSME and MAE values indicate greater reliability and accuracy of the data. Furthermore, the analysis encompassed an assessment of the average temperature trends over time, considering the variability and dispersion of temperature measurements among the participants.