## 4 Environmental Approach Using Air and Thermometric Sensors
### Experiment Results and Discussion  
Time-series split cross-validation (TSSCV) was used to evaluate the recognition accuracy of behavior patterns. The number of divisions \(k\) is the most important parameter for TSSCV. We set \(k=8\) as a result based on our preliminary experiment. Figure 9 depicts the distributions of recognition accuracies in each validation result. The distributions demonstrate that the difference in recognition accuracy is greater in each validation data subset. Table 16 presents the mean recognition accuracies calculated from the result portrayed in Figure 9. The mean recognition accuracy and its standard deviation from all sensors of both modules are 60.8% and 12.0%, respectively. Reflecting the performance of the sensor module separately, the third and fourth rows in the table, respectively, show recognition accuracies for P1 or P2. Although the accuracy for P1 dropped 18.1%, the accuracy drop for P2 was 3.1%. This accuracy difference demonstrates that the effect of improved recognition accuracy of P2 was higher than that of P1. For the rectangular room depicted in Figure 8a, the sensor installation on the longitudinal side was more effective than that on the latitudinal side. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **ET** & **LC** & **SP** & **GM** & **RD** & **EX** & **NP** & **ST** & **Total** \\ \hline No. Signals & 655 & 3142 & 1139 & 3951 & 1444 & 408 & 414 & 368 & 11521 \\ Ratio (\%) & 5.69 & 27.27 & 9.89 & 34.29 & 12.53 & 3.54 & 3.59 & 3.19 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Details of datasets for respective behavior patterns. Figure 9: Transition of recognition accuracies with time-series split cross-validation (TSSCV). Subsequently, the third, fourth, and fifth columns of Table 16, respectively, present the recognition accuracies for the sole use of either D6T, BME280, or K30. Compared with the three sensors, D6T and K30, respectively, produced 50.6% as the highest accuracy and 18.2% as the lowest accuracy. This revealed tendency demonstrates that delay time gaps of measured feature changes affected feature changes of the measurement object. Moreover, these results demonstrate that feature dimensional differences of the respective sensors, such as 16-dimensional signals from D6T, 3D signals from BME280, and one-dimensional (1D) signals from K30, affected the recognition accuracy. Figure 10 depicts recognition accuracies in each behavior pattern and sensor module. Among the eight behavior patterns, ET exhibited the highest accuracy of more than 90%. Subsequently, LC and GM demonstrated relatively high accuracies: 77.6%, 54.0%, 57.6%, 69.0%, 61.3%, and 77.9%. However, the accuracy difference can be large depending on the installation positions of the sensor modules. The accuracies of RD and EX are low compared with those of ET, LC, and GM. For ST, the accuracies of P1+P2, P1, and P2 are, respectively, 2.8%, 0.0%, and 0.0%. The respective accuracies of SP and NP are 0.0%. We analyzed these unbalanced results using confusion matrices. Tables 17 present confusion matrices. These matrices demonstrated that EX was hindered by some slight confusion with LC in all combinations of the sensor module installation patterns. By contrast, numerous LC signals were confused with RD signals. Particularly, marked confusion occurred between LC and SP for P2 compared with those for P1+P2 and P1. For SP, confusion occurred in all categories, except for EX and ST. For GM, confusion among LC, RD, and NP occurred frequently. Confusion with the other categories occurred less. Although confusion among ET, SP, and ST occurred, the other categories showed less confusion. Numerous instances of confusion occurred between EX and LC. This tendency differs from the confusion property for LC, which caused a few instances of confusion against EX.