## 5 Conclusions  
As a feasibility study based on findings of an HAR mini-survey, this paper presented two original benchmark datasets used to recognize daily life behavior patterns from the assessment of tiny sensor signals generated from slight motions. The first dataset comprises feature vectors from time-series panoramic images obtained using a fisheye lens camera. We used OpenPose and CNN with the VGG-16 backbone to recognize behavior patterns of five types after first dividing them into learning and verification subsets. Our originally obtained first dataset comprises five categories: eating, reading, operating a smartphone, operating a personal computer, and sitting. Recognition evaluation experiments were \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**P1\(+\)P2** & **EX** & **LC** & **SP** & **GM** & **RD** & **EX** & **NP** & **ST** \\ \hline
**EX** & 408 & 23 & 4 & 0 & 0 & 10 & 0 & 0 \\
**LC** & 0 & 2437 & 0 & 62 & 552 & 73 & 0 & 17 \\
**SP** & 8 & 51 & 0 & 11 & 23 & 0 & 22 & 0 \\
**GM** & 0 & 439 & 1 & 2725 & 486 & 28 & 270 & 2 \\
**RD** & 536 & 0 & 468 & 0 & 627 & 0 & 0 & 113 \\
**EX** & 0 & 217 & 0 & 0 & 0 & 197 & 0 & 0 \\
**NP** & 0 & 0 & 0 & 0 & 408 & 0 & 0 & 0 \\
**ST** & 42 & 214 & 0 & 28 & 13 & 16 & 0 & 9 \\ \hline
**P1** & **EX** & **LC** & **SP** & **GM** & **RD** & **EX** & **NP** & **ST** \\ \hline
**EX** & 409 & 23 & 3 & 0 & 0 & 10 & 0 & 0 \\
**LC** & 0 & 1696 & 0 & 114 & 1324 & 7 & 0 & 0 \\
**SP** & 8 & 62 & 0 & 13 & 32 & 0 & 0 & 0 \\
**GM** & 0 & 889 & 0 & 2420 & 642 & 0 & 0 & 0 \\
**RD** & 463 & 0 & 471 & 0 & 481 & 0 & 0 & 29 \\
**EX** & 0 & 323 & 0 & 0 & 0 & 91 & 0 & 0 \\
**NP** & 0 & 0 & 0 & 0 & 408 & 0 & 0 & 0 \\
**ST** & 42 & 222 & 9 & 28 & 13 & 8 & 0 & 0 \\ \hline
**P2** & **EX** & **LC** & **SP** & **GM** & **RD** & **EX** & **NP** & **ST** \\ \hline
**EX** & 411 & 23 & 1 & 0 & 0 & 10 & 0 & 0 \\
**LC** & 42 & 1809 & 849 & 81 & 349 & 7 & 0 & 4 \\
**SP** & 8 & 51 & 0 & 25 & 31 & 0 & 0 & 0 \\
**GM** & 0 & 390 & 0 & 3078 & 483 & 0 & 0 & 0 \\
**RD** & 458 & 0 & 351 & 0 & 635 & 0 & 0 & 0 \\
**EX** & 0 & 318 & 0 & 0 & 0 & 96 & 0 & 0 \\
**NP** & 0 & 0 & 0 & 0 & 408 & 0 & 0 & 0 \\
**ST** & 42 & 80 & 9 & 28 & 155 & 8 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Confusion matrices for the results shown in Figure 10. Underlined values are the maximum numbers of signals in the respective recognition categories. conducted by changing the combination of subsets. The obtained results demonstrated characteristics of behavioral pattern recognition obtained from wide-field images taken using a fisheye lens camera, even for panoramic images that included distortions. The second dataset was obtained using five environmental sensors. We were concerned not only with non-constraint but also with privacy awareness for this environmental sensor system. Using LSTM combined with CNN, we attempted to recognize eight behavior patterns. Comparing these datasets, the recognition accuracy of the second dataset was 38.9 percentage points lower than that of the first dataset. Nevertheless, we demonstrated the possibility of recognizing behavior patterns from time-series of weak sensor signals. We consider that the recognition results obtained from the first dataset after the accuracy evaluation can be reused for automatic annotation of labels to the second benchmark dataset. As a subject of future work, we must develop an integrated system that is able to use the two datasets for automatic annotation and autonomous learning without using manually created labels. The limitation of this study is that respective dataset involve only one subject. We intend to develop a dataset accumulated from multiple subjects. Furthermore, intend to expand the categories of target behavior patterns, including data types and volumes. Moreover, our future studies are expected to clarify relations between emotions and behavior patterns through field tests conducted in unusual living environments such as accommodation facilities. Conceptualization, H.M.; methodology, H.M.; software, S.N. ; validation, H.W.