## 7 Conclusions  
In this paper, activity detection and emotion detection systems were proposed; they used wearable devices that leveraged incremental machine learning models. The methodology presented in the study demonstrates the effectiveness of using wearable sensor-based techniques and machine learning algorithms for the quantification of behaviors and for the development of automated systems. A challenge that was addressed here was the collection of data for different activities, because fetching data from animals is a very difficult task that had to be performed. Considering the earlier reports, this work plotted some very new findings, such as (1) using accelerometric and gyroscopic data together for the detection of activity and emotion detection; (2) collecting data at two different locations, the neck and tail, that provided a lot of samples for making a robust model for the development of an automated system; (3) quantifying emotional state based on tail wagging, which is novel considering the use accelerometric and gyroscopic data, yet was effectively presented in this paper; (4) using incremental learning methods effectively, to achieve improved accuracy over conventional machine learning methods.  
The limitations of this study are as follows. (1) The study did not take any additional cues for the development of its emotion detection model; this study only used tail wagging data for classifying different emotional states of the dog. (2) The emotion detection model that was developed in this study on the basis of tail wagging needs further validation to be implemented in real-life situations.  
The methodology proposed in this paper could be improved by taking into account the physiological signals which are directly or indirectly related to behavioral patterns. The system was developed using accelerometer and gyroscope data, but in future, other parameters, such as galvanic skin responses, respiration, and heart rate data have to be included to develop a more robust model for the detection of the behavioral patterns. As previous pieces of literature already mentioned the importance of heart rate and physiological data for the detection of emotional states, considering these data with the tail data could make the model more robust and provide much-improved accuracy. The outcome of this study provided us with a lot of confidence that this system can be used in real-life situations for detecting the activity patterns and the emotional patterns of pet dogs for monitoring their health and well-being.  
Conceptualization, S.A. and S.C.; methodology, S.A. and S.C.; validation, S.A., S.C., and H.-C.K.; formal analysis, S.A., S.C., and H.-C.K.; data curation, J.-S.S., S.A., and S.C.; writing--original draft preparation, S.A. and S.C.; writing--review and editing, S.A. and S.C.; supervision, H.-C.K. and D.-J.J.; project administration, H.-C.K.; funding acquisition, H.-C.K.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Location of sensor** & **Accuracy** \\ \hline Our work & Neck and Tail & 96.58\% \\ [PERSON] [23] & [PERSON] midway between the two & 91.3\% \\ [PERSON] [19] & [PERSON] & 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: A comparison of our result with state-of-the-art models’ work for activity detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Author** & **Accuracy** \\ \hline Our work & 92.87\% \\ [PERSON] [46] & 75.56\% \\ [PERSON] [47] & 73\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: A comparison of our result with state-of-the-art models’ work for emotion detection.  
**Funding:** This research was supported by the Ministry of Trade, Industry, and Energy (MOTIE), Korea, through the Education program for Creative and Industrial Convergence (grant number N0000717).  
**Conflicts of Interest:** The authors declare no conflicts of interest.