## 5 Conclusions and Future Work  
Thus far, the analysis of behaviors in companion dogs has required the animals to wear devices equipped with sensors. However, it might be difficult to equip dogs with these devices, and the devices themselves might cause the dogs to feel uncomfortable. The proposed mobile app in this study recognizes dog behaviors using the VideoPose3D model to estimate the dogs' 3D poses based on their 2D poses estimated using the DeepLabCut model. Thus, the technology enables users to analyze their dogs' behaviors without requiring sensors. As few studies on animal-pose estimation have been conducted [1; 2; 3; 22; 27], it is difficult to obtain relevant datasets. The motion capture of an object needs to be performed to measure 3D poses. Certain motions of animals cannot be tracked by motion capture because of the difficulty in controlling these animals. Furthermore, the motion capture of animals is quite expensive, given the need for trainers and well-trained animals. However, the technology proposed in this study successfully generated a dataset of joint locations in dogs by estimating their 3D poses based on multi-view RGB images. In addition to the two aforementioned technical contributions, this study also proposes a mobile application that can recognize stress-related behaviors in companion dogs based on the 3D poses obtained through deep learning. When their stress becomes unbearable, dogs express their physical and mental conditions through their behavior. However, people often cannot easily recognize dog behavior because the physical appearance--rather than the actions--of dogs draws their attention at first glance. We developed a mobile application that identifies stress levels in dogs by recording their walking time and time spent alone, followed by analyzing problematic stress-related behaviors based on circling, as well as environmental stress factors. In-depth interviews were conducted with five experts with regard to the proposed app's performance for the practical management of stress in companion dogs. The interview results verified a relationship between the dog's walking and isolation times, and the amount of circling that they perform as a result of their stress. However, the relationship between the walking time and stress level was evaluated to be uncertain. All five experts emphasized the importance of the relationship between dogs and their owners as additional stress-related environmental information. In addition, it was confirmed that the app is capable of successfully informing users about the meaning of their dogs' behaviors and stress level. The VideoPose3D model estimates 2D poses of dogs from single-view RGB videos, and subsequently estimates 3D poses from the 2D poses. If an unstable 2D pose estimation is derived from a single-view RGB video, this result might also lead to an unstable 3D pose estimation. A high-quality 2D dog-pose estimation result can be obtained only when the camera angle and position of the dog in the single-view RGB video are similar to those in the training dataset of the DeepLabCut model. Therefore, follow-up research should consider camera-based imaging techniques in the process of generating datasets. The future work directions with respect to good stress management for companion dogs are two-fold. Firstly, we need a comprehensive analysis that includes not only subtle differences in a dog's body parts but also the tendencies of dogs, as experts stated that stress can also be expressed through the facial expressions of dogs. They also mentioned that different breeds might express stress differently. Secondly, we set the walking and isolation times of companion dogs as stress-related environmental information. However, in addition to external environmental information, such as the walking and isolation time of companion dogs, internal environmental information such as the dog-owner relationship can also serve as significant stress-related environmental information.