## 1 Introduction  
The rapid development of digital information processing technology provided an opportunity to explore the behavior of the animals aside from direct observation by humans [1]. The crucial step in understanding animal behavior, which includes the activity patterns and emotional patterns, is to create specific ethograms related to that species by monitoring the body postures and the physical movements [1; 2; 3]. One approach for monitoring these behaviors needs a continuous collection of data by using human observers. However, this approach is not ideal because (1) continuous observation by a human and interfering with the normal life of pets by continuous observation can have a negative impact on a pet's behavior and health. (2) The observation process is costly and labor-intensive. The other approach for the collection of data is by leveraging an automated system without interfering with pets' normal lives. The automated system that is available now uses sensors and it can be directly attached to dogs; or it creates an environment using the sensors for the collection of data [4]. In recent years, it has been observed that automated systems for the identification of behaviors are getting popular because the attached sensors in the automated systems have the potential to discriminate various activity patterns and emotional patterns. In the domain of biosignal processing, it is a well-known fact that comprehending the information from multiple sensors attached to one's body allows an increased level of precision and accuracy. For the case of decision making with respect to one's health, state of mind, activity, and emotion, there are a lot of factors to consider, and therefore, leveraging multiple body sensors in this case is deemed necessary. Additionally, fetching the data from multiple body sensors and performing a fusion with respect to the features enables much more information acquisition, as it improves the signal to noise ratio, reduces ambiguity and uncertainty, increases confidence, enhances the robustness and reliability of the data, and improves the data' resolution [5; 6; 7]. However, in the last decade, the implementation of multiple body sensors for the resolution of a particular decision was a subject of debate, as there were limited resources and robust frameworks for the management of multi-sensory data. But with the advent of body sensor networks (BSN) and enterprise cloud architectures, the process of rapid prototyping and deployment has become much easier [8; 9]. With respect to the case of animals, the body movement characteristics and the tail movement characteristics are important for the identification of behavioral patterns. Therefore, accelerometer and gyroscope sensors are normally used in automated systems. The accelerometer sensors can measure the static accelerations and the dynamic acceleration of the body movements and the gyroscope can measure the rotational movement of the body [10; 11; 12; 13]. In this study, an automated system was developed to understand the activity patterns of dogs by collecting accelerometer and gyroscope data respectively, with wearable devices being placed at the neck and the tail of the dog; and their emotional patterns were studied by collecting the accelerometer and gyroscope data of the tail. Seven distinct activities--walking, sitting, \"stay,\" eating, \"sideway\" (moving sideways), jumping, and nose-work were chosen for collecting the data to automate the activity pattern, and three emotional states--positive, negative and neutral, were chosen, based on the tail wagging of the dog to automate the emotional pattern detection system. The positive behavior was indicated when the dog was wagging the tail to the right side of the body. The negative was indicated when the dog was wagging the tail to the left side of the body. The neutral position was indicated by the straight wagging position, during which it does not sway either to the right or to the left of the body [14].