## 1 Introduction
Our system was developed based on the data collected from 10 different dogs of various sizes, breeds, and ages. The objective of this study was to develop an automated system that could accept accelerometer and gyroscope data as input and distinguish different activity patterns and emotional patterns using supervised machine learning algorithms. An evaluation of the system was performed using supervised machine learning algorithms--an ANN (artificial neural network), a random forest, a SVM (support vector machine), KNN (k nearest neighbors), and a naive Bayes classifier. Most of the past studies focused on the activity detection of different animals using wearable accelerometers but very few have considered both accelerometer and gyroscope sensors for activity detection. On the other hand, various studies in the past found the relationship between the tail wagging and emotional response by using visual stimulation. The visual stimulation creates an abnormal behavior in the body that changes the cardiac activity, that in turn changes the emotional responses. As the tail wagged towards the right, higher cardiac activity was observed in the dog, and with the tail wagged to the left, lower cardiac activity was observed in the dog [15]. Past studies provide enough evidence that heart rate has a direct relationship with the emotions of human beings and animals [16]. Based on the evidence, few studies used electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmography (PPG) signals to detect the emotional pattern of human beings [17]. At the same time, a few other studies considered accelerometer data for emotional detection, and so far nobody has considered the combination of accelerometer and gyroscope sensors for emotion-detection. After getting lot of evidence from past studies, we decided to use neck-wearable and tail-worn sensors for detecting the activity patterns and emotional patterns of household pets. In this study, we made the first attempt to develop an automated system that uses neck and tail-worn sensors data for activity and emotion pattern classifications, respectively. We used conventional machine learning techniques and incremental machine learning techniques for the performance comparison. We also tested our system with validation sets of data and received optimum prediction performance from the machine learning models, which indicates that our system has the potential to be used in real-life situations. The structure of the paper is organized as follows: Section 2 describes the related work regarding the study, where all the state-of-the-art methods in the wearable sector and activity detection are duly discussed. Section 3 provides a behavioral understanding with regard to the data collection procedure and the experimental procedure. Section 4 provides the methodology of the complete work and also includes the featured engineering process and gives an overview of the usage of classification algorithms for solving our particular problem. Section 5 provides the results that were generated from the complete work. Section 6 discusses study overall, and its future implications. Finally, Section 7 concludes the paper.