# Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope
###### Abstract  
Dog owners are extremely driven to comprehend the activity and health of their dogs and to keep tabs on their well-being. Dogs' health and well-being, whether as household pets or service animals, are critical issues that are addressed seriously for moral, psychological, and economical reasons. Evaluations of a dog's welfare depend on quantitative assessments of the frequency and variability of certain behavioral features, which are sometimes challenging to make in a dog's normal environment. While it is challenging to obtain dogs' behavioral patterns, it is nearly impossible to directly identify one distinct behavior when they are roaming around at will. Applications for automatic pet monitoring include real-time surveillance and monitoring systems that accurately identify pets using the most recent methods for the classification of pet activities. The suggested method makes use of a long short-term memory (LSTM)-based method to detect and classify the activities of dogs based on sensor data (i.e., accelerometer and gyroscope). The goal of this study is to use wearable sensor data and examine the activities of dogs using recurrent neural network (RNN) technology. We considered 10 pet behaviors, which include walking, sitting, down, staying, feeding, sideways, leaping, running, shaking, and nose work. As dog activity has a wider diversity, experimental work is performed on the multi-layer LSTM framework to have a positive influence on performance. In this study, data were collected from 10 dogs of various ages, sexes, breeds, and sizes in a safe setting. Data preprocessing and data synchronization were performed after the collection of data. The LSTM model was trained using the preprocessed data and the model's performance was evaluated by the test dataset. The model showed good accuracy and high performance for the detection of 10 activities of dogs. This model will be helpful for the real-time monitoring of dogs' activity, thus improving the well-being of dogs. pet activity detection; dog activity; accelerometer; gyrometer; wearable sensors; LSTM; classifier; RNN +
Footnote †: journal: Applied Sciences  
Animal activity recognition has grown in popularity recently because of its applicability in a variety of industries, including remote monitoring, security, and health. Additionally, more people are able to contribute to this subject thanks to the rise in sensor-based technologies. The low cost of numerous sensors and the quick development of technology, particularly sensor devices, make it simple to monitor the everyday activities of pets. Low-voltage pressure sensors provide long-lasting and better performance for advance sensing furthermore it is and are gaining much attention [1]. Wearable technology is utilized to identify activities. Wearable gadgets are seamlessly enabled in the activity recognition process by inertial sensors and embedded systems. Wearable sensors have been developed for activity detection and are utilized in daily life and sporting activities. They combine embedded systems with acceleration and gyro sensors. These days, they are used in a wide range of everyday contexts, such as animal welfare evaluation, medical monitoring, therapeutic interventions, activity recognition, remote control, and health care [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. The research is clear on the value of accelerometry data for developing supervised machine learning models to categorize diverse animal and livestock behaviors, particularly those of cattle. There are several examples [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] of such research. However, there has not been much work performed to implement behavior categorization on a wearable device with an accelerometer and gyro sensors [24; 25; 16]. Few scientific articles highlighted in recent years [26; 27] contend that understanding an animal's activity patterns might help us better comprehend its health and welfare.