Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope
| Autor: | Ali Hussain, Khadija Begum, Tagne Poupi Theodore Armand, Md Ariful Islam Mozumder, Sikandar Ali, Hee Cheol Kim, Moon-Il Joo |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 12, Iss 19, p 9427 (2022) |
| Druh dokumentu: | article |
| ISSN: | 12199427 2076-3417 |
| DOI: | 10.3390/app12199427 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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