부산대학교 도서관

Simple Summary: This study explored the use of mobile phone sensors to accurately classify the gaits of five-gaited horses. The data were collected from horses and riders using a mobile phone in the rider's pocket and an existing multi-sensor gait classification system. A machine learning model was then trained to classify the gaits using input from the phone's accelerometer and gyroscope, achieving an accuracy of 94.4%. This research demonstrates that mobile phones can be used to gather data on horse gaits, reducing the cost of large-scale studies. This efficient method for acquiring labelled data will be invaluable for ongoing research into horse riding activities. Automated gait classification has traditionally been studied using horse-mounted sensors. However, smartphone-based sensors are more accessible, but the performance of gait classification models using data from such sensors has not been widely known or accessible. In this study, we performed horse gait classification using deep learning models and data from mobile phone sensors located in the rider's pocket. We gathered data from 17 horses and 14 riders. The data were gathered simultaneously from movement sensors in a mobile phone located in the rider's pocket and a gait classification system based on four wearable sensors attached to the horse's limbs. With this efficient approach to acquire labelled data, we trained a Bi-LSTM model for gait classification. The only input to the model was a 50 Hz signal from the phone's accelerometer and gyroscope that was rotated to the horse's frame of reference. We demonstrate that sensor data from mobile phones can be used to classify the five gaits of the Icelandic horse with up to 94.4% accuracy. The result suggests that horse riding activities can be studied at a large scale using mobile phones to gather data on gaits. While our study showed that mobile phone sensors could be effective for gait classification, there are still some limitations that need to be addressed in future research. For example, further studies could explore the effects of different riding styles or equipment on gait classification accuracy or investigate ways to minimize the influence of factors such as phone placement. By addressing these questions, we can continue to improve our understanding of horse gait and its role in horse riding activities. [ABSTRACT FROM AUTHOR]