Real-time Pilates Posture Recognition System Using Deep Learning Model

Abstract

As the pandemic situation continues, many people exercise at home. Mat Pilates is a popular workout and effective core strengthening. Although many researchers have conducted pose recognition studies for exercise posture correction, the study on Pilates exercise is only one case on static images. Therefore, for the purpose of exercise monitoring, we propose a real-time Pilates posture recognition system on a smartphone for exercise monitoring. We aimed to recognize 8 Pilates exercises—Bridge, Head roll-up, Hundred, Roll-up, Teaser, Plank, Thigh stretch, and Swan. First, the Blazepose model is used to extract body joint features. Then, we designed a deep neural network model that recognizes Pilates based on the extracted body features. It also measures the number of workouts, duration, and similarity to experts in video sequences. The precision, recall, and f1-score of the posture recognition model are 0.90, 0.87, and 0.88, respectively. The introduced application is expected to be used for exercise management at home.