A Real-time Posture Recognition System using YOLACT++ and ResNet18
Jiacheng Lin, Chen-Chiung Hsieh · 2021
This paper presents a deep learning based real-time two-stage gesture recognition system. In the first stage, the system deploys the YOLACT++ model to detect the human body mask. Then four ResNet18 models are used separately to predict the correctness of push-up, the number of push-ups, correctness of sitting posture, and the flexibility of the standing forward bend. The accuracy rates of these four ResNet18 models reached 97.9%, 97.5%, 95.1%, and 95.4%, respectively, using the test dataset collected from ten individuals.