Decoding Posture: Distinguishing Between Good and Bad Body Alignment

Abstract

On the grounds of artificial intelligence sensors, the research focuses on achieving precise detection of intelligent posture to minimize the risk of injuries resulting from incorrect body positioning and to mitigate the negative impact on both performance and physical well-being. Utilizing an attitude analysis and recognition system that relies on the nertial measurement unit enables not only the measurement of human body motion information but also the acquisition of data pertaining to motion characteristics and body movement states through the examination of posture data. A proposition is presented for intelligent posture training that is centered on accuracy and real-time feedback. Furthermore, it is elucidated that the accuracy of posture recognition through machine learning is significantly influenced by variations in user BMI. This paper will introduce the most recent advancements in posture recognition techniques, providing an overview of the various methodologies and algorithms that have emerged in recent years. Additionally, it delves into enhanced approaches such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The study thoroughly dissects and consolidates the general procedures and datasets involved in posture recognition, comparing several enhanced CNN methodologies alongside three principal recognition techniques. Moreover, it delves into the utilization of advanced neural networks in posture recognition, including but not limited to transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks. Researchers have observed that CNN has made significant strides in the realm of posture recognition and has garnered favor, yet emphasize the necessity for further exploration into areas such as feature extraction and information fusion.