SitPAA: Sitting Posture and Action Recognition Using Acoustic Sensing

Abstract

The technologies associated with recognizing human sitting posture and actions primarily involve computer vision, sensors, and radio frequency (RF) methods. These approaches often involve handling substantial amounts of data, pose privacy concerns, and necessitate additional hardware deployment. With the emergence of acoustic perception in recent times, acoustic schemes have demonstrated applicability in diverse scenarios, including action recognition, object recognition, and target tracking. In this paper, we introduce SitPAA, a sitting posture and action recognition method based on acoustic waves. Notably, our method utilizes only a single speaker and microphone on a smart device for signal transmission and reception. We have implemented multiple rounds of denoising on the received signal and introduced a new feature extraction technique. These extracted features are fed into static and dynamic-oriented networks to achieve precise classification of five distinct poses and four different actions. Additionally, we employ cross-domain recognition to enhance the universality of the classification results. Through extensive experimental validation, our method has demonstrated notable performance, achieving an average accuracy of 92.08% for posture recognition and 95.1% for action recognition. This underscores the effectiveness of our approach in providing robust and accurate results in the challenging domains of posture and action recognition.