# Sleep Posture Recognition Method Based on Sparse Body Pressure Features
## 2 Related Work  
In recent years, machine learning (incl. deep learning) models have achieved considerable theoretical advancements in sleep posture identification. A main challenge facing research on this subject is the collection of the requisite dataset and its associated processing, encompassing both non-contact and contact approaches [13]. Non-contact methods mostly encompass thermal cameras, radar, and similar technologies. A thermal camera is the most straightforward method for obtaining a user's sleep posture [14; 15]. For instance, researchers like [PERSON] et al. [16] employed Red Green Blue (RGB) and thermal cameras for the identification of sleep postures. That study categorized sleep into two conditions, with and without blankets. In the absence of blankets, the quadratic support vector machine attained an accuracy of 96.7%. Conversely, in the presence of blankets, the linear support vector machine achieved a recognition rate of 96%. Additionally, the fusion of normally distributed sequences reached a recognition rate of 99%. Nonetheless, the utilization of thermal cameras for recognizing sleep posture through the capture of thermal radiation imaging of the human body presents significant privacy concerns due to the detailed thermal imaging contours, including sensitive areas. This issue positions radar technology for sleep posture recognition as a promising alternative [17; 18; 19]. [PERSON] et al. [20], employed a triple ultra-wideband radar system to transmit electromagnetic waves and acquire inverse positional change data, ultimately determining that the Swin Transformer with lateral and head radar configurations attained the highest prediction accuracy of 80.8%. Nonetheless, the expense associated with radar utilization is excessive for daily application. In the domain of sleep recognition, contact-based methods are frequently employed, such as accelerometers and pressure sensors. Accelerometer sensors are employed to gather data on subjects' indicators using devices such as bracelets and headgear [21; 22]. [PERSON] et al. [23] employed triaxial acceleration signal monitoring to develop a system for monitoring human bed activity and sleep posture. Their triaxial accelerometer sensors, utilizing a single GY-521 sensor, were positioned on the abdominal muscles to gather data, which was subsequently classified using the K-Nearest Neighbor (KNN) algorithm, achieving an average classification accuracy of 93%. [PERSON] et al. [24] described a portable wearable device that uses a single accelerometer to monitor 12 sleep positions. Experiments were conducted on data from 15 participants and used a lightweight deep learning model, AnpoNet, which had a classification accuracy of 94.67% \(\pm\) 0.80%. In contrast to accelerometers, pressure sensors do not require wearing and are more suited for home usage, contributing to their popularity in the research of sleeping postures [25; 26; 27; 28]. Among the most suitable for daily application are airbag pressure sensors, as demonstrated in the experiments conducted by [PERSON] et al. [29]. Their primary design features an air spring matrix in the torso area, comprising 121 branching air pathways that segment the torso into four zones, each regulated by four subordinate control units and four air pumps for efficient inflation. Ultimately, the F1 score of the Sparrow Search Algorithm-optimized CNN model reached 99.2%. [PERSON] et al. [30] developed a sleep posture recognition data acquisition system utilizing SPR-DE. This system resembles the one created by [PERSON] et al., comprising three components, the head, torso, and legs. Ultimately, AdaBoost-SVM was employed as the classification model, achieving an accuracy of 99.9%. An air spring matrix, while more precise, is characterized by complexity, high cost, susceptibility to damage resulting in erroneous data collection, and maintenance challenges. [PERSON] et al. [31] proposed a microcontroller-based approach for pose classification using the execution of embedded machine learning models.