Unobtrusive Smart Mat System for Sleep Posture Recognition

Abstract

Sleep posture, as a crucial index for sleep quality assessment and pressure ulcer prevention, has been widely studied for medical diagnoses and sleep disease treatment. In this paper, an unobtrusive smart mat system for sleep posture recognition is proposed, which is based on a dense flexible sensor array and printed electrodes and along with an algorithmic framework. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared with other large-area and low-density mat systems, it reduces the area to minimize manufacturing cost and computational complexity, while also increases the density of the sensor to improve accuracy. To distinguish the sleep postures, the algorithmic framework that includes pre-processing, feature extraction, and posture classification is developed. Pilot studies in two scenarios including subject-dependent and subjectindependent classification are performed with 7 persons for 4 different postures recognition. The experimental results show that the accuracy of the smart mat system can achieve over 78% using Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) for the subject-independent scenario. For the subject-dependent scenario, the accuracy can reach over 95%. It proves that the proposed method can recognize different sleep postures effectively.

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

“…However, it is not popular for human posture estimation based on interaction forces. Raw data [51], statistics [50,52], sliding window [40,50], HOG [39,46] [14, 16,22,34,[39][40][41]46,[50][51][52][53][54][55][56][57][58][59][60] K-nearest neighbor (kNN) 78% [34]-98.52% [22] Raw data [61,62], statistics [50], sliding window [50] [ 13,22,34,40,41,47,50,54,55,[61][62][63][64][65] used for regression: [12] Convolutional Neural Networks (CNNs) The classification accuracies reach values above 90% for the most used algorithms (see Table 3). Data pre-processing is used in most of the studies to improve the quality of the data input to the algorithms.…”