Recognition Algorithm for Sleep Postures using a Smart Fabric Pad with Multiple Pressure Sensors

Abstract

The existing studies suggest the following methods for measuring sleep postures: installing cameras and record the sleep postures then analyze the postures or measuring the change of the posture by attaching the sensor to the user's body. However, the installation of high-cost devices such as a camera or direct attachment of sensors on the subject's body present issues relating to the cost and convenience. As a solution, this paper develops a recognition algorithm for analyzing sleep postures using a smart pad with embedded fabric type pressure sensors. This algorithm is applied using a fabric-made smart pad with multiple pressure sensors. The sensors detect the distribution of the body pressure on the pad during sleeping, and the collected body pressure distribution data determines the sleep postures of the users. Further, this smart fabric pad does not require any additional analytical devices. This pad allows the users to monitor own sleep postures continuously. With the analyzed sleep posture data, the user can recognize one's typical sleep postures. In order to verify the effectiveness of the algorithm, this paper conducts an experiment to validate using the sleep posture data defined as nine categories. As a result, the algorithm had an average of 91.4% accuracy rate.