###### Abstract
Various types of pressure sensors, accelerometers, and gyroscope sensors have been used for sleep pattern and posture recognition [20]. The smart mat system, based on a dense flexible sensor array and printed electrodes for sleep posture recognition and sleep activity monitoring, is presented in [20]. Force-sensing resistors are incorporated into the mat to capture the pressure distribution of the human body, and machine learning methods including pre-processing and deep residual networks are applied to classify sleep postures [7]. The IoT-based smart bed systems are designed to support care assistants for elderly patients and patients with limited mobility. The use of the Internet of Things (IoT) and artificial intelligence in modern healthcare has promising technological, economic, and social implications [21]. All health information can be collected, managed, and used more efficiently due to the global connectivity of IoT. Wearable health management systems offer low-cost solutions for non-invasive personal health monitoring, enabling early diagnosis and better treatment for various medical conditions. The advanced intelligent IoT proposes the innovative textile pressure Velostat sensors to observe twenty-one people in four lying positions for preventing fall accidents and bedsores through the message queuing telemetry transport (MQTT) protocol and Arduino-based hardware [22]. Additionally, an intelligent Velostat mat for monitoring body position by obtaining pressure distribution on the patient's body and posture prediction by ANN for medical applications is presented in [23]. A low-cost pressure sensor array consisting of conductive fabric and wires has been designed for sleep posture recognition by a shallow convolutional neural network (CNN) or by multiple accelerometers. The current paper presents the characterization of a sensitive mat based on Velostat and a control system adopted as the target solution to improve medical diagnosis. * The fabrication, method, and materials of the mat surface are described using simple facilities. * The different design and geometry of homemade sensor mats (Figure 1a,b) are proposed to explore the electrical and mechanical properties, including repeatability, through the conducted tests. * A data acquisition system using software and an Arduino microcontroller is provided to automate the measurements of mat sensors in the test bench. * Thus, the approximation method based on the neural network algorithm is developed to achieve a relation between the different geometries of mats, the pressure and voltages applied to the mat, the resistance of the conductive material, and the number of sensing cells. The novelty of this method involves an intelligent system completed by a measurement system, a micro-controller, and peripherals, including an innovative algorithm for the description of the characteristics of the 2D pressure-sensing matrix based on commercial materials that can be integrated in real-life conditions, i.e., installed onto a patient's bed. Additionally, a neural network for assisting in positional detection of applied pressure was developed in that system. The smart mats can be integrated with innovative technology and materials in order to improve the design process and user comfort while providing health benefits such as sleep monitoring functionality, sleep quality and anti-snoring, detecting human postures, and counting posture cycles. A variety of different data such as heart rate, respiration, sleep quality, and blood pressure can be provided by smart mat sensors able to analyze the psycho-physical factors related to sleep in a non-invasive way. The analysis of the electrical performance of the mat using new techniques can help manufacturers to create higher quality and efficient products, to monitor sleep and rest patterns, and to optimize sleep activity connecting with smart devices, Wi-Fi, and IoT.