# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 1 Introduction  
Invention regarding human health is becoming more popular in the science world. Intelligent and regular health monitoring is necessary to diagnose vital signs such as human respiration, heart rate, etc., at the early stage. In particular, regular monitoring of the patient's state of health is mandatory to avoid significant losses. Healthcare monitoring requires reliable and efficient follow-up on regular operations, resources, and services. Smart healthcare systems need artificial intelligence (AI) and the Internet of things (IoT) to develop smart cities that reduce patient waiting time [1]. Several challenges still exist in ubiquitous healthcare systems such as frequent mobility, scalability, flexibility, reliability, and energy efficiency issues. Successful deployment of smart e-health gateways can ensure that the ubiquitous health monitoring systems are used extensively, especially in the smart hospital environment [2]. Quantifying uncertainty in cloud-based computing for different environments is an important factor for reliability in various configurations [3]. It is essential to build highly secure features based onsmart healthcare applications [4]. The development of intelligent and smart medical services reduces the likelihood of death [5]. When solving health care problems, researchers participated in the development of intelligent platforms and the search for promising solutions. According to international perspectives on spinal cord injury from the World Health Organization (WHO) and the International Spinal Cord Society (ISCOS), international incidence data suggest that each year between 250,000 and 500,000 people become spinal cord injured. Most of these cases are traumatic spinal cord injury, the leading causes of which are road traffic injuries, violence, and falls. Usually, these cases require surgical intervention [6]. Even though there are many systems in place for health monitoring, existing platforms such as computer vision, wearable devices, or sensors are relatively mature [7; 8], but they have a lot of disadvantages; for example, the computer vision platform cannot be used in private places such as the bathroom and changing room, whereas the wearable sensor platform can be uncomfortable when wearing it all the time, especially for elders and children, and also some of existing platforms do not work in real-time. Studies have shown that human existence and their movements affect the channel state information (CSI) of Wi-Fi signals using OFDM technology [9; 10]. CSI has an advantage over light, infrared, or thermal energy when trying to infer people's movements [11; 12]. WCSI based detection techniques were successfully applied to several applications, such as respiration detection and fall detection [13; 14]. These techniques completely depend on AI based machine learning algorithms that require training data for all the detection of target activities [15]. In this paper, our goal was to develop a smart health care system in which computation and data storage are closer to the place where it is needed, and to improve reliability, flexibility, scalability, and portability. The primary contributions of this paper are summarized below:  
* We verify the feasibility of non-contact sensing using USRP by exploiting AI for post-surgery activities detection, especially weight lifting activity. To the great of our knowledge, this is the first work using universal software-defined radio peripheral (USRP) to extract WCSI for healthcare purposes and to monitor patients' activities. * We suggest a dynamic feature extraction technique of WCSI, which efficiently decreases the influence of environmental changes. * We only depend on training data derived in ordinary situations. We collect data when a patient is normally lifting things and feed them into an algorithm to avoid collecting the data after when the patient had surgery, which is hard to collect in practical use. * We measured the execution of this technique in an actual environment, which we perform in our lab.