## 3 Materials and Methods
### Proposed Patient Monitoring Scheme  
In parallel to the region-level monitoring, there is a need to have a continuous basic monitoring scheme for women during and after pregnancy. Due to improper monitoring of maternal health during and after pregnancy, many women die from pregnancy-related illnesses. This is especially prevalent in rural and low-income urban areas of developing countries. Identifying the main causes of these maternal health issues is essential for implementing effective early warning systems [9]. Accordingly, the patient data focus on individual patient health metrics. They include physiological parameters such as blood pressure, blood sugar, body temperature, and heart rate, as well as demographic information like age and weight. These factors are very important when assessing a patient's risk of developing pregnancy-related complications. The proposed model analyzes these factors to identify the patients who are at high risk and provide timely interventions. The Internet of Medical Things (IoMT) is a network of medical devices and applications that leverages IT to collect and transmit medical data for storage, analysis, and delivery of healthcare and e-healthcare services [18]. Following the basic architecture of the IoMT, the proposed model is presented in Figure 2, in which sensors (for example, those included in a health watch) are deployed to continuously collect readings to monitor the health condition of a pregnant woman. In this model, readings from blood pressure, pulse oximeter, temperature, and glucose level sensors are collected and input to a trained XAI model, which analyzes the data and accordingly produces the factors behind the risk, and accordingly, a physician may suggest a dietary or exercise recommendation to the patient. The collected sensor readings and the recommendation of the XAI model are collectively transmitted to a data center for storage, to the hospital, and/or to a physician responsible for the follow-up of the pregnant woman's state. Depending on her health condition, a hospital and/or a physician could remotely alter the medications of a pregnant woman and send back the new prescriptions, which could be automatically ordered through smart in-home devices. Security and privacy are among the most critical challenges associated with the deployment of the IoMT. The IoMT includes various types of devices and connections that could be targets for security and privacy attacks resulting in the leakage of sensitive data and the possibility of data tampering causing inaccurate diagnoses, which in turn can  
Figure 1: Proposed region monitoring scheme. result in improper prescriptions, treatments, and in extreme cases could potentially be life threatening [18; 19]. Recommendations for secure IoMT development fall into the following three categories: device security, connectivity security, and cloud security [20]. Possible security attacks on IoMT devices include side-channel attacks, device tampering, tag cloning, and sensor tracking. Attacks at the connectivity or network level include eavesdropping, replay, man-in-the-middle, rogue access, denial of service (DoS)/distributed denial of service (DDoS), sinkhole, sniffing, and selective forwarding. Finally, attacks at the cloud, data centers, or hospitals where data storage, analytics, and visualization are carried out include attacks such as brute force attacks, SQL injections, account hijacking, and ransomware [18]. To ensure the trust and confidence of IoMT stakeholders, various security measures should be implemented. To guarantee the legitimacy of participants and devices in the IoMT, diverse authentication schemes are deployed in the IoMT, such as key-based authentication, multi-factor authentication, and digital certificates [21; 22].