## 1 Introduction
An IoMT platform must be adaptable in such a way that enable the use of existing communication protocols and the integration of new ones [11]. However, there is a lack of proposals that faced this issue and there is a tendency for the definition of platforms that considers only one communication approach (not standardized in many cases);
3. Electronic health records [12] (EHRs) have been widely used for storing patient data in hospital information systems. However, the lack of proposals for their integration with IoMT platforms [13] has caused health professionals to manage a patient's clinical history with distributed records provided by multiple sources. This requires the knowledge of several platforms and applications, in addition to human effort and time for migrating information and data among them; and
4. The volume and complexity of the data collected in IoMT solutions hamper their interpretation by physicians, hence, leading to difficulties in dealing with big data. Such challenges advocate the use of intelligent algorithms that correlate vital signs and extract valuable insights hidden in healthcare data repositories [14]. Even though big data techniques, such as Map/Reduce [15], linear regression, classification, and clustering have become popular solutions, the lack of standards for data integration and standardization impede their application. Several studies have aimed at solving such issues. [PERSON] et al. [16] analyzed the impact of health data source heterogeneity on the application of data processing and knowledge-extractiontechniques (i.e., training of machine learning models for decision support). The representation of data collected for granting compatibility among different IoMT platforms and their sharing among different applications is highly relevant. An EHR maintains a patient's medical history using a unique identifier, e.g., the national health ID, for relating a patient's health data. It can be implemented locally, within a single institution, or developed in more complex structures (i.e., at country or international level). Its main objective is to facilitate access to information in relation to a wide range of new applications and architectures that follow the modeling of the data adopted in the EHR. Organizations, such as OpenEHR [17], have standardized EHRs models. However, their use involves collection, transformation, and loading of data handled by heterogeneous IoMT solutions. This process can be conceptualized as \"Collect-Transform-Load\" process (CTL). Nevertheless, it must be studied, since each manufacturer uses proprietary protocols, which hinders their integration with larger systems. Therefore, a CTL mechanism standardized among sensors, IoMT devices and EHRs is required. An IoMT platform must also provide mechanisms for data sharing with third party applications. Standards, such as those developed by Health Level 7 [18] (HL7), enable data sharing among various health systems and applications. However, the EHR data must be translated to HL7-compatible resources. Applications focused on knowledge extraction that work with a large volume of data require more than an API, in order to prevent latency. The platforms must provide mechanisms for the application of data mining and data warehousing techniques that help physicians through clinical decision support systems (DSS). In summary, a modern pervasive healthcare platform should provide, at the minimum, functionalities for:  
1. Storage and management of clinical and demographic data in a standardized EHR in relation to their organization in a human-understandable approach;
2. A CTL mechanism for the integration of multiple IoMT solutions which consider heterogeneous communication protocols;
3. Management of patient clinical data by medical teams (creation, visualization, update and deletion of electronic records); and
4. Knowledge extraction and analytical studies to support clinical DSS services (for example, by the use of OLAP and-or data mining and-or data warehousing techniques).