## 2 Related Work  
Recently, there has been an increasing scholarly interest in aggregating wearable data with official health records to give HCPs an integrated view of an individual's health. Smartwatches collect enormous amounts of data using different sensors and they can record data points per day, minute, or second, thus generating hundreds of megabytes of data per user. With the advancement of sensors for heart rate monitoring and the introduction of Electrocardiography (ECG) and Photoplethysmography (PPG) sensors in smartwatches, several authors have focused on the detection of cardiovascular diseases. Using wearable PPG sensors, [PERSON] and [PERSON] [21] created a multitask deep learning model to evaluate the signal quality and arrhythmia event detection for real-time identification of atrial fibrillation. [PERSON] and [PERSON] [22] too developed a deep neural network for the passive detection of Atrial Fibrillation using smartwatch data and validated it against the reference standard 12-lead electrocardiography (ECG) diagnosed Atrial Fibrillation.  
Additionally, the COVID-19 outbreak has also accelerated the development of cutting-edge medical technologies that allow care delivery without requiring direct physical contact. A growing body of research has demonstrated the utilization of smartwatches in predicting and preventing COVID-19 by employing anomaly detection algorithms [6; 7; 8].  
Wearable technology, therefore, creates opportunities for real-time remote health monitoring by HCPs. This technology has the potential to transform the healthcare industry and medical practices for better diagnosis, timely treatment, and preventive healthcare. Commercially available wearables, including the Fitbit, Apple Watch, Samsung Gear, and Xiaomi Mi, have shown reasonable performance in accuracy for physiological parameters, leading these devices to be increasingly used in research [2; 14]. Data processing and continuous monitoring of physiological parameters recorded by wearables may foster more efficient and reliable diagnoses. For example, heart rate is a physiological parameter that is clinically relevant, and an abnormal heart rate over time can give an indication of an individual's overall health. These abnormal data points that deviate significantly from most data and do not conform to expected behaviour are called anomalies [23], which can be detected using mathematical models and machine learning algorithms.  
Increasing research efforts are directed at exploring continuous and remote health monitoring and the integration of smartwatch data to predict, detect, or manage various diseases. [PERSON] and [PERSON] [24] and [PERSON] and [PERSON] [25] proposed solutions to aggregate data from multiple wearable devices on a central database server or by creating a mobile app to enable interoperability between wearable devices. [PERSON] and [PERSON] [26] have also developed a framework for real-time and online assessment of mobility monitoring (ROAMM) to monitor physical activity based on smartwatch data for visualization and summary statistics. Their framework included a mobile application to collect and pre-process data and a server for storing and retrieving data and remote monitoring. However, before integrating smartwatches into health monitoring, a thorough analysis of their accuracy and influence on work practices should be performed for the smartwatch to obtain greater acceptance from healthcare experts. It is necessary to develop real-world cases focused on the needs of HCP in order to assess the usefulness, user-interface design, and usability [2].