# Embedded AI-Based Digi-Healthcare
## 2 Related Work
The authors used RobustScaler to scale the data to eliminate any outliers. The dataset was divided into two parts of ratio 80-20% for training and testing purposes. The design of this meta-classifier algorithm was successful and it achieved an accuracy of 88.42% on the test with a precision value of 1.0. This paper focuses mainly on the development of the ML model and not on any IoT-based ECG monitoring system. The authors have not applied their model to real-time data of patients. In our proposed architecture, not only multiple vitals are acquired, but also ML techniques are used for the prediction of the health condition of real-time patients. In [6], the researchers address the issue of remote monitoring by proposing a system that is based on IoT and machine learning techniques. It implements an IoT-based sensor network, particularly a pulse sensor, interfaced with Arduino. This system also employs ML classification techniques on the data recorded for heart illness classification. The algorithms tested in this study are the Decision Tree algorithm, Random Backwoods classifier, and Support Vector Machine (SVM). The authors collected sample data of 40 patients and trained the aforementioned algorithms. It was observed that SVM shows higher accuracy in terms of the exactness of identifying a heart illness (an accuracy of 86%). The proposed hardware, as well as software system, helps patients to predict heart disease in early stages. This system does provide benefits to CVD patients, but a diagnosis based only on pulse readings cannot give the whole picture of the patient's health. Multiple sensors need to be added to improve the performance of the ML model. Thus, in our proposed system, we used multiple vital signs' sensors to monitor the health of the chronic patient. Article [7] proposes yet another smart data analytics infrastructure for e-healthcare applications, using federated learning on local Edge computing devices. The significance of introducing federated learning in the system architecture, lies in the data privacy and security of the sensitive medical data collected through wearable devices. The paper offers a comprehensive description of the proposed framework, which comprises three modules: Cloud module, Edge module, and Application module. However, the authors do not clearly mention the system's implementation. The main highlight of this study was to identify federated learning on the edge for e-health system architectures to guarantee quality (fast response time) and security (data security) of service. CAMISA is an AI-powered solution for remote monitoring of COVID-19 patients proposed in [8]. The authors have designed a Wireless Sensor Network (WSN) system in the form of a smart shirt and nebulizer which is integrated with multiple sensors to obtain physiological parameters of a patient. The system measures the pulse, SpO2 level, temperature, and breathing rate of a patient. In case any of these parameters exceed their threshold values, alerts are generated that share the live location of the patient with the hospital so that the patient can be timely rushed to intensive care. In addition to this, the authors have also implemented the model of a neural network to predict the possibility of coronavirus in a user. The Artificial Neural Network (ANN) model takes input 20 parameters like the presence of cold, fever, or cough i.e., different symptoms experienced during coronavirus infection. The health monitoring app obtains these input parameters from a user in form of a questionnaire and estimates whether the patient has COVID-19 or not. The app also visualizes the sensory data from the system using the Thingspeak IoT platform. This system is useful for real-time monitoring of COVID-19 patients remotely to assist the hospitals in controlling the spread of the virus. In [9], the authors have leveraged Internet of Medical Things (IoMT) technology to develop and test multiple in-home quality of life applications.