Smart Real-Time Health Monitoring Band Using Machine Learning and IoT

Nowadays significant progress has been made in a health monitoring system. Health monitoring watches reduce people’s effort by monitoring day-to-day activity related to health. The devices which are embedded with different technologies like IOT and ML will give the endure solution for the reliable, objective and remote monitoring and support through ambient assisted living. In this research work, ML computations are used to screen the clinical issue of individuals.In Beginning getting ML computations is performed using the dataset. Testing takes out is passed on by gathering beat, circulatory strain, and temperature of the individual using IoT.Health trackers and body sensor devices are making exceptional impact on the idea of living and clinical benefits system. The central task of smart watches is to keep monitoring patient standard activities by giving central information concerning the activity over the entire day. This information can be obtained by using a smart band or a mobile phone application. The central objective of this watch is to give the significant information as for the electronic sensors used in these smart band and moreover their working to choose step count, beat, nature of rest, calories burned-through, etc."Internet-of-things" and "Machine Learning" are playing a major role in everyone’s life and even in health care applications also. Especially in smart wearable devices such as smartwatches like bit fit, Rolex, and others. By using these smart devices one can easily monitor health and chance of risk. These devices are embedded with different sensors based on the application. In our application, we are using SPO2, heart rate, and body temperature attributes. These sensors’ data are captured and sent to the cloud and analyzed based on machine learning techniques. The testing stage checks for any abnormalities in the clinical issue from the sensor data accumulated through the IoT framework. Genuine assessment is performed from data amassed into the cloud from IoT devices to assess the exactness in assumption rate.