Design of an intelligent IoT enabled healthcare responsive framework for emergency scenarios

Timely assessment and response to critical health scenarios are important for survival of patients. Smart wearables help in non-interrupted patient tracking whereas advanced intelligent models enhance early risk detection. But nowadays, heavy road traffic is causing delays in arrival of ambulance service thereby decreasing emergency service efficiency. Existing frameworks either address patient health monitoring or traffic control in a separate manner. Thus, a model which can integrate risk analysis and adaptive traffic management for ambulance service is lacking. The aim of this research is to design an intelligence based responsive health model for patients needing emergency help by tracking vital metrics with an advanced risk predictive model. Real time traffic support is also desirable to reduce ambulance service delay. The framework consists of a smart wristband 'BioTrace-G' to collect patient's vital signs. This data is sent to the patient's smartphone where an application 'E-response' is configured. The application hosts GA-DNN (Genetic algorithm-Deep neural network) model used for feature optimization and critical risk level prediction. When the detected risk is high or mid type, emergency ambulance service is automatically triggered which is supported by a traffic unit to facilitate faster emergency service. The model upon evaluation recorded a promising outcome. The mean risk prediction accuracy with GA-DNN was 95.2% in context to sensor readings while it is 94.2% when number of patients are considered. The computed mean inference latency was only 57.8 s. Also, the GA-DNN generated the least mean false negatives and false positives of 6.9% and 13.4% respectively. The framework optimized the patients prioritization and ambulance dispatch delay as compared to conventional approach. The model with integrated traffic support showed better results when validated against metrics like response delay, number of signal stops and ambulance speed. Hence, the integrated responsive framework serves as a prototype for early risk identification and categorization with reduced response delay and enhanced patient care.