An End-to-End Medical Emergency Response System to Support Elderly People

This paper proposes the concept and preliminary design of an end-to-end medical emergency response system (EEMERS) to support and help elderly people in the community who live alone. The system integrates the informal caregivers, like the neighbors, friends, and family, with the traditional formal caregivers, such as the paramedics, ambulance and medical professionals. The informal caregivers act as the first responders to attend a patient in case of a medical emergency situation before the arrival of an ambulance or other medical services. An overview of the different modules of the EEMERS, the technological details and the end-to-end process flow of the system are discussed in this work. Moreover, the selection of the most appropriate informal caregiver to attend a medical emergency situation depends on a list of pre-defined contexts and is an important part of EEMERS. This work also discusses the preliminary validation results of the informal caregiver selection based on three machine learning algorithms, namely, Logistic Regression, Support Vector Machine, Nave Bayes. Finally, the paper provides a brief overview of the basic proof-of-concept implementation of the system.