Speech Recognition Driven Assistive Framework for Remote Patient Monitoring

Abstract

Health care resources have started to become scarce due to their increase in demand. Hospitals have begun to run out of space, forcing them to deny admission of patients. Remote Patient Monitoring (RPM) has the potential to help citizens who suffer from chronic diseases and provide environments were easy to access healthcare is available. RPM allows people to receive the same amount of care without having to difficulties to find a spot at a hospital ward. However, some roadblocks end up preventing RPM from being implemented by more healthcare providers. Data integrity, user privacy, and high power consumption are some of these concerns. With data transmission and transaction, privacy and confidentiality have always been an issue. High power consumption is a concern due to RPM’s demand for continuous data collection. This paper proposes a framework that reinforces the RPM system to address these concerns. The design not only allows better data filtering for privacy but also a more responsive system with the use of controlled surveillance and speech recognition. Overall, this framework provides an opportunity for RPMs to be a viable implementation for healthcare providers.