PERSONALIZED HEALTHCARE MONITORING USING BDCAM: A CONTEXT-AWARE BIG DATA FRAMEWORK FOR ACCURATE EMERGENCY DETECTION AND RESPONSE IN ASSISTED LIVING

In order to provide real-time monitoring and early detection of potential health issues or emergencies for elderly and disabled patients residing in assisted living facilities, a novel approach has been developed that makes use of wearable devices, ambient sensors, machine learning algorithms, and big data infrastructure. These innovations in healthcare monitoring allow for more individualized treatment plans and better overall health outcomes for patients.In this study, we examine the research on assisted living facilities' personalised healthcare monitoring, with a focus on the use of big data frameworks for accurate emergency detection and response. We propose a hybrid approach that uses machine learning algorithms, predictive analytics, and indepth knowledge of healthcare professionals to enhance the precision with which assisted living facilities can identify and respond to emergencies. We also cover the obstacles of adopting personalised healthcare monitoring utilising big data frameworks in assisted living facilities, as well as the limitations of our proposed technique.Overall, assisted living facility residents who are elderly or disabled may benefit greatly from personalised healthcare monitoring enabled by big data frameworks. These devices can boost patient outcomes while cutting healthcare expenses by keeping tabs on them in real time and alerting doctors to any possible problems. Healthcare for people in assisted living facilities is likely to improve as research and development in this field continues.