No abstract

The rapid introduction of Internet of Things (IoT) Technology has boosted the service deliverance aspects of health sector in terms of m-health, and remote patient monitoring. IoT Technology is not only capable of sensing the acute details of sensitive events from wider perspectives, but it also provides a means to deliver services in time sensitive and efficient manner. Henceforth, IoT Technology has been efficiently adopted in different fields of the healthcare domain. In this paper, a framework for IoT based patient monitoring in Intensive Care Unit (ICU) is presented to enhance the deliverance of curative services. Though ICUs remained a center of attraction for high quality care among researchers, still number of studies have depicted the vulnerability to a patient's life during ICU stay. The work presented in this study addresses such concerns in terms of efficient monitoring of various events (and anomalies) with temporal associations, followed by time sensitive alert generation procedure. In order to validate the system, it was deployed in 3 ICU room facilities for 30 days in which nearly 81 patients were monitored during their ICU stay. The results obtained after implementation depicts that IoT equipped ICUs are more efficient in monitoring sensitive events as compared to manual monitoring and traditional Tele-ICU monitoring. Moreover, the adopted methodology for alert generation with information presentation further enhances the utility of the system.

No abstract

No abstract

No abstract

No abstract

No abstract

The healthcare industry is the premier domain that has been significantly influenced by incorporation of Internet of Things (IoT) technology resulting in smart healthcare application. Inspired by the enormous potential of IoT technology, this research provides a framework for an IoT-based smart toilet system, which enables home-based determination of Urinary Infection (UI) efficaciously. The overall system comprises a four-layered architecture for monitoring and predicting infection in urine. The layers include the Urine Acquisition, Urine Analyzation, Temporal Extraction, and Temporal Prediction layers, which enable an individual to monitor his or her health on daily basis and predict UI so that precautionary measures can be taken at early stages. Moreover, probabilistic quantification of urine infection in the form of Degree of Infectiousness (DoI) and Infection Index Value (IIV) were performed for infection prediction based on a temporal Artificial Neural Network. In addition, the presence of UI is displayed to the user based on a Self-Organized Mapping technique. For validation purposes, numerous experimental simulations were performed on four individuals for 60 days. Results were compared with different state-of-the-art techniques for measuring the overall efficiency of the proposed system.