Embedded Based Smart ICU-For Intelligent Patient Monitoring

Smart ICUs are networks of audio-visual communication and computer systems that link critical care doctors and nurses (intensivists) to intensive care units (ICUs) in other, remote hospitals. The intensivists in the “command center” can communicate by voice with the remote ICU personnel and can receive video communication and clinical data about the patients. Direct patient care is provided by the doctors and nurses in the remote ICU who do not have to be intensivists themselves. In recent years there has been an increase in the number of patients needing ICU care without a corresponding increase in the supply of intensivists. Smart ICUs can be a valuable resource for hospitals faced with the need to expand capacity and improve care for a growing elderly population. Evidence from some early-adopter hospitals indicates that it can leverage management of patient care by intensivists, reduce mortality rates, and reduce LOS. However, positive outcomes appear to depend on the organizational environment into which the Smart ICU is introduced. The dramatic improvements in mortality and LOS reported by some early-adopter hospitals have not been matched in most. The limited research available suggests that the best outcomes may occur in ICUs that: Can make organizational arrangements to support the management of patient care by intensivists using Smart ICU; Have little or no intensivist staff available to them in the absence of Smart ICU; Have relatively high severity-adjusted mortality and LOS rates; Are located in remote or rural areas where safe and efficient transfer of patients to regional centers for advanced critical care presents difficulties. Smart ICU connects a central command center staffed by intensivists with patients in distant ICUs. Continuous, real-time audio, video, and electronic reports of vital signs connect the command center to the patients’ bedsides. Computer-managed decision support systems track each patient’s status and give alerts when negative trends are detected and when changes in treatment patterns are scheduled. The patient data include physiological status (e.g., ECG and blood oxygenation), treatment (e.g., the infusion rate for a specific medicine or the settings on a respirator), and medical records.

A Method For Iris Liveness Detection Using Passive Light Trigger

The present disclosure relates to a passive liveness detection method for a camera-based iris biometric system. The method comprising: detecting an initiation of transaction on one of a user device and a payment terminal in supervised/unsupervised payment environments, introducing a passive light trigger to record real-time involuntary biological response of user’s eye when the initiation of transaction is detected, comparing the recorded real-time involuntary biological response of the user’s eye to similar biological response of the user’s eye recorded at the time of enrollment and/or a previous successful transaction by the user stored in a database, and determining if the artifact being presented to the iris biometric system at the time of transaction is real or spoof based on the comparison. The present disclosure uses shutter speed and aperture of camera lens, and image sensor sensitivity settings coupled with a passive light trigger for liveness detection. This approach overcomes the need for specialized expensive hardware and the limitation of detecting a fixed set of fake artifacts (spoofs) for passive liveness detection