Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence.

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easily or explicitly detectable. Such alterations have been recently used to spread false news or disinformation.

Illumination Adaptation in a Multi-Wavelength Opto-Electronic Patch Sensor.

In capturing high-quality photoplethysmographic signals, it is crucial to ensure that appropriate illumination intensities are used. The purpose of the study was to deliver controlled illumination intensities for a multi-wavelength opto-electronic patch sensor that has four separate arrays each consisting of four light-emitting diodes (LEDs), the wavelength of the light generated by each array being different. The study achieved the following: (1) a linear constant current source LED driver incorporating series negative feedback using an integrated operational amplifier circuit; (2) the fitting of a linear regression equation to provide rapid determination of the LEDs driver voltage; and (3) an algorithm for the automatic adjustment of the output voltage to ensure suitable LED illumination.

Oxygen Saturation Measurements from Green and Orange Illuminations of Multi-Wavelength Optoelectronic Patch Sensors.

Photoplethysmography (PPG) based pulse oximetry devices normally use red and infrared illuminations to obtain oxygen saturation (SpO₂) readings. In addition, the presence of motion artefacts severely restricts the utility of pulse oximetry physiological measurements. In the current study, a combination of green and orange illuminations from a multi-wavelength optoelectronic patch sensor (mOEPS) was investigated in order to improve robustness to subjects' movements in the extraction of SpO₂ measurement.

Aeromedical Transfer of Patients with Viral Hemorrhagic Fever.

For >40 years, the British Royal Air Force has maintained an aeromedical evacuation facility, the Deployable Air Isolator Team (DAIT), to transport patients with possible or confirmed highly infectious diseases to the United Kingdom. Since 2012, the DAIT, a joint Department of Health and Ministry of Defence asset, has successfully transferred 1 case-patient with Crimean-Congo hemorrhagic fever, 5 case-patients with Ebola virus disease, and 5 case-patients with high-risk Ebola virus exposure. Currently, no UK-published guidelines exist on how to transfer such patients.

Development of m-health monitoring systems in India and Iraq.

Two separate projects have been carried out to implement m-health programs in India and Iraq, and, for each, this paper describes the work performed by the teams involved, presents results and details a number of lessons learned. In general, it is found that although India and Iraq have very different medical priorities, they pose similar issues when introducing m-health strategies.

Monitoring heart disease and diabetes with mobile internet communications.

A telemedicine system is described for monitoring vital signs and general health indicators of patients with cardiac and diabetic conditions. Telemetry from wireless sensors and readings from other instruments are combined into a comprehensive set of measured patient parameters. Using a combination of mobile device applications and web browser, the data can be stored, accessed, and displayed using mobile internet communications to the central server.

Data acquisition in a wireless diabetic and cardiac monitoring system.

A telemedicine system is described for monitoring the vital signs and general health indicators of patients with cardiac and diabetic conditions. Telemetry from wireless sensors and readings from other instruments are combined into a comprehensive patient health dataset. The data can be stored, accessed and displayed using mobile Internet communications with a server.

Mobile communications for monitoring heart disease and diabetes.

This paper describes a practical development project to enable the monitoring of vital signs data obtained from patients located in remote rural locations. The data are gathered from a wireless network of sensors attached to a patient's body and stored locally for secure transmission over existing communication infrastructures to a hospital server. Clinicians are then able to monitor the patient offline and upload diagnoses.