Real-time Aircrew Health Monitoring with QCM

060803-F-2907C-107 U.S. Air Force Master Sgt. Lance Cheung photographs himself and a three-ship formation of F-15E Strike Eagle aircraft from Royal Air Force Lakenheath, England, on Aug. 3, 2006. The Strike Eagles are attached to the 492nd Fighter Squadron and are practicing basic surface attack techniques. Cheung is a photojournalist for the Air Force News Agency in San Antonio, Texas. DoD photo by Master Sgt. Lance Cheung, U.S. Air Force. (Released)

Fighterpilots and aircrew must be in optimal health and fitness to handle the physical demands of modern aerial operations and warfare. In modern fighter jets flying at high altitude, the flight crew is subjected to extreme G forces and have reduced Oxygen. It is therefore essential to measure and monitor the critical and vital parameters of the aircrew in real time and alert in case of acute and abnormal deviations.

Remote real-time and inflight health monitoring of aircrew for the assessment of health and physiological parameters during extended fighter flying and aeromedical research is based on measuring the following (but not limited to) vital parameters non-invasively and provide real-time alerts for acute and critical deviations.

Heart Rate
Pulse
Blood Pressure
Rate of Respiration
Body Temperature
Arterial Oxygen Saturation
Electro-Cardiogram
Electro-Myogram
Electrodermal Activity
Galvanic Skin Response

Ontonix develops iCOMS – a Complexity-based Monitoring System for medical application – a system which utilizes real-time measure of the complexity of patients and delivers early warnings of instabilities or crises. The system has been developed in collaboration with US-based SAIC (www.saic.com) and with the Military Medicine Institute in Warsaw, Poland (https://wim.mil.pl/index.php).

The iCOMS system is currently available on Android smartphones and tablets and may be installed on any hardware or integrated within a given IT infrastructure. The system can display the instantaneous complexity of an individual as well as real-time ranking of vital signs, indicating those that are causing instability at a given time. In its present implementation the system draws real-time data from a cloud repository connected to an Intensive Care Unit (ICU) in a hospital.

What differentiates this application from other commercial solutions is that it doesn’t process channels on a one-by-one basis, checking if particular thresholds or critical levels are crossed. The iCOMS considers all the possible interactions between channels, not just the values of each channel, delivering a single systemic indicator of the patient’s state of health and its dynamics. In validation tests with the MMI, the system was shown to anticipate fainting of patients in a sudden posture change. This feature makes it particularly applicable to pilots subjected to high-g manoeuvres and reduced oxygen supply.

Critical situations in medicine do not necessarily require certain vital signs to reach or exceed particular thresholds. Because of the immense complexity of the human body, medicine lacks a systems perspective. Today, thanks to our Quantitative Complexity Management Technology, it is possible to view a patient as a system of systems which interact and to provide real-time measures of his complexity and stability. This approach provides unprecedented insight into the dynamics of a patient and enables us to issue early-warnings of imminent crises or instabilities. These measures of stability and resilience are obtained in real-time not via a data-base look-up but by analysing real-time streaming multi-channel data. Our system processes all channels as well as all channel interactions. This is illustrated in the diagram below.

The above Complexity Map is obtained in real time and reflects the structure of patient’s instantaneous vital signs and the corresponding interactions. It is these interactions between the various data channels that furnish new insight and enable a truly predictive capability. In fact, the goal is not merely to report a critical situation but to anticipate it. Anomaly detection and crisis anticipation are the key characteristics of QCM technology.

The Android implementation, the application looks as illustrated below.

When complexity exceeds a given threshold, an alarm is issued (right). Such complexity peaks tend to precede states of crisis or anomalies. The bar chart indicates impact of each physiological parameter on the patient’s current state.

Ontonix has been active in the medical field, particularly in cardiology, collaborating with hospitals, research centres (USAISR, US Army Institute of Surgical Research, https://www.usaisr.amedd.army.mil/) and medical equipment manufacturers, such as Boston Scientific and Biotronik. Various publications in medical journals have been produced (see references: https://www.ontomed.net/publications).

Work on predicting fainting in the presence of sudden posture change has been performed with the Military Medicine Institute in Warsaw, Poland (https://wim.mil.pl/index.php) and is relevant to a high-g manoeuvres and lack of oxygen scenario, in which fainting or loss of consciousness can be induced.

In alternative to an Android phone, the QCM algorithm can run on a modified ReliaGATE 10-14 Edge Gateway by Eurotech. Based on the NXP i.MX 8M Mini Cortex-A53 quad core processor, with up to 4GB of RAM, up to 32GB of eMMC and a user-accessible microSD slot, the ReliaGATE 10-14 is a low power gateway suitable for demanding use cases. The ReliaGATE 10-14 comes with Everyware Software Framework (ESF), a commercial, enterprise-ready edition of Eclipse Kura, the open source Java/OSGi middleware for IoT Edge Gateways.

The ReliaGATE 10-14 can process data from multiple crew members, each one waring a data hub which, in turn, will transmit data to it.

Alerts are transmitted to on-board computer or stand-alone display. All data is stored for subsequent download and analysis. The scheme of the solution is illustrated below.