Intelligent surveillance to improve senior and patient care

Author: Riccardo Biella, software engineer at Paradox Engineering


Health systems face immense challenges in many countries around the world. Lots deal with an ageing population and need to control public spending for healthcare service, but in many cases patients outnumber available resources including basics such as hospital beds, doctors, and nurses. This shortage became far more relevant during Covid-19 emergency, with most countries struggling to provide adequate pandemic response and quality of care.

Smart technologies are enabling a whole new class of services to improve patient assistance, specifically the treatment of elderly and long-term patients, whether in hospital, in assisted living facilities, or at home. Connected healthcare systems and remote patient monitoring (RPM) technologies are being increasingly used to ensure constant surveillance, support professional caregivers in their daily duties, and relief family members.

Together with Minebea Intec and MinebeaMitsumi Sensing Division, at Paradox Engineering we are working on an innovative solution for the managed care industry: our goal is to finalize a secure and open technology platform for non-invasive patient monitoring in hospitals, clinics, nursing, care houses and home environments.

It is meant as an adaptive and knowledge building solution, able to generate patient-specific intelligence by collecting data from multiple sensors, correlating multiple parameters, and integrating a unique Artificial Intelligence (AI) engine for situation recognition, behavior analysis, long-term health evolution monitoring and personal relationships.

The solution provides local AI to prevent possible data leakages from the hospital environment and better protect patient privacy. We are currently piloting Machine Learning to improve some specific features, such as the detection of adverse events: by processing data coming from connected smart cameras and sensors, the system can recognize when something goes wrong and immediately alert caregivers in case of falls, lack of movements or other types of anomalies, ensuring constant monitoring of patients around the clock.

Another feature we are testing relates to room access control. We are using Machine Learning to elaborate images from connected smart cameras in order to recognize the number of people in a room and distinguish patients from hospital staff. This is a highly valuable feature to increase guest safety by detecting unauthorized access.

We started from an initial dataset made of hundreds of images of patients and doctors to train the system. PE Smart Cameras are now able to perform object detection tasks in real time, distinguishing people within the room and classifying them as patients or hospital staff, thanks to the use of a Deep Neural Network that can also recognize things such as scrubs, uniforms, nightgowns, medical instruments and postures.

Watch this short video to have a sneak peek:



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