We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




Facial Recognition Continuously Monitors ICU Patients

By HospiMedica International staff writers
Posted on 12 Jun 2019
Print article
A new study evaluates an automated system that uses facial recognition technology to continuously monitor the safety of patients admitted to the intensive care unit (ICU).

Developed by researchers at Yokohama City University (Japan), the system uses ceiling-mounted cameras placed above the patients' beds. After collecting about 300 hours of daytime image data of patients facing the camera in body positions that showed their face and eyes clearly, 99 images were subject to a machine-learning (ML) algorithm to analyze them. Based on input from the observational data, especially from around the subject's face, the ML algorithm learned to identify potential high-risk behavior in a process that resembles the way a human brain learns new information.

In a proof of concept study that included 24 postoperative patients (average 67 years of age) who were admitted to the ICU in Yokohama City University Hospital between June and October 2018, the ML algorithm was able to identify high risk unsafe behavior--such as accidentally removing their breathing tube--with 75% accuracy. They also suggested that monitoring consciousness may improve accuracy by helping to distinguish between high-risk behavior and voluntary movement. The study was presented at the Euroanaesthesia annual congress, held during June 2019 in Vienna (Austria).

“Using images we had taken of a patient's face and eyes we were able to train computer systems to recognize high-risk arm movement,” said lead author and study presenter Akane Sato, MD. “We were surprised about the high degree of accuracy that we achieved, which shows that this new technology has the potential to be a useful tool for improving patient safety, and is the first step for a smart ICU which is planned in our hospital.”

Facial recognition systems use biometrics to map facial features from a photograph or a video. The geometry of the face is then analyzed, with key factors including interpapillary distance and the distance from forehead to chin. In all, there are over 65 quantifiable features that can be used to identify a face, generating a unique facial signature.

Related Links:
Yokohama City University

New
Gold Member
X-Ray QA Meter
T3 AD Pro
Gold Member
12-Channel ECG
CM1200B
New
Anterior Cervical Plate System
XTEND
New
X-ray Diagnostic System
FDX Visionary-A

Print article

Channels

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.