A study supported by the NIHR ARC North East and North Cumbria has explored the ethical use of video technology to support people at risk of falls.
PhD student Jason Moore has led the work as part of his studentship, which is co-funded by the NIHR ARC North East and North Cumbria and Northumbria University.
His research has looked at how AI can be used to maintain the privacy of patients whilst they’re wearing specialised video glasses that allow medical staff to monitor how they move around their homes and community.
Patients are asked to wear the video glasses alongside technology worn on the lower back to track walking movements, in order to gather accurate information on how they move depending on their surroundings. This helps medical staff to understand what might be putting them at increased risk of falls.
However, patients wearing these video-enabled glasses, and their families, must be able to maintain their privacy.
To explore how these privacy concerns could be overcome, a group of computing and digital health experts including PhD student Jason Moore ran a pilot to test the application of new AI software that can blur personal data and information captured by video glasses – for example photographs around the home, footage of children or confidential paperwork.
They found that the AI software could successfully analyse the raw video footage and detect and blur details such as faces, letters and laptop or phone screens, ensuring patient privacy could be maintained.
The study demonstrates that by combining information from the wearable device that records gait data with footage captured by video glasses which is obscured where necessary thanks to the ethical use of AI, clinicians have a much more comprehensive picture of how patients’ move in their own surroundings.
This could lead to significant improvements in the accuracy of patient fall risk assessment and in the decision-making process around patient care.
Their research has now been published in Nature Digital Medicine.
Jason said: “Traditionally the use of video within the home has caused some apprehension among patient populations as a result of privacy concerns owing to what else may be captured on camera.
“However, through the use of AI software that can identify and obscure personal or sensitive information we can effectively capture the contextual information that will allow us to better understand abnormal gait data, whilst overcoming the concerns patients may have around the use of video technology in their own homes.
“The benefit of providing this contextual information is that clinicians will have a fuller picture for each individual patient which could ultimately allow them to provide more informed care plans and potentially keep more patients in their own homes for longer.”
Dr Alan Godfrey, Associate Professor in Computer and Information Sciences at Northumbria University, is supporting Jason Moore during his PhD studentship.
He said: “The suggested application of this technology is unique. The contextual information it could provide when it comes to identifying potential reasoning for abnormal variability or asymmetry in gait is invaluable.
“Without this context, patients with numerous clinical conditions such as Parkinson’s could find themselves being classed as high risk for falls and ultimately face difficult decisions around moving out of their own environment earlier than necessary. It also alleviates the pressures on patients to travel to bespoke clinics for monitoring replacing this with a community-based approach. It could go a long way to help us truly understand the fall risk of a patient and to provide personalised care solutions that are most appropriate to the individual and their needs.”
Read the full article in Nature Digital Medicine: Contextualizing remote fall risk: Video data capture and implementing ethical AI | npj Digital Medicine (nature.com)
Find out more about this PhD study: Understanding fall risk in real-world settings for people with Parkinson’s disease (U-Fall PD): Deep Learning Approach to Video Based Environmental Classification
This news item has been adapted from a story originally published by Northumbria University.
Photo credit: Northumbria University Newsroom.