AI Algorithm Analyzes Daily Activities in People with Dementia

Data streamed from these devices is analyzed using machine-learning solutions, and identified health problems are flagged on a digital dashboard and followed up by a clinical monitoring team.

For the study, the researchers used non-negative matrix factorization (NMF) to find hidden clues of possible UTI cases; they then applied novel machine learning algorithms to identify early UTI symptoms and signs. In addition, they designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline, using an isolation forest (iForest) technique to create a holistic view of the daily activity patterns. The study was published on January 19, 2018, in PLOS One.

“Urinary tract infections are one of the most common reasons why people living with dementia go into hospital. We have developed a tool that is able to identify the risk of UTIs so it is then possible to treat them early,” said professor of machine intelligence Payam Barnaghi, PhD, of the University of Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) unit. “We are confident our algorithm will be a valuable tool for healthcare professionals, allowing them to produce more effective and personalized plans for patients.”

“The Technology Integrated Health Management (TIHM) for dementia study is a collaborative project that has brought together the NHS, academia, and industry to transform support for people with dementia living at home and their carers. Our aim has been to create an Internet of Things led system that uses machine learning to alert our clinicians to potential health problems that we can step in and treat early,” said Professor Helen Rostill, director of innovation and development at SABP. “The system helps to improve the lives of people with dementia and their carers and could also reduce pressure on the NHS.”

Related Links:
University of Surrey
Surrey and Borders Partnership NHS Foundation Trust