System can effectively monitor activities such as sleeping, eating and frequency of bathroom use
Hajar Abedi, PhD candidate in systems design engineering (SYDE) is harnessing artificial intelligence (AI) and wireless technology to unobtrusively monitor elderly people in their living spaces and provide early detection of emerging health problems.
The new system, developed by researchers in SYDE, electrical and computer engineering, and the School of Public Health Sciences follows an individual’s activities accurately and continuously as it gathers vital information without the need for a wearable device and alerts medical experts to the need to step in and provide help.
"We use artificial intelligence to actually make our lives easier because we can train them and they can do our job, and basically, our main purpose is to save lives using this AI technology," said Abedi.
While a senior’s physical or mental condition can change rapidly, it’s almost impossible to track their movements and discover problems 24/7 — even if they live in long-term care. In addition, other existing systems for monitoring gait — how a person walks — are expensive, difficult to operate, impractical for clinics and unsuitable for homes.
The new system represents a major step forward and works this way: first, a wireless transmitter sends low-power waveforms across an interior space, such as a long-term care room, apartment or home.
As the waveforms bounce off different objects and the people being monitored, they’re captured and processed by a receiver. That information goes into an AI engine which deciphers the processed waves for detection and monitoring applications.
The system, which employs extremely low-power radar technology, can be mounted simply on a ceiling or by a wall and doesn’t suffer the drawbacks of wearable monitoring devices, which can be uncomfortable and require frequent battery charging.
Waterloo researchers have partnered with a Canadian company, Gold Sentintel, to commercialize the technology, which has already been installed in several long-term care homes.
A paper on the work, AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing, appears in the IEEE Internet of Things Journal.
Doctoral student Hajar Abedi was the lead author, with contributions from Ahmad Ansariyan, Dr. George Shaker, Dr. Plinio Morita, Dr. Jen Boger and Dr. Alexander Wong.
This research was featured in the CBC News article, New AI safety system tracks seniors in care homes while giving them more privacy, researchers find and in the Waterloo News story, New in-home AI tool monitors the health of elderly residents.