Sleep is very important; it’s one of the most peaceful things to experience in the world but not everyone is blessed with good sleep. Millions of people suffer from sleep-related disorders. The positions we take in our sleep can have implications for our health. Researchers at Northeastern University are combining different sensing technology with machine learning techniques to monitor a sleeper’s position even under the covers in total darkness. Their work could make this monitoring easier for doctors and less invasive for patients.
“As healthy adults, we spend almost one-third of our life in bed, if not more,” says Sarah Ostadabbas, an assistant professor of electrical and computer engineering at Northeastern University. “For patients that are in hospitals, the elderly, and young kids, it can go up to 100 percent of the time. We can bring the power of computer vision and artificial intelligence to make the process of understanding human behaviour in bed easier.”
Sarah and her team built a data set of sleep positions to use to train algorithms to recognize and identify the pose of a sleeping person. To allow other researchers to use and expand on their work they recently shared both . “There were no data sets out there that we could use to teach our algorithm how to understand, predict, and diagnose some of these sleep behaviours or abnormalities,” Ostadabbas says. “So we decided to collect our own data and release it to the public.”
The team recruited more than 100 people from the Northeastern University’s community to come to their lab and lie down in various positions to help them build their data set. The researchers collected several types of data, each session took about two hours. In the end, the researchers had compiled more than 14,000 position samples and labeled each one.
“Better algorithms and better data sets will lead us to a toolbox that is easy to use, easy to tune, and can work in different applications,” Ostadabbas says. “At the end of the day, we are hoping to have a better quality of life for patients that are suffering from some of these sleep-related or bed-related disorders.”
Mayuri Talgaonkar
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