Building an Alzheimer’s early warning system through artificial intelligence

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[A flat white box attached to the wall] knows when [David Graham] gets out of bed, gets dressed, walks to his window, or goes to the bathroom. It can tell if he’s sleeping or has fallen. It does this by using low-power wireless signals to map his gait speed, sleep patterns, location, and even breathing pattern. All that information gets uploaded to the cloud, where machine-learning algorithms find patterns in the thousands of movements he makes every day.

The rectangular boxes are part of an experiment to help researchers track and understand the symptoms of Alzheimer’s.

Currently, there’s no easy way to diagnose Alzheimer’s. No single test exists, and brain scans alone can’t determine whether someone has the disease. Instead, physicians have to look at a variety of factors, including a patient’s medical history and observations reported by family members or health-care workers. So machine learning could pick up on patterns that otherwise would easily be missed.

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Over time, the device creates large readouts of data that show patterns of behavior. The AI is designed to pick out deviations from those patterns that might signify things like agitation, depression, and sleep disturbances.

“If you can catch these deviations early, you will be able to anticipate them and help manage them,” [geriatric physiatrist Ipsit] Vahia says.

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Read full, original post: AI can spot signs of Alzheimer’s before your family does

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