AI Case Study
MIT AI Lab predicts Alzheimer's disease before close family members with advanced motion detection and analysis with machine learning
MIT’s Computer Science and Artificial Intelligence Laboratory used unobtrusive motion detection algorithms designed to detect falls to patients they suspect have early onset of Alzheimer's. They use low-power wireless to record sleep, breathing, gait and other behavioural patterns. Supervised machine learning algorithms analyse the motion to determine 'normal' patterns versus Alzheimer's related such as repeating behaviour or agitation, depression and sleep disturbances.
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"Researchers are monitoring...movements and comparing them with patterns seen in patients who doctors suspect have the disease."
They "intention was to monitor people without needing them to put on a wearable tracking device every day. 'This is completely passive. A patient doesn’t need to put sensors on their body or do anything specific, and it’s far less intrusive than a video camera,'" said Dina Katabi the lead on the project.
"The device’s wireless radio signal, only a thousandth as powerful as wi-fi, reflects off everything in a 30-foot radius, including human bodies. Every movement—even the slightest ones, like breathing—causes a change in the reflected signal."
The "team developed machine-learning algorithms that analyze all these minute reflections. They trained the system to recognize simple motions like walking and falling, and more complex movements like those associated with sleep disturbances. 'As you teach it more and more, the machine learns, and the next time it sees a pattern, even if it’s too complex for a human to abstract that pattern, the machine recognizes that pattern,' Katabi says."
"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. It could also pick up whether a person is repeating certain behaviors during the day. These are all classic symptoms of Alzheimer’s.
In a patient with an Alzheimer’s diagnosis the team "were able to tell that a patient was waking up at 2 a.m. and wandering around her room. They also noticed that she would pace more after certain family members visited. After confirming that behavior with a nurse, Vahia adjusted the patient's dose of a drug used to prevent agitation."
The team "initially developed the device as a fall detector for older people. But they soon realized it had far more uses. If it could pick up on a fall, they thought, it must also be able to recognize other movements, like pacing and wandering, which can be signs of Alzheimer’s." They used deep neural network based machine learning to analyse motion.
"It knows when [a patient] 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."
"Spotting the first indications of Alzheimer’s years before any obvious symptoms come on could help pinpoint people most likely to benefit from experimental drugs and allow family members to plan for eventual care. Devices equipped with such algorithms could be installed in people’s homes or in long-term care facilities to monitor those at risk. For patients who already have a diagnosis, such technology could help doctors make adjustments in their care.
Drug companies, too, are interested in using machine-learning algorithms, in their case to search through medical records for the patients most likely to benefit from experimental drugs. Once people are in a study, AI might be able to tell investigators whether the drug is addressing their symptoms.
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."
The models are built using data to compare patterns of movements of those they suspect have the the disease.