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In-home wireless device tracks disease progression in Parkinson’s patients

ItemDate=2022-09-23 16:31:29 Status=publish

TopicTaglist=['H9', 'V7']

#Discussion(General) [ via IoTGroup ]

“By being able to have a device in the home that can monitor a patient and tell the doctor remotely about the progression of the disease and the patient’s medication response so they can attend to the patient even if the patient can’t come to the clinic — now they have real reliable information — that actually goes a long way toward improving equity and access ” says senior author Dina Katabi the Thuan and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) and a principle investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Jameel Clinic.The researchers used these devices to conduct a one-year at-home study with 50 participants.

They showed that by using machine-learning algorithms to analyze the troves of data they passively gathered (more than 200 000 gait speed measurements) a clinician could track Parkinson’s progression and medication response more effectively than they would with periodic in-clinic evaluations.The device which is about the size of a Wi-Fi router gathers data passively using radio signals that reflect off the patient’s body as they move around their home.In an effort to address these problems researchers from MIT and elsewhere demonstrated an in-home device that can monitor a patient’s movement and gait speed which can be used to evaluate Parkinson’s severity the progression of the disease and the patient’s response to medication.

The device incorporates a machine-learning classifier that can pick out the precise radio signals reflected off the patient even when there are other people moving around the room.They gathered 50 participants 34 of whom had Parkinson’s and conducted a one-year study of in-home gait measurements Through the study the researchers collected more than 200 000 individual measurements that they averaged to smooth out variability due to the conditions irrelevant to the disease.They used statistical methods to analyze the data and

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