The device is about the size of a Wi-Fi router, found in every home, and passively collects data using radio signals that reflect off the patient’s body without him needs to wear a gadget.
One example showed that this type of device could be used to detect Parkinson’s disease from a person’s breathing patterns while they sleep.
The researchers undertook a year-long home study with 50 participants using these devices. They found that by incorporating machine learning algorithms to analyze the data they passively collected, a clinician could track Parkinson’s disease progression and medication response more effectively than they would with assessments. clinical periodicals.
The scientists did this by bringing together more than 200,000 individual measurements which they averaged to smooth out variability due to conditions unrelated to the disease.
“By being able to have a device in the home that can monitor a patient and notify the doctor remotely of the patient’s disease progression and drug response so that they can attend to the patient even if the patient cannot come to the clinic – now they have real, reliable information – which goes a long way towards improving equity and access,” lead author Dina Katabi, Professor Thuan and Nicole Pham in the Department of Electrical Engineering and of Computer Science (EECS) .