The device is about the size of a Wi-Fi router, which can be found in every home, and collects data passively using radio signals that reflect off the patient’s body without the need for him or her to wear a gadget.
One example showed that this type of device could be used to detect Parkinson’s from a person’s breathing patterns while sleeping.
The researchers undertook a one-year long at-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 progression and medication response more effectively than they would with periodic, in-clinic evaluations.
The scientists did this by gathering more than 200,000 individual measurements that they averaged to smooth out variability due to the conditions irrelevant to the disease.
“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,” said in the statement senior author Dina Katabi, the Thuan and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS).