For people living with Parkinson’s disease, there has been a gap between laboratory research and real-world behavior that has limited efforts to improve gait symptoms outside of the clinic. Now, researchers from UC San Francisco have taken an important step toward closing that gap by successfully moving the laboratory into the living room. In a new study published February 13 in Science Advances, the team demonstrated that brain activity recorded from fully implanted devices while patients are at home can be used to reliably determine whether a person is walking or not. By analyzing synchronized neural and movement data collected during more than 80 hours of unsupervised daily activity, researchers identified individualized patterns of brain activity associated with walking. These neural signatures allowed an implanted deep brain stimulation (DBS) device to classify movement states using signals generated during natural, at-home activities.

When generative AI systems produce false information, this is often framed as AI “hallucinating at us”—generating errors that we might mistakenly accept as true.