Using Remote Radio Wave Technology to Improve Parkinson Disease Patient Outcomes

In this Q&A, Yingcheng Liu, PhD student, MIT Computer Science & Artificial Intelligence Lab (CSAIL), advisee of Dina Katabi, PhD, professor, MIT CSAIL, talks to Neurology Learning Network about an exciting new technology: a home device that uses radio waves to detect and analyze the movements of individuals living with Parkinson disease.

Not only does the device eliminate the time-consuming upkeep associated with traditional “wearables,” but the data collected can be used by doctors to evaluate patient well-being and adjust medication dosages. The study, entitled “Monitoring gait at home with radio waves in Parkinson’s disease: a marker of severity, progression, and medication response,” was published in Science Translational Medicine in September 2022.

Editors Note: This interview has been lightly edited for clarity.

Brionna Mendoza, Neurology Learning Network: What led you and your colleagues to look into developing a home device that can detect and analyze the movements of individuals living with Parkinson disease?

Yingcheng Liu, PhD Student, MIT CSAIL: The conceptualization of this idea is really thanks to my advisor, and the wireless sensor technology our lab has been developing for the last 10 years—a contactless device that can localize and track human motion, analyze human activities, and even capture breathing and heart signals. With this invention, we started to ask what the real-world applications of this device are. Can we solve important problems using this new technology?

One very good area of application is Parkinson disease (PD) since it is a movement disorder and manifests its symptom in the movements (such as gait and motion) of a person. Thus, we started our research to apply this technology to PD. 

Mendoza: Could you explain how the device works? 

Liu: You can imagine this device as a in-home human radar that emits signals and then receives signals reflected back from human body. The reflection conveys not only a person's relative location but also their interaction with signal. 

Mendoza: Why did you choose radio waves as the primary mechanism of action?

Liu: We believe the wireless signal can provide non-contact interaction with the participant. Therefore, it does not require the users to wear any sensors. Wearable sensors require people to remember to charge devices and attach them to their body once charged. This could cause low adherence rates, especially among the elderly and PD populations who may be less tech-savvy. Another option is to use camera sensors, but cameras collect too much privacy data and does not work in poor lighting (such as at night). So, we think that wireless signal in the form of radio waves is the best way to collect data. 

Mendoza: How might clinicians be able to use technology in the future to improve clinical outcomes for patients living with Parkinson disease?

Liu: I think this technology can mean a lot for telehealth not only for PD but also for other diseases manifesting symptoms in movements. As we have shown in our paper, our device can tell us about the medication responses of those living with PD.

In the future, PD individuals might no longer need to keep a medication diary (doctors need 2 weeks of a medication state diary with entries every hour to determine dosages for treatment of PD. This is a labor-intensive approach to adjusting medication and it takes too long. In the future, doctors can simply look at the outcomes measured from our device and objectively determine whether the current dosage is effective. 

Mendoza: What future areas of inquiry do your findings point towards?

Liu: There are many diseases that manifest symptoms in movements. We want to apply this technology similarly to those diseases and determine whether it can improve clinical outcomes.

Yingcheng Liu, PhD student, MIT CSAIL, graduated with honors from Peking University with a Bachelor's degree in Computer Science. As a PhD student at MIT's Computer Science and Artificial Intelligence Laboratory working under Professor Dina Katabi, Liu researches machine perception, computer vision, and healthcare. He has had papers published in Science Translational Medicine, Nature Medicine, and Frontiers in Psychiatry.