PULLMAN, WA – An algorithm developed by Washington State University researchers aims to represent and replace missing health data from wearable sensors with estimated data. The research is designed to increase the accuracy of the devices.
The scientists recently presented their work on the algorithm at the 2024 International Joint Conference on Artificial Intelligence in Jeju, South Korea. They hope the work could have practical applications, especially, for people who are having their health monitored in remote or underserved areas.
Wearable devices are increasingly popular for important health applications, such as for vital sign monitoring, rehabilitation, and movement disorders. Especially in rural areas, a wearable device can provide continuous and important health information when a doctor is far away. The wearable devices use sensors to collect information on users’ health and then make decisions using machine learning algorithms.
However, while the machine learning algorithms assume that data from all sensors are available, that is often not the case, says Ganapati Bhat, Raymond and Beverly Lorenz Distinguished Assistant Professor in WSU’s School of Electrical Engineering and Computer Science who led the work.
Because of user error, energy limitations, or a malfunctioning sensor, the technology can often have missing or incomplete data, which creates inaccuracy in diagnostics, especially in communities where power is only available intermittently.
“Missing data can lead to a significant drop in performance of the health algorithms. In the worst case, it can miss catastrophic cases like falls, which impact user health,” he said.
In their work, the WSU team developed a way to represent the missing data in wearable health applications while maintaining accuracy. The approach aims to represent missing data in an energy-efficient manner since wearable devices are typically constrained by their small batteries. In addition to Bhat, the work was led by graduate students Dina Hussein and Taha Belkhouja along with Jana Doppa, Huie-Rogers Endowed Chair Associate Professor in Computer Science.
“The key insight is that we do not need the exact representation of the missing sensor data if we can maintain high predictive accuracy for the health task,” said Bhat.
The researchers validated their approach with several wearable health applications, such as when an assistive device is used for paralyzed patients, and found that their method was highly accurate even when multiple sensors were missing. The researchers now plan to work with the WSU School of Medicine to test their work in gesture and activity recognition applications in real-world settings. The team also plans to apply their work in other application domains, such as environmental monitoring.
The work was funded by Bhat’s National Science Foundation CAREER award.