NYC Gazette

A team of researchers, including Dr. Yucheng Xie from the Katz School's Graduate Department of Computer Science and Engineering, has developed a monitoring system that uses commercial millimeter-wave technology to track concentration-related movements without physical contact or cameras. This innovative system can detect eye blinking, yawning, and leg shaking with high accuracy.

The details of this system are outlined in the paper titled “Towards Contactless Human Concentration Monitoring Using mmWave Signals,” which has been accepted by the IEEE International Conference on Collaboration and Internet Computing (CIC). Dr. Xie collaborated with colleagues from George Mason University, Rutgers University, Temple University, and New York Institute of Technology.

“With concentration becoming increasingly difficult to sustain in today’s fast-paced, distraction-filled environments,” said Dr. Xie, “accurately monitoring people's focus is critical for boosting productivity, improving educational outcomes and even supporting cognitive health.”

Millimeter-wave (mmWave) technology operates using electromagnetic waves between 1 millimeter and 10 millimeters in wavelength. This part of the radio spectrum can carry large amounts of data at high speeds and is crucial for modern communication systems like 5G networks.

Traditional methods for assessing concentration rely on self-reporting or direct observation, which can be subjective or intrusive. The new mmWave-based system offers a non-invasive alternative by capturing subtle physical indicators from a distance through radio waves. It provides significant improvements over camera-based and wearable technologies that may compromise privacy or require constant interaction.

Dr. Xie's system monitors concentration-linked movements with 95.3% accuracy using a single commercial mmWave device. To address the limited field of view inherent in these devices, they developed a spatial decomposition approach utilizing beamforming techniques to enhance signals reflected from specific body parts involved in different activities.

By decomposing mmWave signals into frequency components, multiple activities can be monitored simultaneously while reducing interference. A convolutional neural network integrated with domain adaptation techniques ensures accurate functionality across various environments.

“This technology opens up new possibilities for unobtrusive concentration monitoring in places where people need to stay alert and focused,” said Dr. Xie. “With its contactless and privacy-preserving design, our system is versatile enough for real-world scenarios.”

The researchers tested their system across multiple office settings at varying distances. At two feet from participants, optimal detection was achieved; accuracy remained consistent even at five feet away. Tests conducted in different environments demonstrated the robustness of the system with minor adaptations required for varied room layouts.

As attention-related disorders like ADHD become more prevalent, this technology could help capture early signs of concentration lapses aiding timely interventions. In classrooms specifically it might assist educators tracking student engagement adjusting lesson pacing accordingly while psychologists evaluate fluctuations identifying triggers behind attention decline

“This promising approach marks milestone future focus-tracking technology wide-ranging implications enhancing both individual productivity cognitive wellness” added Dr.Xie