By Dave DeFusco
A monitoring system developed by a team of researchers, including Dr. Yucheng Xie of the Katz School’s Graduate Department of Computer Science and Engineering, leverages commercial millimeter-wave technology that operates without physical contact or privacy-invasive cameras to track people's concentration-related movements, such as eye blinking, yawning and leg shaking with remarkable accuracy.
The system is detailed in the paper, “Towards Contactless Human Concentration Monitoring Using mmWave Signals,” which has been accepted by the IEEE International Conference on Collaboration and Internet Computing (CIC). The IEEE CIC is a premier multidisciplinary conference for advancing Internet technologies, collaborative networking and innovative applications. Dr. Xie collaborated on the study with colleagues at 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 refers to the use of electromagnetic waves with wavelengths between 1 millimeter and 10 millimeters, corresponding to frequencies between 30 gigahertz and 300 gigahertz. This part of the radio spectrum is characterized by its ability to carry large amounts of data at very high speeds, making it a key technology for modern communication systems like 5G networks.
Traditionally, concentration assessment has relied on self-reporting or direct observation—both labor-intensive and often subjective—or sensor-based monitoring, which can be intrusive. This new mmWave-based system presents an innovative, non-invasive alternative by capturing subtle physical indicators of focus from a distance by sending and receiving radio waves. It offers significant improvement over camera-based and wearable technologies that compromise privacy or require constant physical interaction.
Using a single commercial mmWave device, Dr. Xie’s system monitors concentration-linked movements with 95.3% accuracy. To overcome the limited field of view of commercial mmWave devices, they developed a spatial decomposition approach using a beamforming technique to enhance signals reflected from specific body parts involved in different activities.
By decomposing the mmWave signals into their frequency components, they can simultaneously monitor multiple activities while mitigating interference between them. Furthermore, a convolutional neural network integrated with domain adaptation techniques allows the technology to function accurately in various environments.
“This technology opens up new possibilities for unobtrusive concentration monitoring in places where people need to stay alert and focused, such as classrooms, workplaces and healthcare settings,” said Dr. Xie. “With its contactless and privacy-preserving design, our system is versatile enough for real-world scenarios.”
The researchers tested the system across multiple office settings and distances. At two feet, from participants, the system achieved optimal detection, with accuracy maintained even at five feet. Tests in varied environments demonstrated the robustness of the system, with minor adaptations required for different room layouts.
As the prevalence of attention-related disorders like ADHD continues to rise, this technology’s ability to capture early signs of concentration lapses could aid in timely interventions. In classrooms, for instance, it may help educators track student engagement and adjust lesson pacing. Furthermore, the technology could assist psychologists in evaluating concentration fluctuations and identifying triggers for attention decline.
“This promising approach marks a milestone for the future of focus-tracking technology, with wide-ranging implications for enhancing both individual productivity and cognitive wellness,” said Dr. Xie.
