Techniques for subtle mid-air gestural interaction using mmWave radar

Ravindran, Anith Manu (2025) Techniques for subtle mid-air gestural interaction using mmWave radar. PhD thesis, University of Glasgow.

Abstract

Users need to be able to interact with mid-air gesture systems in ways that are efficient, precise, and socially acceptable. Subtle mid-air micro gestures can provide low-effort and discreet ways of interaction. This thesis contributes techniques for recognizing and utilizing subtle mid-air gestures with millimeter wave radars, a rapidly emerging sensing technology in human-computer interaction.

The first contribution focused on the problem of addressing a system. By analyzing the frequency components of various hand motions, subtle activation gestures were identified which produced high-frequency signals through deliberate, rhythmic movements. A novel activation gesture recognition pipeline was then developed using frequency analysis to recognize these gestures and ignore incidental hand motions. Tested across three types of sensors, the pipeline demonstrated robust performance in recognizing subtle high-frequency activation gestures and producing zero false activations for broad hand motions. Further improvements were also explored to enhance robustness to reduce false activations during activities like typing, writing, and phone usage.

The second contribution focused on recognition of subtle gestures from mmWave radar data using deep learning. A new dataset was developed, capturing the temporal dynamics and motion patterns of 10 different subtle gestures from 8 users with a mmWave radar. Multiple neural network architectures were trained and evaluated using the dataset, achieving a high recognition accuracy of 90%. The results demonstrated that hybrid neural networks combining convolutional and recurrent layers can effectively recognize subtle gestures from mmWave radar signals and generalize across different users.

The final contribution progressed from offline evaluations to practical, real-time assessments. The neural network models were integrated into prototype applications that enabled real-time subtle gesture interactions for tasks such as selecting photos and adjusting media playback. A user study demonstrated significant improvements in task completion, accuracy, and user experience compared to traditional macro gestures. The findings suggest that subtle gestural interaction, enabled by mmWave radar sensors, signal processing, and deep learning, can significantly enhance usability of virtual interfaces.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from the Google Advanced Technology and Projects group.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Supervisor's Name: Murray-Smith, Professor Roderick and Kaul, Dr. Chaitanya
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85136
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 16 May 2025 15:16
• Last Modified: 16 May 2025 15:17
• Thesis DOI: 10.5525/gla.thesis.85136
• URI: https://theses.gla.ac.uk/id/eprint/85136