Year: 2025
Type: article
Abstract: Gestures serve as an intuitive and natural medium for conveying human intent and personal identity, offering a convenient, contactless, and privacy-preserving interaction modality for human-computer interaction (HCI) systems. This paper proposes a radar-based multimodal framework for joint gesture and identity recognition, aimed at enhancing performance in radar-based gesture-identity recognition tasks. First, a robust preprocessing and multimodal feature extraction method is introduced, which integrates gesture-range-based valid frame detection with clutter suppression, enabling the extraction of multidimensional gesture features including micro-Doppler maps (MDMs), elevation-time maps (ETMs), and azimuth-time maps (ATMs). Next, a novel Joint Recognition Framework with Cross-Modal Attention Fusion (JRF-CMAF) is proposed, which incorporates Adaptive Rectification Blocks (ARBs) to dynamically leverage the complementary and correlated information across modalities. Extensive experiments were conducted on a custom radar gesture dataset collected from 7 volunteers performing 7 distinct gestures. The proposed JRF-CMAF achieves accuracies of 99.76%, 97.57%, and 96.84% in gesture recognition, identity recognition, and joint recognition tasks, respectively. Compared with conventional gesture recognition approaches and existing radar-based identity recognition methods, it attains the highest overall recognition accuracy.
Source: Sensors
DOI: 10.3390/s25237249
Institution: Nanjing University of Science and Technology
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G4261717650
OpenAlex ID: W4416782641