WLMMF-Wireless Multimodal Fusion of mm-Wave Radar and Vision for Real-Time Human Action Recognition (RT-HAR) in Mobile Robotics (MR)

Human Action Recognition (HAR) is essential for enabling mobile robots to interact intelligently and safely in human-centric environments. This study introduces a wireless-aware multimodal fusion framework that integrates millimeter-wave (mm-Wave) radar with vision sensing for real-time HAR, ensuring robustness even under fluctuating wireless and environmental conditions. Unlike prior multimodal HAR frameworks that assume stable connectivity, our design explicitly embeds wireless channel-state feedback into the fusion process, ensuring real-time adaptability under dynamic communication conditions. The radar modality captures motion kinematics via range-Doppler and point-cloud signatures, while the camera provides spatial and appearance cues. Latency on the prototype has been kept below 50[Formula: see text]ms, and the proper integration of these heterogeneous features is additionally safeguarded through a lightweight deep-learning pipeline using adaptive fusion weighting. All the experiment simulation Outcome results showed that the proposed system obtains recognition accuracy values of up to 92% and an F1-score of 0.91, which actually outperforms both vision-only having 84% accuracy along with radar-only with 86% accuracy baselines. The results prove that wireless-vision fusion is possible for deploying real-world mobile robots in which robustness to environmental uncertainty becomes very important. The work also establishes the framework of wireless sensing in supporting context-aware human–robot interaction.