Hojjat Salehinejad
Papers
6
Total Citations
91
H-Index
5
About
Hojjat Salehinejad is pioneering the intersection of artificial intelligence, wireless sensing, and robotics, with a focus on enabling robots to perceive and predict motion without relying on traditional cameras. His core research addresses a critical challenge: how can robotic systems recognize and anticipate movements in indoor environments where visibility is limited and privacy is a concern? Instead of using vision or LiDAR, Salehinejad leverages passive WiFi signals—specifically Channel State Information (CSI)—as a non-intrusive, privacy-preserving alternative. His highly cited narrative review on AI in robotic surgery (41 citations) establishes the clinical relevance of this work, while his foundational paper on robot motion prediction using CSI (11 citations) laid the groundwork for a new sensing paradigm. With the RoboFiSense framework (16 citations) and the RoboMNIST multimodal dataset (13 citations), he has created both the algorithms and the benchmark data needed to advance multi-robot activity recognition. By integrating Vision Transformers with wavelet-transformed CSI and testing robustness in noisy environments, Salehinejad is building the sensory backbone for the next generation of autonomous, collaborative robots.
Research Focus
Key Achievements
Top Papers
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- 4Robot Motion Prediction by Channel State Information11 citations · 2023
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