Papers
1
Total Citations
7
H-Index
1
About
Dana Axinte is a researcher whose work sits at the intersection of robotics, computer vision, and human-robot interaction. Her key research areas include human activity recognition, assistive robotics, and the application of deep learning to robotic perception. Her most notable contribution is a 2018 study on human activity recognition using the TIAGo robot, where she developed a two-layer convolutional neural network that fuses spatial and temporal information from RGB camera feeds. This work, which has garnered 7 citations, demonstrates a practical approach to enabling robots to understand and respond to human actions in real time. By processing video frames individually and combining spatial cues with temporal dynamics, Axinte’s method offers a lightweight yet effective solution for activity classification. Her research is particularly relevant to the development of socially aware assistive robots, where accurate perception of human behavior is critical. Axinte’s work contributes to the growing field of embodied AI, bridging the gap between raw sensor data and meaningful robotic responses.
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Top Papers
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