Sensor Modality

Sensor modality research focuses on optimizing the selection and integration of diverse sensor types (e.g., visual, inertial, tactile, sonar) to improve data acquisition and analysis in various applications. Current research emphasizes developing robust fusion algorithms, such as graph neural networks and transformers, to effectively combine data from heterogeneous sensors with varying temporal dynamics and to address challenges like data scarcity and computational constraints. This work is crucial for advancing fields like human activity recognition, autonomous navigation, and human-robot interaction by enabling more accurate, efficient, and reliable system performance. The development of large, diverse datasets and efficient model architectures are key to achieving these goals.