Domain Adaptation with Contrastive Simultaneous Multi-Loss Training for Hand Gesture Recognition.

Hand gesture recognition from images is a critical task with various real-world applications, particularly in the field of human-robot interaction. Industrial environments, where non-verbal communication is preferred, are significant areas of application for gesture recognition. However, these environments are often unstructured and noisy, with complex and dynamic backgrounds, making accurate hand segmentation a challenging task. Currently, most solutions employ heavy preprocessing to segment the hand, followed by the application of deep learning models to classify the gestures. To address this challenge and develop a more robust and generalizable classification model, we propose a new form of domain adaptation using multi-loss training and contrastive learning. Our approach is particularly relevant in industrial collaborative scenarios, where hand segmentation is difficult and context-dependent. In this paper, we present an innovative solution that further challenges the existing approach by testing the model on an entirely unrelated dataset with different users. We use a dataset for training and validation and demonstrate that contrastive learning techniques in simultaneous multi-loss functions provide superior performance in hand gesture recognition compared to conventional approaches in similar conditions.

Rotation related systematic effects in a cold atom interferometer onboard a Nadir pointing satellite.

We study the effects of rotations on a cold atom accelerometer onboard a Nadir pointing satellite. A simulation of the satellite attitude combined with a calculation of the phase of the cold atom interferometer allow us to evaluate the noise and bias induced by rotations. In particular, we evaluate the effects associated to the active compensation of the rotation due to Nadir pointing. This study was realized in the context of the preliminary study phase of the CARIOQA Quantum Pathfinder Mission.