Unsupervised feature learning using self-organizing maps.

In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. These approaches are known as feature learning or deep learning methods and have been successfully applied to classify scene images and recognize with high precision handwritten characters. In this thesis we show that a feature learning approach can be used to segment complex textures, a problem for a long time addressed proposing a large amount of handcrafted descriptors and local optimization strategies. We employ the SOM neural network for its ability to natively provide a set of topologically ordered features. These features allow us to obtain a highly accurate local description, even in areas characterized by a transition from one texture to another. We also show that a single feature learning unit can be combined with others in order to significantly improve the quality of the texture description and, consequently, reduce the segmentation errors. The results obtained prove that the proposed segmentation method is valid and provides a real alternative to other state-of-the-art methods. Since the proposed framework is simple, we easily combined it with a pyramidal histogram encoding and a SVM supervised network in order to classify scene images. We show that the important topological ordering property, inherited from the SOM network, allow us to resize the feature set, obtained during the initial unsupervised learning, avoiding an unpredictable performance loss. Moreover, the results on the standard Caltech-101 dataset prove a significant improvement on some state-of-the-art computer vision methods, designed specifically for image classification

Content-based Filtering in On-line Social Networks

This paper proposes a system enforcing content-based message filtering for On-line Social Networks (OSNs). The system allows OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows a user to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labelling messages in support of content-based filtering

Physical human-robot interaction of an active pelvis orthosis: toward ergonomic assessment of wearable robots

Abstract Background In human-centered robotics, exoskeletons are becoming relevant for addressing needs in the healthcare and industrial domains. Owing to their close interaction with the user, the safety and ergonomics of these systems are critical design features that require systematic evaluation methodologies. Proper transfer of mechanical power requires optimal tuning of the kinematic coupling between the robotic and anatomical joint rotation axes. We present the methods and results of an experimental evaluation of the physical interaction with an active pelvis orthosis (APO). This device was designed to effectively assist in hip flexion-extension during locomotion with a minimum impact on the physiological human kinematics, owing to a set of passive degrees of freedom for self-alignment of the human and robotic hip flexion-extension axes. Methods Five healthy volunteers walked on a treadmill at different speeds without and with the APO under different levels of assistance. The user-APO physical interaction was evaluated in terms of: (i) the deviation of human lower-limb joint kinematics when wearing the APO with respect to the physiological behavior (i.e., without the APO); (ii) relative displacements between the APO orthotic shells and the corresponding body segments; and (iii) the discrepancy between the kinematics of the APO and the wearer’s hip joints. Results The results show: (i) negligible interference of the APO in human kinematics under all the experimented conditions; (ii) small (i.e., < 1 cm) relative displacements between the APO cuffs and the corresponding body segments (called stability); and (iii) significant increment in the human-robot kinematics discrepancy at the hip flexion-extension joint associated with speed and assistance level increase. Conclusions APO mechanics and actuation have negligible interference in human locomotion. Human kinematics was not affected by the APO under all tested conditions. In addition, under all tested conditions, there was no relevant relative displacement between the orthotic cuffs and the corresponding anatomical segments. Hence, the physical human-robot coupling is reliable. These facts prove that the adopted mechanical design of passive degrees of freedom allows an effective human-robot kinematic coupling. We believe that this analysis may be useful for the definition of evaluation metrics for the ergonomics assessment of wearable robots