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BACKGROUND Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests. OBJECTIVE To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources' connectivity based on a support vector machine (SVM) applied to EEG small-world parameter. METHODS A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). RESULTS The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification. CONCLUSION EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC).

We present a real time method for updating a 3D map with dynamic obstacles detection. Moving obstacles are detected through ray-casting on spherical voxelization of point clouds. We evaluate the accuracy of this method on a point cloud dataset, suitably constructed for testing ray-surface intersection under relative motion conditions. Moreover, we show the benefits of the map updating in a real robot equipped with a rotating LIDAR system, navigating in real world scenarios, populated by moving people.

International audience; This paper describes our experience in designing, developing and deploying systems for supporting human-robot teams during disaster response. It is based on R&D performed in the EU-funded project NIFTi. NIFTi aimed at building intelligent, collaborative robots that could work together with humans in exploring a disaster site, to make a situational assessment. To achieve this aim, NIFTi addressed key scientific design aspects in building up situation awareness in a human-robot team, developing systems using a user-centric methodology involving end users throughout the entire R&D cycle, and regularly deploying implemented systems under real-life circumstances for experimentation and testing. This has yielded substantial scientific advances in the state-of-the-art in robot mapping, robot autonomy for operating in harsh terrain, collaborative planning, and human-robot interaction. NIFTi deployed its system in actual disaster response activities in Northern Italy, in July 2012, aiding in structure damage assessment.

Modeling cognitive control is a major issue in robot control, and it is about deciding when a task cannot succeed and a new task need to be initiated. These decisions are induced by incoming stimuli alerting of events taking place while the robot is executing its duties. To learn cognitive control we address the human inspired mechanisms that govern cognitive control and that have been widely studied in neuroscience, namely, shifting and inhibition. Shifting and inhibition are, in fact, executive cognitive functions responding selectively to stimuli, so as to switch from one activity to a more compelling one or to inhibit inappropriate urges and preserve focus on the current task. In an autonomous system these cognitive skills are crucial to assess a well-regulated reactive behavior, which is of particular relevance in critical circumstances. In this paper we illustrate a new method developed for learning shifting and inhibition, based on Gaussian Processes, and using examples provided by skilled operators. We finally show that the learning method is promising and can be seen as a new view for modeling robot reactive and proactive behaviors.