Orientability is an important performance index to evaluate the dexterity of haptic master devices. Currently, most of the existing haptic master devices have limited workspace and limited dexterity. In this paper, we present the RemoT-ARM, a 6 Degree-of-Freedom (DOF) haptic master device that can provide larger and more dexterous workspace for operators. To evaluate its reachability of orientations, we propose a novel orientability index. Furthermore, a relative orientability index is proposed to characterize the matching degree of the workspace of a given manipulator to its target workspace. The volume, the manipulability and the condition number are also introduced as performance indices to evaluate the size and the isotropy of the workspace. According to these performance indices, all possible configurations for the RemoT-ARM have been taken into consideration, analyzed, and compared to finalize its optimal configuration.

This paper addresses the problem of predicting emergent leaders (ELs) in small groups, that is, meetings. This is a long-lasting research problem for social and organizational psychology and a relevant problem that recently gained momentum in social computing. Toward this goal, we propose a novel method, which analyzes the temporal dependencies of the audio-visual data by applying unsupervised deep learning generative models (feature learning). To the best of our knowledge, this is the first attempt that sequential data processing is performed for EL detection. Feature learning results in a single feature vector per a given time interval and all feature vectors representing a participant are aggregated using novel fusion techniques. Finally, the EL detection is performed using the state-of-the-art single and multiple kernel learning algorithms. The proposed method shows (significantly) improved results compared to the state-of-the-art methods and it can be adapted to analyze various small group interactions given that it is a general approach.

Exploring the human structural connectome often involves dealing with millions of white matter tracts reconstructed from diffusion MRI. Reducing the dimensionality of such data by grouping tracts into bundles can prove essential for subsequent analyses. Many unsupervised clustering algorithms aim at providing such bundles but often require the choice of a distance metric and suffer from memory storage issues relating to the size of the datasets. We propose for the first time a neural network approach for the unsupervised clustering of white matter tracts. It has the main properties of learning automatically the tract features and scaling well with the data size. As a proof of concept, we compare both quantitatively and qualitatively the computed tract clusters with a commonly used clustering method. The proposed approach shows results similar to the reference approach while not using any distance matrix or similarity metric.

Mouse models play an important role in preclinical research and drug discovery for human diseases. The fact that mice are a social species partaking in social interactions of high degree facilitates the study of diseases characterized by social alterations. Hence, robust animal tracking is of great importance in order to build tools capable of automatically analyzing social behavioral interactions of multiple mice. However, the presence of occlusions is a major problem in multiple mice tracking. To deal with this problem, we present here a tracking algorithm based on Kalman filter and Gaussian Mixture Modeling. Specifically, Kalman tracking is used to track the mice and when occlusions happen, we fit 2D Gaussian distributions to separate mouse blobs. This helps us to prevent mice identity swaps as it is an important feature for accurate behavior analysis. As the results of our experiments show, the proposed algorithm results in much fewer identity swaps than other state of the art algorithms.

Extracting a statistically significant result from video of natural phenomenon can be difficult for two reasons: (i) there can be considerable natural variation in the observed behaviour and (ii) computer vision algorithms applied to natural phenomena may not perform correctly on a significant number of samples. This study presents one approach to clean a large noisy visual tracking dataset to allow extracting statistically sound results from the image data. In particular, analyses of 3.6 million underwater trajectories of a fish with the water temperature at the time of acquisition are presented. Although there are many false detections and incorrect trajectory assignments, by a combination of data binning and robust estimation methods, reliable evidence for an increase in fish speed as water temperature increases are demonstrated. Then, a method for data cleaning which removes outliers arising from false detections and incorrect trajectory assignments using a deep learning‐based clustering algorithm is proposed. The corresponding results show a rise in fish speed as temperature goes up. Several statistical tests applied to both cleaned and not‐cleaned data confirm that both results are statistically significant and show an increasing trend. However, the latter approach also generates a cleaner dataset suitable for other analysis.

Rodents play an important role in sleep studies since they are the most easily available low-cost animal species sharing most genes and gene functions with humans. The scoring of sleep stages in these studies is usually based on a manual analysis of long-lasting recordings of the brain electrical activity and the activity of skeletal muscles. Hence, there is a great need for tools automating the investigation over large experiments. In this paper we present a way of analyzing this huge amount of electrophysiological data using unsupervised learning models. We show how employing latent variable models, like Restricted Boltzmann Machines, we can define different sleep-wakefulness sub-stages reflected by regularities in the data, without any prior knowledge. Our analysis shows that we can effectively discover meaningful feature representations which characterize sleep stages at a finer level than those commonly used by the experts. These feature representations can be also used to further characterize different mouse genotypes, without incurring biases of experts like in a classical analysis setup where predefined rules limit the discovery of novel insights.

Detecting leadership while understanding the underlying behavior is an important research topic particularly for social and organizational psychology, and has started to get attention from social signal processing research community as well. It is known that, visual activity is a useful cue to investigate the social interactions, even though previously applied nonverbal features based on head/body actions were not performing well enough for identification of emergent leaders (ELs) in small group meetings. Starting from these premises, in this study, we propose an effective method that uses 2D body pose based nonverbal features to represent the visual activity of a person. Our results suggest that, i) overall, the proposed nonverbal features derived from body pose perform better than existing visual activity based features, ii) it is possible to improve classification results by applying unsupervised feature learning as a preprocessing step, and iii) the proposed nonverbal features are able to advance the EL identification performances of other types of nonverbal features when they are used together.

Automatic analysis of rodent behavior has been receiving growing attention in recent years since rodents have been the reference species for many neuroscientific studies, with the social interaction being among the subjects of the most important ones. Systems that are employed in these studies are mainly based on tracking of mice and activity classification through supervised learning methods, trained on datasets manually annotated by experts. In this paper, we introduce a completely unsupervised way of analysing tracking data for the automatic identification of social and non-social behaviors using models capable of spotting regularities in the data. In particular, a mean-covariance Restricted Boltzmann Machine is employed to abstract higher-level behavioral configurations of mice interacting in an arena for a long time.