Basic Human Body Dimensions Relate to Alcohol Dependence and Predict Hospital Readmission

Alcohol dependence is a severe mental illness and there is a need for more effective preventive and therapeutic strategies. Translational research suggests that intrauterine sex hormone exposure modulates the risk and course of alcohol dependence during adulthood. During development, sex hormones permanently shape sexually dimorphic body dimensions. Thus, these dimensions may provide insight into sex hormone organization. Here, we compared body measurements (absolute, relative to, and residualized on height) between 200 alcohol-dependent in-patients and 240 age-matched healthy control subjects and investigated how these measurements associate with the patients’ prospective 12- and 24-month outcome. The results show that alcohol dependence is related to lower absolute, relative, and residualized body measurements for height and weight, head circumference, bitragion head arc, lip-chin distance, hip, thigh, and calf circumference, and foot length and breadth. In male alcohol-dependent in-patients, higher risk, shorter latency, and more alcohol-related readmissions were predicted by higher absolute, relative, and residualized thigh and calf circumferences. The second-to-fourth finger length ratio, a putative proxy for prenatal sex hormone organization, was not convincingly correlated with the body dimensions, suggesting that the results represent pubertal (or later) effects. The study’s findings have implications for further research. The body measurements’ high accessibility may facilitate the future transition into clinical settings

Fusion of Single View Soft k-NN Classifiers for Multicamera Human Action Recognition

Proceedings of: 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010). San Sebastián, Spain, June 23-25, 2010This paper presents two different classifier fusion algorithms applied in the domain of Human Action Recognition from video. A set of cameras observes a person performing an action from a predefined set. For each camera view a 2D descriptor is computed and a posterior on the performed activity is obtained using a soft classifier. These posteriors are combined using voting and a bayesian network to obtain a single belief measure to use for the final decision on the performed action. Experiments are conducted with different low level frame descriptors on the IXMAS dataset, achieving results comparable to state of the art 3D proposals, but only performing 2D processing.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02Publicad

Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes

Development of a bipolar cell for electrochemical production of lithium

Lithium metal can be electrolytically refined from aqueous solutions of its compounds by partial reduction to form a lithium amalgam, followed by reduction of the amalgam to liquid lithium in a molten salt cell at 225 C. A bipolar cell (with a continuous, amalgam electrode circulating between the aqueous and salt cells) was designed, constructed and successfully tested on the bench scale, as a proof of principle of an efficient, safe and low-temperature alternative to existing processes

Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification

Proceedings of: 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011.This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.Publicad

A 3D Human Posture Approach for Activity Recognition Based on Depth Camera

Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL