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The use of smartphones for human activity recognition has become popular due to the wide adoption of smartphones and their rich sensing features. This article introduces a benchmark dataset, the MobiAct dataset, for smartphone-based human activity recognition. It comprises data recorded from the accelerometer, gyroscope and orientation sensors of a smartphone for fifty subjects performing nine different types of Activities of Daily Living (ADLs) and fifty-four subjects simulating four different types of falls. This dataset is used to elaborate an optimized feature selection and classification scheme for the recognition of ADLs, using the accelerometer recordings. Special emphasis was placed on the selection of the most effective features from feature sets already validated in previously published studies. An important qualitative part of this investigation is the implementation of a comparative study for evaluating the proposed optimal feature set using both the MobiAct dataset and another popular dataset in the domain. The results obtained show a higher classification accuracy than previous reported studies, which exceeds 99% for the involved ADLs.

Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.

To investigate the interaction of low frequency electric and magnetic fields with pregnant women and in particular with the fetus, an anatomical voxel model of an 89 kg woman at week 30 of pregnancy was developed. Intracorporal electric current density distributions due to exposure to homogeneous 50 Hz electric and magnetic fields were calculated and results were compared with basic restrictions recommended by ICNIRP guidelines. It could be shown that the basic restriction is met within the central nervous system (CNS) of the mother at exposure to reference level of either electric or magnetic fields. However, within the fetus the basic restriction is considerably exceeded. Revision of reference levels might be necessary.

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International audienceDepression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities ( audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features ( emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying notdepressed individuals on the development set and 0.52/0.81, respectively for the test set