A unified approach to the recognition of complex actions from sequences of zone-crossings

We present a method for the recognition of complex actions. Our method combines automatic learning of simple actions and manual definition of complex actions in a single grammar. Contrary to the general trend in complex action recognition that consists in dividing recognition into two stages, our method performs recognition of simple and complex actions in a unified way. This is performed by encoding simple action HMMs within the stochastic grammar that models complex actions. This unified approach enables a more effective influence of the higher activity layers into the recognition of simple actions which leads to a substantial improvement in the classification of complex actions. We consider the recognition of complex actions based on person transits between areas in the scene. As input, our method receives crossings of tracks along a set of zones which are derived using unsupervised learning of the movement patterns of the objects in the scene. We evaluate our method on a large dataset showing normal, suspicious and threat behaviour on a parking lot. Experiments show an improvement of ~ 30% in the recognition of both high-level scenarios and their composing simple actions with respect to a two-stage approach. Experiments with synthetic noise simulating the most common tracking failures show that our method only experiences a limited decrease in performance when moderate amounts of noise are added.

Movement intention detection from autocorrelation of EEG for BCI

Movement intention detection is important for development of intuitive movement based Brain Computer Interfaces (BCI). Various complex oscillatory processes are involved in producing voluntary movement intention. In this paper, temporal dynamics of electroencephalography (EEG) associated with movement intention and execution were studied using autocorrelation. It was observed that the trend of decay of autocorrelation of EEG changes before and during the voluntary movement. A novel feature for movement intention detection was developed based on relaxation time of autocorrelation obtained by fitting exponential decay curve to the autocorrelation. This new single trial feature was used to classify voluntary finger tapping trials from resting state trials with peak accuracy of 76.7%. The performance of autocorrelation analysis was compared with Motor-Related Cortical Potentials (MRCP).

Intake of total and subgroups of fat minimally affect the associations between selected single nucleotide polymorphisms in the PPARγ pathway and changes in anthropometry among European adults from cohorts of the DiOGenes study

BACKGROUND: Although the peroxisome proliferator-activated receptor γ (PPARγ) pathway is central in adipogenesis, it remains unknown whether it influences change in body weight (BW) and whether dietary fat has a modifying effect on the association. OBJECTIVES: We examined whether 27 single nucleotide polymorphisms (SNPs) within 4 genes in the PPARγ pathway are associated with the OR of being a BW gainer or with annual changes in anthropometry and whether intake of total fat, monounsaturated fat, polyunsaturated fat, or saturated fat has a modifying effect on these associations. METHODS: A case-noncase study included 11,048 men and women from cohorts in the European Diet, Obesity and Genes study; 5552 were cases, defined as individuals with the greatest BW gain during follow-up, and 6548 were randomly selected, including 5496 noncases. We selected 4 genes [CCAAT/enhancer binding protein β (CEBPB), phosphoenolpyruvate carboxykinase 2, PPARγ gene (PPARG), and sterol regulatory element binding transcription factor 1] according to evidence about biologic plausibility for interactions with dietary fat in weight regulation. Diet was assessed at baseline, and anthropometry was followed for 7 y. RESULTS: The ORs for being a BW gainer for the 27 genetic variants ranged from 0.87 (95% CI: 0.79, 1.03) to 1.12 (95% CI: 0.96, 1.22) per additional minor allele. Uncorrected, CEBPB rs4253449 had a significant interaction with the intake of total fat and subgroups of fat. The OR for being a BW gainer for each additional rs4253449 minor allele per 100 kcal higher total fat intake was 1.07 (95% CI: 1.02, 1.12; P = 0.008), and similar associations were found for subgroups of fat. CONCLUSIONS: Among European men and women, the influence of dietary fat on associations between SNPs in the PPARγ pathway and anthropometry is likely to be absent or marginal. The observed interaction between rs4253449 and dietary fat needs confirmation.