BackgroundAt present there are no objective, biological markers that can be used to reliably identify individuals with post-traumatic stress disorder (PTSD). This study assessed the diagnostic potential of structural magnetic resonance imaging (sMRI) for identifying trauma-exposed individuals with and without PTSD.MethodsMRI scans were acquired from 50 survivors of the Sichuan earthquake of 2008 who had developed PTSD, 50 survivors who had not developed PTSD and 40 healthy controls who had not been exposed to the earthquake. Support vector machine (SVM), a multivariate pattern recognition technique, was used to develop an algorithm that distinguished between the three groups at an individual level. The accuracy of the algorithm and its statistical significance were estimated using leave-one-out cross-validation and permutation testing.ResultsWhen survivors with PTSD were compared against healthy controls, both grey and white matter allowed discrimination with an accuracy of 91% (p < 0.001). When survivors without PTSD were compared against healthy controls, the two groups could be discriminated with accuracies of 76% (p < 0.001) and 85% (p < 0.001) based on grey and white matter, respectively. Finally, when survivors with and without PTSD were compared directly, grey matter allowed discrimination with an accuracy of 67% (p < 0.001); in contrast the two groups could not be distinguished based on white matter.ConclusionsThese results reveal patterns of neuroanatomical alterations that could be used to inform the identification of trauma survivors with and without PTSD at the individual level, and provide preliminary support to the development of SVM as a clinically useful diagnostic aid.

Most of the existing gait recognition methods rely on a single view, usually the side view, of the walking person. This paper investigates the case in which several views are available for gait recognition. It is shown that each view has unequal discrimination power and, therefore, should have unequal contribution in the recognition process. In order to exploit the availability of multiple views, several methods for the combination of the results that are obtained from the individual views are tested and evaluated. A novel approach for the combination of the results from several views is also proposed based on the relative importance of each view. The proposed approach generates superior results, compared to those obtained by using individual views or by using multiple views that are combined using other combination methods.

We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of HMMs. In the proposed system, holistic features are initially used for capturing general gait dynamics while, subsequently, model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. Experimental results show that the proposed method exhibits performance advantages in comparison to popular existing methods.

In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, the Shifted Energy Image and the Gait Structural Profile, that have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of Linear Discriminant Analysis. In the decision stage, the two representations are fused.Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison to current state-of-the-art systems.

In this paper, we present a gait recognition method that does not presume the existence of strict lab conditions for its operation. The proposed algorithm includes a side-view detection and extraction approach that is useful when the subject is walking randomly as well as a novel template for gait representation that is robust to body posture variations. Experimental results show that the proposed system not only has high computational efficiency, but also exhibits robust performance, especially in cases where the subject is walking along random paths.