Abstract
Wearable technologies have been evolving towards daily usage and are a major player in the personalized health challenge. In this paper we present a personal view of their evolution, how one of them developed within our lab went to the international market and how this type of technology is being used in pHealth projects for first responder professionals and public transportation drivers.

Abstract
Depression is expected to be the disorder with the highest disease burden in high-income countries by the year 2030. ICT4Depression (ICT4D) is a European FP7 project, which aims to contribute to the alleviation of this burden by making use of depression treatment and ICT innovations. In this project we developed an ICT-based system for use in primary care that aims to improve access as well as actual care delivery for depressed adults. Innovative technologies within the ICT4D system include 1) flexible self-help treatments for depression, 2) automatic assessment of the patient using mobile phone and web-based communication 3) wearable biomedical sensor devices for monitoring activities and electrophysiological indicators, 4) computational methods for reasoning about the state of a patient and the risk of relapse (reasoning engine) and 5) a flexible system architecture for monitoring and supporting people using continuous observations and feedback via mobile phone and the web. The general objective of the ICT4D project is to test the feasibility and acceptability of the ICT4D system within a pilot study in the Netherlands and in Sweden during 2012 and 2013. © 2012 Interactive Media Institute and IOS Press.

Abstract
Permutation tests have extensively been used to estimate the significance of classification. Permutation tests usually use the test error as a dataset statistic to measure the difference between two or more populations. Then, to estimate the p-value(s), the test error is compared to a set of permuted test-error(s), which is usually obtained after permuting the labels of the populations. In this study, we investigate how several dataset factors, e.g., the number of samples, the number of classes, and the dimensionality size, may affect the p-value obtained via permutation tests. We performed the analysis using the standard permutation test procedure that uses the overall all test error dataset statistic and compared it to the permutation test procedure that uses per-class test error as a dataset statistic that we recently have proposed (doi:10.1016/j.neucom.2011.11.007). We found that permutation tests that use a per-class test error as a dataset statistic are not only more reliable in addressing the null hypothesis but also are highly sensitive to changes in the dataset factors that we investigated in this work. An important finding of this study is that when the dimensionality is low and the number of classes is up to several, say ten, highly above chance accuracy would be required to state the significance. For the same low dimensionality, however, slightly above chance accuracy would be adequate to state significance in a two-class problem.

Abstract
There has been increasing interest in pattern classification methods and neuroimaging studies using permutation tests to estimate the statistical significance of a classifier (p-value). Permutation tests usually use the test error as a dataset statistic to estimate the p-value(s) by measuring the dissimilarity between two or more populations. Using the test error as a dataset statistic; however, may camouflage the lowest recognizable classes, and the resulting p-value will be biased toward better values (usually lower values) because of the highly recognizable classes; thus, lower p-values could sometimes be the result of undercoverage. In this study, we investigate this problem and propose the implementation of permutation tests based on a per-class test error as a dataset statistic. We also propose a model that is based on partially scrambling the testing samples (in this model, the training samples are not scrambled) when computing the non-permuted statistic in order to judge the p-value's tolerance and to draw conclusions regarding, which permutation test procedures are more reliable. For the same purpose, we propose another model that is based on chance-level shifting of the permuted statistic. We tested these two proposed models on functional magnetic resonance imaging data that were collected while human subjects responded to visual stimulation paradigms, and our results showed that these models can aid in determining, which permutation test procedure is superior. We also found that permutation tests that use a per-class test error as a dataset statistic are more reliable in addressing the null hypothesis that all classes in the problem domain are drawn from the same distribution.

Abstract
Optical characterization and internal structure of biological tissues is highly important for biomedical optics. In particular for optical clearing processes, such information is of vital importance to understand the mechanisms involved through the variation of the refractive indices of tissue components. The skeletal muscle presents a fibrous structure with an internal arrangement of muscle fiber cords surrounded by interstitial fluid that is responsible for strong light scattering. To determine the refractive index of muscle components we have used a simple method of measuring tissue mass and refractive index during dehydration. After performing measurements for natural and ten dehydration states of the muscle samples, we have determined the dependence between the refractive index of the muscle and its water content. Also, we have joined our measurements with some values reported in literature to perform some calculations that have permitted to determine the refractive index of the dried muscle fibers and their corresponding volume percentage inside the natural muscle.

Abstract
Movement quantification of the human body is presently used for analyzing deficits resulting from Central Nervous System (CNS) pathologies or exploring the insights of the human motor system behaviour. Following our previous work on 2D movement quantification of epileptic seizures, we now present a feasibility study for a newly developed 3D technique. In order to validate this new 3D approach we made a comparison with the previous method. Both techniques were tested in two different datasets: a simple motor execution performed by a volunteer and a complex motor motion induced by a real epileptic seizure. The results obtained showed, as expected, the superior robustness and precision of the 3D approach but also confirmed the validity of the 2D method, given certain constraints. We conclude that the newly developed 3D system will highly improve our capacity of pursuing the clinical research on quantitative characterization of seizure semiology to support epilepsy diagnosis.