Abstract
Until recent years most research involving the capture and analysis of biometric and/or physiological signals have been limited to a laboratory or otherwise controlled environment. Wearable technologies introduced a refinement to personal signal capturing by permitting a long-term onperson approach. Sensors, integrated circuits, textile integration and other elements are directly responsible for advancements in this area; however, in spite of the present progress there are still a number of obstacles to overcome for truly achieving seamless wearable monitoring technology (WMT). This article presents an overview of a generic monitoring systems architecture based on designs found in recent literature and commercially available solutions. A custom implementation based on commercially available components and evaluation boards is also presented, including some obtained data in varying body locations and/or activities. © 2010 IEEE.

Abstract
In this paper we present an evaluation of optical flow techniques applied to a case study in the perception of visual motion. This case study is being conducted in a project for the evaluation of human factors in road traffic, specifically, concerning the processing of visual information. We present the goals of the case study, discuss the need to apply optical flow techniques to synthesized image sequences and evaluate some limitations encountered in their use. © 1996 IEEE.

Abstract
Optical flow algorithms generally demand for high computational power and huge storage capacities. This paper is a contribution for real-time implementation of an optical flow algorithm on a pipeline machine. This overall optical flow computation methodology is presented and evaluated on a set of synthetic and real image sequences. Results are compared to other implementations using as measures the average angular error, the optical flow density and the root mean square error. The proposed implementation achieves very low computation delays, allowing operation at standard video frame-rate and resolution. It compares favorably to recent implementations in standard microprocessors and in parallel hardware.

Abstract
Compressive sensing (CS) has recently emerged and is now a subject of increasing research and discussion, undergoing significant advances at an incredible pace. The novel theory of CS provides a fundamentally new approach to data acquisition which overcomes the common wisdom of information theory, specifically that provided by the Shannon-Nyquist sampling theorem. Perhaps surprisingly, it predicts that certain signals or images can be accurately, and sometimes even exactly, recovered from what was previously believed to be highly incomplete measurements (information). As the requirements of many applications nowadays often exceed the capabilities of traditional imaging architectures, there has been an increasing deal of interest in so-called computational imaging (CI). CI systems are hybrid imagers in which computation assumes a central role in the image formation process. Therefore, in the light of CS theory, we present a transmissive single-pixel camera that integrates a liquid crystal display (LCD) as an incoherent random coding device, yielding CS-typical compressed observations, since the beginning of the image acquisition process. This camera has been incorporated into an optical microscope and the obtained results can be exploited towards the development of compressive-sensing-based cameras for pixel-level adaptive gain imaging or fluorescence microscopy.

Abstract
A main requirement in clinical gait analysis is the ability to accurately identify gait events; especially, the initial contact of the heel with the floor and the toe off. The knowledge of the major events of the gait cycle is needed, for instance, in biomechanical data normalization and in the calculation of several temporal/distance parameters. The most common technologies for gait event detection are foot switches and force platforms; however, if the same performance could be achieved, it would be preferable to use the information collected by visual sensors to detect the main gait events. This paper proposes a procedure to be used in the detection of not just stance phase events (i.e. initial contact, opposite toe off, heel rise, opposite initial contact), but also of swing phase events (i.e. toe off, feet adjacent, tibia vertical) through the analysis of visual gait data acquired by image cameras. Moreover, in this paper, it is compared the performance of detecting the initial contact and toe off using our visual methodology and the information obtained from force platforms.

Abstract
In this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.