Abstract
The work presented here describes the development and characterization of intensity fiber optic sensor integrated in a specifically designed piece of garment to measure elbow flexion. The sensing head is based on macrobending incorporated in the garment, and the increase of curvature number was studied in order to investigate which scheme provided a good result in terms of sensitivity and repeatability. Results showed the configuration that assured a higher sensitivity (0.644 dBm/deg) and better repeatability was the one with four loops. Ultimately, this sensor can be used for rehabilitation purposes to monitor human joint angles, namely, elbow flexion on stroke survivors while performing the reach functional task, which is the most common upper-limb human gesture. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
Visual motion perception from a moving observer is the most often encountered case in real life situations. It is a complex and challenging problem, although, it can promote the arising of new applications. This article presents an innovative and autonomous robotic system designed for active surveillance and a dense optical flow technique. Several optical flow techniques have been proposed for motion perception however, most of them are too computationally demanding for autonomous mobile systems. The proposed HybridTree method is able to identify the intrinsic nature of the motion by performing two consecutive operations: expectation and sensing. Descriptive properties of the image are retrieved using a tree-based scheme and during the expectation phase. In the sensing operation, the properties of image regions are used by a hybrid and hierarchical optical flow structure to estimate the flow field. The experiments prove that the proposed method extracts reliable visual motion information in a short period of time and is more suitable for applications that do not have specialized computer devices. Therefore, the HybridTree differs from other techniques since it introduces a new perspective for the motion perception computation: high level information about the image sequence is integrated into the estimation of the optical flow. In addition, it meets most of the robotic or surveillance demands and the resulting flow field is less computationally demanding comparatively to other state-of-the-art methods.

Abstract
Human motion capture systems help clinicians to detect and identify mobility impairments, early stages of pathologies and evaluate the effectiveness of surgical or rehabilitation intervention. Although there is a considerable number of solutions presently available, these systems are often expensive, complex, difficult to wear, and uncomfortable for the patient. With the purpose of solving the formerly mentioned problems, a new wearable locomotion data capture system for gait analysis is being developed. This system will allow the measurement of several locomotion-related parameters in a practical and non-invasive way, also reusable, that can be used by patients from light to severe impairments or disabilities. © 2013 The Textile Machinery Society of Japan.

Abstract
Over the last few decades, surveillance applications have been an extremely useful tool to prevent dangerous situations and to identify abnormal activities. Although, the majority of surveillance videos are often subjected to different noises that corrupt structured patterns and fine edges. This makes the image processing methods even more difficult, for instance, object detection, motion segmentation, tracking, identification and recognition of humans. This paper proposes a novel filtering technique named robust bilateral and temporal (RBLT), which resorts to a spatial and temporal evolution of sequences to conduct the filtering process while preserving relevant image information. A pixel value is estimated using a robust combination of spatial characteristics of the pixel's neighborhood and its own temporal evolution. Thus, robust statics concepts and temporal correlation between consecutive images are incorporated together which results in a reliable and configurable filter formulation that makes it possible to reconstruct highly dynamic and degraded image sequences. The filtering is evaluated using qualitative judgments and several assessment metrics, for different Gaussian and Salt Pepper noise conditions. Extensive experiments considering videos obtained by stationary and non-stationary cameras prove that the proposed technique achieves a good perceptual quality of filtering sequences corrupted with a strong noise component.

Abstract
Falls of patients are an important issue in hospitals, it causes severe injuries to the patients, increases hospitalization time and treatment costs. The detection of a fall, in time, provides faster rescue to the patient, preventing more serious injuries, as well as saving nursing time. The MovinSense (R) is an electronic device designed for monitoring patients to prevent pressure sores, and the main goal of this work was to develop a new tool for this device, with the purpose of detecting if the patient has fallen from the hospital bed, without changing any of the device original features. Experiments for gathering data samples of inertial signals of falling from the bed were obtained using the device. For fall detection a sensitivity of 72% and specificity of 100% were reached. Another algorithm was developed to detect if the patient got out of his/her bed.

Abstract