Healico-a wound care assistant in your pocket.

Non-optimal wound management and late referral to specialised units negatively impacts patient prognosis and quality of life, as well as healthcare costs. Healico is a new mobile application (app), created in the wound care field, in response to the challenges and difficulties encountered by health professionals (HPs) who deal with patients with wounds on a daily basis. This article aims to describe how this new app was developed, how it works, as well as the real-life clinical benefits and evidence supporting its use. The Healico App assists nurses, physicians and other HPs by: supporting a holistic approach to patient management; facilitating wound assessment and documentation, irrespective of where care is provided (primary, specialised or hospital services, in either public or private institutions); and supporting consistent and safe clinical practice, as well as reducing variation in care. It also provides a fast, fluid and secure communication channel, and effective coordination between HPs, supporting early interventions. The app has also been shown to improve therapeutic adherence of patients by promoting inclusive dialogue with them.

[Benefit of hypnosis in in-home nursing care]. / Intérêt de l'hypnose dans la pratique infirmière à domicile.

Treating pain is a priority for caregivers and patients. With in-home care, nurses care for patients who are sometimes in pain and for whom they implement treatments prescribed by the doctor. A nurse and a doctor describe here the complementary contribution of hypnosis in the treatment of pain, including in in-home nursing practice.

A Study of Vicon System Positioning Performance.

Motion capture setups are used in numerous fields. Studies based on motion capture data can be found in biomechanical, sport or animal science. Clinical science studies include gait analysis as well as balance, posture and motor control. Robotic applications encompass object tracking. Today's life applications includes entertainment or augmented reality. Still, few studies investigate the positioning performance of motion capture setups. In this paper, we study the positioning performance of one player in the optoelectronic motion capture based on markers: Vicon system. Our protocol includes evaluations of static and dynamic performances. Mean error as well as positioning variabilities are studied with calibrated ground truth setups that are not based on other motion capture modalities. We introduce a new setup that enables directly estimating the absolute positioning accuracy for dynamic experiments contrary to state-of-the art works that rely on inter-marker distances. The system performs well on static experiments with a mean absolute error of 0.15 mm and a variability lower than 0.025 mm. Our dynamic experiments were carried out at speeds found in real applications. Our work suggests that the system error is less than 2 mm. We also found that marker size and Vicon sampling rate must be carefully chosen with respect to the speed encountered in the application in order to reach optimal positioning performance that can go to 0.3 mm for our dynamic study.

PHROG: A Multimodal Feature for Place Recognition.

Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera. In this paper, we emphasize our work on testing usual feature point extractors under both constraints: repeatability across spectral ranges and long-term appearance. We develop a new feature extraction method dedicated to improve the repeatability across spectral ranges. We conduct an evaluation of feature robustness on long-term datasets coming from different imaging sources (optics, sensors size and spectral ranges) with a Bag-of-Words approach. The tests we perform demonstrate that our method brings a significant improvement on the image retrieval issue in a visual place recognition context, particularly when there is a need to associate images from various spectral ranges such as infrared and visible: we have evaluated our approach using visible, Near InfraRed (NIR), Short Wavelength InfraRed (SWIR) and Long Wavelength InfraRed (LWIR).

The Observatoire en Ville des Plaies ExSudatives (VIPES) study: insight into the patient characteristics, epidemiology, previous management, and features of wounds treated in the French community setting.

BACKGROUND: Wounds that become complex and hard-to-heal are a challenge for all health care systems. Identifying and understanding the nature of these wounds is necessary to allow appropriate intervention. OBJECTIVE: To present the epidemiological outcomes of the VIPES study. MATERIALS AND METHODS: The prospective, observational VIPES study aimed to describe the use and investigate the performance of 2 wound dressings-a silicone foam and a gelling fiber-in the management of chronic and acute wounds in a community setting in France. RESULTS: Of 407 patients recorded, 285 were included in the analysis. The 184 chronic wounds included ulcers (venous/arterial/mixed, diabetic foot, and pressure) and malignant wounds. The 101 acute wounds included surgical and traumatic wounds. Of all wounds, 98.2% were exuding and 77.9% showed exudate pooling. Unhealthy wound edges and periwound skin were reported in 57.2% and 35.4% of wounds, respectively. Of all wounds, 78.6% were in treatment failure (poor exudate management or stagnant wound). The silicone foam dressing (n = 86) and the gelling fiber (n = 199) were generally used in wounds with low or moderate exudation, or moderate or high exudation, respectively. CONCLUSIONS: The VIPES study highlights that wounds can be complex and that community care practices in France warrant improvement. Practical and up-to-date wound management recommendations are needed.

A comparative study of semantic segmentation of omnidirectional images from a motorcycle perspective.

The semantic segmentation of omnidirectional urban driving images is a research topic that has increasingly attracted the attention of researchers, because the use of such images in driving scenes is highly relevant. However, the case of motorized two-wheelers has not been treated yet. Since the dynamics of these vehicles are very different from those of cars, we focus our study on images acquired using a motorcycle. This paper provides a thorough comparative study to show how different deep learning approaches handle omnidirectional images with different representations, including perspective, equirectangular, spherical, and fisheye, and presents the best solution to segment road scene omnidirectional images. We use in this study real perspective images, and synthetic perspective, fisheye and equirectangular images, simulated fisheye images, as well as a test set of real fisheye images. By analyzing both qualitative and quantitative results, the conclusions of this study are multiple, as it helps understand how the networks learn to deal with omnidirectional distortions. Our main findings are that models with planar convolutions give better results than the ones with spherical convolutions, and that models trained on omnidirectional representations transfer better to standard perspective images than vice versa.

Avant-propos.

Hard-to-heal wounds are a problem for both patients and caregivers. The biofi lm is one of the local factors of delayed healing. Wound hygiene carried out in 4 steps (cleansing, debridement, refashion - care of the edges, and dressing) constitutes the basis of proactive and curative anti-biofi lm strategies.

Homography Based Egomotion Estimation with a Common Direction.

In this paper, we explore the different minimal solutions for egomotion estimation of a camera based on homography knowing the gravity vector between calibrated images. These solutions depend on the prior knowledge about the reference plane used by the homography. We then demonstrate that the number of matched points can vary from two to three and that a direct closed-form solution or a Gröbner basis based solution can be derived according to this plane. Many experimental results on synthetic and real sequences in indoor and outdoor environments show the efficiency and the robustness of our approach compared to standard methods.

Efficacy and safety of hyaluronic acid in the management of acute wounds.

INTRODUCTION: Hyaluronic acid (hyaluronan) is a key component of the extracellular matrix and is known to be involved in several mechanisms of the wound healing process. It has been shown to improve and accelerate the healing process of chronic wounds. This open-label study investigated whether application of hyaluronic acid could also improve the healing progression of acute wounds. METHODS: Forty-three patients with acute wounds, including trauma wounds, surgical sutures, burns, and dermabrasions, were included in the study. Hyaluronic acid (ialuset) was applied either as a cream or as a dressing, and the progression and quality of healing were evaluated at each application for a maximum of nine applications. RESULTS: The mean surface area of the wounds decreased from 556 mm(2) at baseline to 169 mm(2) by the sixth evaluation, an average reduction of 70% in the surface area of the wounds. Complete healing occurred in 56% of cases by the sixth application. Little or no fibrin was observed in 91% of cases, and 84% of wounds showed little or no exudate. Both formulations of hyaluronic acid were well tolerated and were rated highly by nurses and patients for ease of use and treatment satisfaction. CONCLUSION: Interpretation of the results of this open-label study is limited by the lack of a standard wound dressing as a comparator and by the varying types of wounds selected for inclusion. Nevertheless, the results of this study indicate that hyaluronic acid provides good healing of acute wounds and is well accepted by both patients and nurses.

Robust radial face detection for omnidirectional vision.

Bio-inspired and non-conventional vision systems are highly researched topics. Among them, omnidirectional vision systems have demonstrated their ability to significantly improve the geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the picture. In this paper, we investigate what must be taken into account and how to process omnidirectional images provided by the sensor. We focus our research on face detection and highlight the fact that particular attention should be paid to the descriptors in order to successfully perform face detection on omnidirectional images. We demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the adaptation of existing object-detection frameworks, designed for perspective images, should be focused on the choice of appropriate image descriptors in the design of the object-detection pipeline.

A branch-and-bound approach to correspondence and grouping problems.

Data correspondence/grouping under an unknown parametric model is a fundamental topic in computer vision. Finding feature correspondences between two images is probably the most popular application of this research field, and is the main motivation of our work. It is a key ingredient for a wide range of vision tasks, including three-dimensional reconstruction and object recognition. Existing feature correspondence methods are based on either local appearance similarity or global geometric consistency or a combination of both in some heuristic manner. None of these methods is fully satisfactory, especially in the presence of repetitive image textures or mismatches. In this paper, we present a new algorithm that combines the benefits of both appearance-based and geometry-based methods and mathematically guarantees a global optimization. Our algorithm accepts the two sets of features extracted from two images as input, and outputs the feature correspondences with the largest number of inliers, which verify both the appearance similarity and geometric constraints. Specifically, we formulate the problem as a mixed integer program and solve it efficiently by a series of linear programs via a branch-and-bound procedure. We subsequently generalize our framework in the context of data correspondence/grouping under an unknown parametric model and show it can be applied to certain classes of computer vision problems. Our algorithm has been validated successfully on synthesized data and challenging real images.