Abstract
Object detection in the traffic domain has faced growing relevance through the years in developing autonomous driving mechanisms. As with vehicles, pedestrians face a very dynamic context, and identifying relevant objects from a pedestrian perspective presents many challenges. Improving the detection of some objects, such as crosswalks, is very relevant in this regard. This paper presents a technique that applies a computer vision approach to automatically generate datasets for training YOLO-based deep learning algorithms. An initial precision of 0.82 achieved with the generated dataset, which is increased to 0.84 after manually removing incorrect annotations. Results show that our approach leverages the dataset building process by reducing the manual workload needed. The approach could be used for training other object detection models used in traffic scenarios.

Abstract
Pedestrian behavior is an essential subject of study when developing or enhancing urban infrastructure. However, most behavior elicitation techniques are inherently bound to be biased by either the observer, the subject, or the environment. The SIMUSAFE project aims at collecting road users’ behavioral data in naturalistic and realistic scenarios to produce more accurate decision-making models. Using video captured from a monocular camera worn by a pedestrian, we employ machine learning and computer vision techniques to identify areas of interest surrounding a pedestrian. Namely, we use object detection and depth estimation to generate a map of obstacles that may influence the pedestrian’s actions. Our methods have shown to be successful in detecting free and occupied areas from monocular video.

Abstract
This article addresses the use of augmented reality content as a part of the point of sale promotion strategy. The main research question is to determine whether the use of an application with augmented reality can facilitate the visualization and localization of the promotions available at the physical point of sale and, consequently, influence the shopping experience. To answer this question, an application was created with augmented reality content, so that it can measure the influence that this technology can have on the perception of promotions within stores. The general objective of the study is to identify whether the use of this application, in the context of supermarkets, could facilitate the visualization and localization of promotions in comparison with conventional disclosure approaches. Based on a literature review with authors from different areas, this article summarizes an ongoing investigation and at the end details the future steps of the research.

Abstract

Abstract
The human hand is the body part most frequently injured in occupational accidents, accounting for one out of five emergency cases and often requiring surgery with subsequently long periods of rehabilitation. This paper proposes a Virtual Reality game to improve conventional physiotherapy in hand rehabilitation, focusing on resolving recurring limitations reported in most technological solutions to the problem, namely the limited diversity support of movements and exercises, complicated calibrations and exclusion of patients with open wounds or other disfigurements of the hand. The system was assessed by seven able-bodied participants using a semistructured interview targeting three evaluation categories: hardware usability, software usability and suggestions for improvement. A System Usability Score (SUS) of 84.3 and participants' disposition to play the game confirm the potential of both the conceptual and technological approaches taken for the improvement of hand rehabilitation therapy. © 2020 IEEE.

Abstract
Background: The human hand is the part of the body most frequently injured in work related accidents, accounting for a third of all accidents at work and often involving surgery and long periods of rehabilitation. Several applications of Augmented Reality (AR) and Virtual Reality (VR) have been used to improve the rehabilitation process. However, there is no sound evidence about the effectiveness of such applications nor the main drivers of therapeutic success. Objectives: The objective of this study was to review the efficacy of AR and VR interventions for hand rehabilitation. Methods: A systematic search of publications was conducted in October 2019 in IEEE Xplore, Web of Science, Cochrane library, and PubMed databases. Search terms were: (1) video game or videogame, (2) hand, (3) rehabilitation or therapy and (4) VR or AR. Articles were included if (1) were written in English, (2) were about VR or AR applications, (3) were for hand rehabilitation, (4) the intervention had tests on at least ten patients with injuries or diseases which affected hand function and (5) the intervention had baseline or intergroup comparisons (AR or VR intervention group versus conventional physical therapy group). PRISMA protocol guidelines were followed to filter and assess the articles. Results: From the eight selected works, six showed improvements in the intervention group, and two no statistical differences between groups. We were able to identify motivators of patients’ adherence, namely real-time feedback to the patients, challenge, and increased individualized difficulty. Automated tracking, easy integration in the home setting and the recording of accurate metrics may increase the scalability and facilitate healthcare professionals’ assessments. Conclusions: This systematic review provided advantages and drivers for the success of AR/VR application for hand rehabilitation. The available evidence suggests that patients can benefit from the use of AR or VR interventions for hand rehabilitation. © 2020 Elsevier Inc.