Abstract
Healthcare centers are ideal settings to identify and motivate parents to adhere to healthier lifestyles in order to reverse their child's excessive weight. However, such promotions are time- and resource-consuming, and primary health care needs new approaches to engage parents. In this article, we present an app for that purpose, with tailored recommendations for parents regarding young children's lifestyles (eating, drinking, moving, and sleeping habits) with gamification mechanics for parents, and a serious game for their children, aged 3-6 years. Healthcare center based questionnaires were used to assess parent's acceptance of the app. We also determined their enrollment and retention rates with a pilot study, in order to test the implementation of a randomized controlled trial (RCT) in the future (pilot trial registration: ClinicalTrials.gov NCT03881280). In the pilot study, we tested their engagement with the app during a four-week period through a monitoring website. In the acceptance test (n = 13), parents rated the app a median score of seven on a ten-point scale. In the pilot study (n = 21), all parents in the intervention group used the app. The retention rate was 71.4%. This study indicated some areas of improvement related to gaming mechanics in order to increase participation. Healthcare center's professionals and parents of children with overweight/obesity accepted this innovative approach. In addition, it is feasible to test its impact on children's lifestyle by conducting an RCT in the future.

Abstract
Childhood obesity is associated with unbalanced lifestyle patterns, and new strategies are needed to support parents in the compliance with the guidelines for children's age. Tailored automatic recommendations mimic interpersonal counseling and are promising strategies to be considered for health promotion programs. This study aimed to develop and test a mobile recommendation system for parents of preschool children identified with overweight/obesity at health care centers. Evidence-based recommendations related to children's eating, drinking, moving, and sleeping habits were developed and tested using a questionnaire. A pilot study was conducted in a health care center to test how using an app with those tailored recommendations, in video format, influenced parents' perceptions of the child's weight status and their knowledge about the guidelines, compared to a control group. The chi-squared test was used for categorical variables and the Mann-Whitney U test for continuous variables (p< 0.05). A high proportion of parents were already informed about the guidelines, but their children were not meeting them. After watching the tailored recommendations, there was an increased knowledge of the guideline on water intake, but there was no improvement in the perception of the child's excessive weight. Parents may benefit from a mobile-based tailored recommendation system to improve their knowledge about the guidelines. However, there is a need to work with parents on motivation to manage the child's weight with additional strategies.

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. © 2020, Springer Nature Switzerland AG.

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. © 2020, Springer Nature Switzerland AG.

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
The fourth author name was missed in the original publication. The correct list of authors should read as "Hugo Fernandes, Paulo Costa, Vitor Filipe, Hugo Paredes, Joao Barroso". It has been corrected in this erratum. The original article has been updated.