Assessment and counseling to get the best efficiency and effectiveness of the assistive technology (MATCH): Study protocol.

AIMS: To determine the psychosocial impact of assistive technology(AT) based on robotics and artificial intelligence in the life of people with disabilities. BACKGROUND: The best match between any person with disabilities and its AT only can be gotten through a complete assessment and monitoring of his/her needs, abilities, priorities, difficulties and limitations. Without this analysis, it's possible that the device won't meet the individual's expectations. Therefore, it is important that any project focused on the development of innovating AT for people with disabilities includes the perspective of outcome measures as an important phase of the research. In this sense, the integration of the assessment, implementation process and outcome measures is crucial to guarantee the transferability for the project findings and to get the perspective from the final user. METHODS: Pilot study, with prospective, longitudinal and analytical cohort. The study lasts from July 2020 until April 2023. The sample is formed by people with disabilities, ages from 2-21, that will participate from the first stage of the process (initial assessment of their abilities and needs) to the final application of outcome measures instruments (with a complete implication during the test of technology). DISCUSSION: Only with the active participation of the person is possible to carry out a user-centered approach. This fact will allow us to define and generate technological solutions that really adjust to the expectations, needs and priorities of the people with disabilities, avoiding the AT from being abandoned, with the consequent health and social spending. TRIAL REGISTRATION: Clinical Trials ID: NCT04723784; https://clinicaltrials.gov/.

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities.

In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders' involvement in the environment during everyday living activities. In order to support the children and youth with both the sequencing and learning of everyday living tasks, our robotic platform incorporates a sophisticated online action detection module that is capable of monitoring the acts performed by users. We explain all the technical details that allow many applications to be introduced to support individuals with functional diversity. We present this work as a proof of concept, which will enable an assessment of the impact that the developed technology may have on the collective of children and youth with neurodevelopmental disorders in the near future.

Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor.

In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results.

Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation.

Training a Convolutional Neural Network (CNN) for semantic segmentation typically requires to collect a large amount of accurate pixel-level annotations, a hard and expensive task. In contrast, simple image tags are easier to gather. With this paper we introduce a novel weakly-supervised semantic segmentation model able to learn from image labels, and just image labels. Our model uses the prior knowledge of a network trained for image recognition, employing these image annotations as an attention mechanism to identify semantic regions in the images. We then present a methodology that builds accurate class-specific segmentation masks from these regions, where neither external objectness nor saliency algorithms are required. We describe how to incorporate this mask generation strategy into a fully end-to-end trainable process where the network jointly learns to classify and segment images. Our experiments on PASCAL VOC 2012 dataset show that exploiting these generated class-specific masks in conjunction with our novel end-to-end learning process outperforms several recent weakly-supervised semantic segmentation methods that use image tags only, and even some models that leverage additional supervision or training data.