Synthesizing accurate hands-object interactions (HOI) is critical for applications in Computer Vision, Augmented Reality (AR), and Mixed Reality (MR). Despite recent ad-vances, the accuracy of reconstructed or generated HOI leaves room for refinement. Some techniques have improved the accuracy of dense correspondences by shifting focus from generating explicit contacts to using rich HOI fields. Still, they lack full differentiability or continuity and are tai-lored to specific tasks. In contrast, we present a Coarse Hand-Object Interaction Representation (CHOIR), a novel, versatile and fully differentiable field for HOI modelling. CHOIR leverages discrete unsigned distances for continu-ous shape and pose encoding, alongside multivariate Gaus-sian distributions to represent dense contact maps with few parameters. To demonstrate the versatility of CHOIR we design JointDiffus ion, a diffusion model to learn a grasp distribution conditioned on noisy hand-object interactions or only object geometries, for both refinement and synthe-sis applications. We demonstrate JointDiffus ion, s improve-ments over the SOTA in both applications: it increases the contact F1 score by 5% for refinement and decreases the sim. displacement by 46% for synthesis. Our exper-iments show that JointDiffusion with CHOIR yield supe-rior contact accuracy and physical realism compared to SOTA methods designed for specific tasks. Project page: https://theomorales.com/CHOIR

Affective computing has an unprecedented potential to change the way humans interact with technology. While the last decades have witnessed vast progress in the field, multimodal affective computing systems are generally black box by design. As affective systems start to be deployed in real-world scenarios, such as education or healthcare, a shift of focus toward improved transparency and interpretability is needed. In this context, how do we explain the output of affective computing models? and how to do so without limiting predictive performance? In this article, we review affective computing work from an explainable AI (XAI) perspective, collecting and synthesizing relevant papers into three major XAI approaches: premodel (applied before training), in-model (applied during training), and postmodel (applied after training). We present and discuss the most fundamental challenges in the field, namely, how to relate explanations back to multimodal and time-dependent data, how to integrate context and inductive biases into explanations using mechanisms such as attention, generative modeling, or graph-based methods, and how to capture intramodal and cross-modal interactions in post hoc explanations. While explainable affective computing is still nascent, existing methods are promising, contributing not only toward improved transparency but, in many cases, surpassing state-of-the-art results. Based on these findings, we explore directions for future research and discuss the importance of data-driven XAI and explanation goals, and explainee needs definition, as well as causability or the extent to which a given method leads to human understanding.

As the population ages, Ambient-Assisted Living (AAL) environments are increasingly used to support older individuals’ safety and autonomy. In this study, we propose a low-cost, privacy-preserving sensor system integrated with mobile robots to enhance fall detection in AAL environments. We utilized the Luxonis OAK-D Edge-AI camera mounted on a mobile robot to detect fallen individuals. The system was trained using YOLOv6 network on the E-FPDS dataset and optimized with a knowledge distillation approach onto the more compact YOLOv5 network, which was deployed on the camera. We evaluated the system’s performance using a custom dataset captured with a robot-mounted camera. We achieved a precision of 96.52%, a recall of 95.10%, and a recognition rate of 15 frames per second. The proposed system enhances the safety and autonomy of older individuals by enabling the rapid detection and response to falls.

We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.

We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.

Hand washing is a critical activity in preventing the spread of infection in \nhealth-care environments. Several guidelines recommended a hand washing \nprotocol consisting of six steps that ensure that all areas of the hands are \nthoroughly cleaned. In this paper, we describe a novel approach that uses a \ncomputer vision system to monitor the user?s hands motions in order to ensure \nthat the hand washing guidelines are followed. This work presents two \nmain contributions: a description of a system which delivers robust segmentation \nof the hands using a combination of colour and motion analysis, and \nthe implementation of a multi-class classification of the hand gestures using \na SVM ensemble. The system performance is analysed and compared with \nhuman performance, showing an accuracy close to that of human experts.

Guido is a healthcare robot that serves as support and navigation aid for the frail and visually impaired. The previous assistive navigation software has been replaced by a full map based control, including real time feature based map building (SLAM) and pose tracking (both using an EKF), and path planning and collision avoidance. Real experiments have been carried out, both in the lab and in the hospital, with great success, showing a high improvement in the ease of use and convenience of the system. The map building and editing tools have proved to be extremely useful, enabling the system to be installed in a new environment in just minutes.

Presents a method to detect objects in images using colour. The system learns by example how characteristic each discrete colour is of a class of objects using two sets of training images; one set is of images known to contain at least one target object, while the other set is of images known to be free of the target objects. Using the training images a lookup table is constructed, in which each discrete colour is assigned a value which approximates the probability that pixels with this discrete colour are part of a target object. This table is called the colour profile. To detect the presence of target objects in a new image the colour profile values are back projected onto the image and the image is thresholded. A suitable threshold is calculated automatically and the system is able to recognize if the target objects are not able to be distinguished by the colour profile method. Applying the colour profiling approach to a difficult inspection task has shown it to be a robust, accurate and fast solution.

This paper describes the design of a smart mobility aid for frail, visually impaired people. The device is based on the concept of a walker or rollator-a walking frame with wheels. This work is motivated by the fact the frail visually impaired have extreme difficulty using conventional mobility aids such as guide dogs or long canes. The device, which is called PAM-AID (Personal Adaptive Mobility Aid) has two modes of operation, manual and assistive. In manual mode the device behaves very much like a normal walker and, in addition, provides the user with information on the environment via a speech interface. In assistive mode, the PAM-AID assumes control of the steering and navigates safely around obstacles. The PAM-AID was evaluated in residential homes for the elderly.

This paper presents an original method using colour histograms for flaw detection in automated industrial inspection. The colour histogram of an image is constructed by mapping the pixels into a colour space composed of discrete 3-D colour intervals called colour bins. Swain [9] demonstrated the use of colour histograms to locate a target object in an image. The colour histograms of the target object and of the image are used to create a ratio histogram. The ratio histogram is back projected onto the image by replacing each pixel in the image by the value of the ratio histogram bin into which it falls. The resulting image is smoothed to reduce the effects of noise. The peak is found and taken to be the location of the target object. The method presented in this paper also uses histogram back projection; its novelty is in the construction of the colour profile, which is back projected in the place of the ratio histogram. The colour profile is the result of presenting the system with a set of training images; some known to contain flaws some without flaws. The colour profile is constructed by comparing the colour histograms of the images of flawed components to those of non-flawed components. This results in an array of weights, proportional to the frequency of each discrete colour occurring in flawed versus non-flawed images. The correct choice of colour space and the quantisation of the colour space are very important. These issues are thoroughly explored in this paper and a solution is presented which maximises both accuracy and speed, by using the RGB and HSV colour spaces in the training phase, and combining them into a single RGB based profile for real-time flaw detection. Using colour profiles has the following advantages: they are accurate, real time, and very importantly can be trained by the end user. Colour profiling could be applied successfully to other automated inspection problems. 1