Assistive Technology for Elderly Care: An Overview

Global demographic changes have resulted in a growing technological demand to meet the arisen social needs. In particular, the increasingly ageing population requires assistive technologies to stay at home for longer independently while receiving continuous healthcare. In this sense, a wide academic and industrial research is taking place, introducing these technologies in hospitals and rehabilitation centres. This paper aims at providing an overview of research projects for elderly care and assistance, focusing on cognitive and robot assistants due to their popularity in the area. More precisely, physical and/or cognitive rehabilitation are presented. This paper also discusses their limitations and the open challenges to be tackled in order to be successfully integrated in our society

Robot Vision for Manipulation: A Trip to Real-World Applications

Along the last decades, Robotics research has taken a major turn from laboratories to factories and ordinary real-world environments. Consequently, new issues to be overcome have arisen, specially when autonomous, dexterous robots are in place. In this paper, we present this evolution in the case of robot vision for manipulation through several robot developments, by analysing their challenges and proposed solutions. This overview highlights the need of using different techniques depending on the task at hand and the scenario to work in.This work was supported in part by the Ministerio de Economía y Competitividad under Grant DPI2015-69041-R, in part by Universitat Jaume I under Grant UJI-B2018-74, and in part by Generalitat Valenciana under Grant PROMETEO/2020/034 and GV/2020/051

Socially Assistive Robots for Older Adults and People with Autism: An Overview

Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.This research was funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

Towards a Better Performance in Facial Expression Recognition: A Data-Centric Approach

Facial expression is the best evidence of our emotions. Its automatic detection and recognition are key for robotics, medicine, healthcare, education, psychology, sociology, marketing, security, entertainment, and many other areas. Experiments in the lab environments achieve high performance. However, in real-world scenarios, it is challenging. Deep learning techniques based on convolutional neural networks (CNNs) have shown great potential. Most of the research is exclusively model-centric, searching for better algorithms to improve recognition. However, progress is insufficient. Despite being the main resource for automatic learning, few works focus on improving the quality of datasets. We propose a novel data-centric method to tackle misclassification, a problem commonly encountered in facial image datasets. The strategy is to progressively refine the dataset by successive training of a CNN model that is fixed. Each training uses the facial images corresponding to the correct predictions of the previous training, allowing the model to capture more distinctive features of each class of facial expression. After the last training, the model performs automatic reclassification of the whole dataset. Unlike other similar work, our method avoids modifying, deleting, or augmenting facial images. Experimental results on three representative datasets proved the effectiveness of the proposed method, improving the validation accuracy by 20.45%, 14.47%, and 39.66%, for FER2013, NHFI, and AffectNet, respectively. The recognition rates on the reclassified versions of these datasets are 86.71%, 70.44%, and 89.17% and become state-of-the-art performance.This work was funded by grant CIPROM/2021/17 awarded by the Prometeo program from Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital of Generalitat Valenciana (Spain), and partially funded by the grant awarded by the Central University of Ecuador through budget certification no. 34 of March 25, 2022, for the development of the research project with code: DOCT-DI-2020-37

A Large Visual, Qualitative, and Quantitative Dataset for Web Intelligence Applications

The Web is the communication platform and source of information par excellence. The volume and complexity of its content have grown enormously, with organizing, retrieving, and cleaning Web information becoming a challenge for traditional techniques. Web intelligence is a novel research area to improve Web-based services and applications using artificial intelligence and automatic learning algorithms, for which a large amount of Web-related data are essential. Current datasets are, however, limited and do not combine visual representation and attributes of Web pages. Our work provides a large dataset of 49,438 Web pages, composed of webshots, along with qualitative and quantitative attributes. This dataset covers all the countries in the world and a wide range of topics, such as art, entertainment, economics, business, education, government, news, media, science, and the environment, addressing different cultural characteristics and varied design preferences. We use this dataset to develop three Web Intelligence applications: knowledge extraction on Web design using statistical analysis, recognition of error Web pages using a customized convolutional neural network (CNN) to eliminate invalid pages, and Web categorization based solely on screenshots using a CNN with transfer learning to assist search engines, indexers, and Web directories.This work has been funded by the grant awarded by the Central University of Ecuador through budget certification No. 34 of March 25, 2022 for the development of the research project with code: DOCT-DI-2020-37

Machine Learning Techniques for Assistive Robotics

Assistive robots are a category of robots that share their area of work and interact with humans [...

Rehabilitation Technology: Assistance from Hospital to Home

Rehabilitation is essential for disabled people to achieve the highest level of functional independence, reducing or preventing impairments. Nonetheless, this process can be long and expensive. This fact together with the ageing phenomenon has become a critical issue for both clinicians and patients. In this sense, technological solutions may be beneficial since they reduce the costs and increase the number of patients per caregiver, which makes them more accessible. In addition, they provide access to rehabilitation services for those facing physical, financial, and/or attitudinal barriers. This paper presents the state of the art of the assistive rehabilitation technologies for different recovery methods starting from in-person sessions to complementary at-home activities.This work was supported by the Spanish Government TIN2016-76515-R Grant, supported with FEDER funds

A Socially Assistive Robot for Elderly Exercise Promotion

The population ageing phenomenon leads to an unceasing need for home-based healthcare systems to continuously monitor the elderly’s cognitive and physical health. In this sense, physical activity may be beneficial in preserving cognition in elder life as well as in providing clinicians and therapists with the indicative of elderly’s health condition. Nevertheless, current systems fail to promote and monitor the elderly’s physical activity in their living environments. This paper is aimed at providing a socially assistive robot solution for this task. Since robot acceptance depends to a great extent on its robustness in performing tasks, we have focused on exercise recognition due to its great importance for both clinicians and elderly. For that, two different tasks were carried out. First, an image dataset for physical exercise recognition has been generated. Then, a comparative analysis of several deep learning techniques is presented. This paper reveals a great performance in the exercise recognition of CNN-LSTM with an exercise recognition accuracy of 99.87%.This work was supported in part by the Spanish Government under Grant TIN2016-76515-R, and in part by Feder Funds

The UJI Aerial Librarian Robot: A Quadcopter for Visual Library Inventory and Book Localisation

Over time, the field of robotics has provided solutions to automate routine tasks in different scenarios. In particular, libraries are awakening great interest in automated tasks since they are semi-structured environments where machines coexist with humans and several repetitive operations could be automatically performed. In addition, multirotor aerial vehicles have become very popular in many applications over the past decade, however autonomous flight in confined spaces still presents a number of challenges and the use of small drones has not been reported as an automated inventory device within libraries. This paper presents the UJI aerial librarian robot that leverages computer vision techniques to autonomously self-localize and navigate in a library for automated inventory and book localization. A control strategy to navigate along the library bookcases is presented by using visual markers for self-localization during a visual inspection of bookshelves. An image-based book recognition technique is described that combines computer vision techniques to detect the tags on the book spines, followed by an optical character recognizer (OCR) to convert the book code on the tags into text. These data can be used for library inventory. Misplaced books can be automatically detected, and a particular book can be located within the library. Our quadrotor robot was tested in a real library with promising results. The problems encountered and limitation of the system are discussed, along with its relation to similar applications, such as automated inventory in warehouses.Support for the research conducted at UJI Robotic Intelligence Laboratory is provided in part by the Ministerio de Economía y Competitividad (DPI2015-69041-R), by Universitat Jaume I (UJI-B2018-74), and by Generalitat Valenciana (PROMETEO/2020/034, GV/2020/051)

EVA: EVAluating at-home rehabilitation exercises using augmented reality and low-cost sensors

Over one billion people in the world live with some form of disability. This is incessantly increasing due to aging population and chronic diseases. Among the emerging social needs, rehabilitation services are the most required. However, they are scarce and expensive what considerably limits access to them. In this paper, we propose EVA, an augmented reality platform to engage and supervise rehabilitation sessions at home using low-cost sensors. It also stores the user’s statistics and allows therapists to tailor the exercise programs according to their performance. This system has been evaluated in both qualitative and quantitative ways obtaining very promising results.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. Edmanuel Cruz is funded by a Panamenian grant for Ph.D. studies IFARHU and SENACYT 270-2016-207. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243 and FPU16/00887. Thanks also to Nvidia for the generous donation of a Titan Xp and a Quadro P6000