Service Robots: Trends and Technology Gonzalez-Aguirre, Juan Angel; Osorio-Oliveros, Ricardo; Rodríguez-Hernández, Karen L. ...
Applied sciences,
11/2021, Letnik:
11, Številka:
22
Journal Article
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The 2021 sales volume in the market of service robots is attractive. Expert reports from the International Federation of Robotics confirm 27 billion USD in total market share. Moreover, the number of ...new startups with the denomination of service robots nowadays constitutes 29% of the total amount of robotic companies recorded in the United States. Those data, among other similar figures, remark the need for formal development in the service robots area, including knowledge transfer and literature reviews. Furthermore, the COVID-19 spread accelerated business units and some research groups to invest time and effort into the field of service robotics. Therefore, this research work intends to contribute to the formalization of service robots as an area of robotics, presenting a systematic review of scientific literature. First, a definition of service robots according to fundamental ontology is provided, followed by a detailed review covering technological applications; state-of-the-art, commercial technology; and application cases indexed on the consulted databases.
Within the field of Humanities, there is a recognized need for educational innovation, as there are currently no reported tools available that enable individuals to interact with their environment to ...create an enhanced learning experience in the humanities (e.g., immersive spaces). This project proposes a solution to address this gap by integrating technology and promoting the development of teaching methodologies in the humanities, specifically by incorporating emotional monitoring during the learning process of humanistic context inside an immersive space. In order to achieve this goal, a real-time emotion recognition EEG-based system was developed to interpret and classify specific emotions. These emotions aligned with the early proposal by Descartes (Passions), including admiration, love, hate, desire, joy, and sadness. This system aims to integrate emotional data into the Neurohumanities Lab interactive platform, creating a comprehensive and immersive learning environment. This work developed a ML, real-time emotion recognition model that provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds. Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective best band powers were extracted; furthermore, multiple models were evaluated using shift-based data division and cross-validations. After assessing their performance, Extra-Trees achieved a general accuracy of 94%, higher than the reported in the literature (88% accuracy). The proposed model provided real-time predictions of VAD variables and was adapted to classify Descartes' six main passions. However, with the VAD values obtained, more than 15 emotions can be classified (reported in the VAD emotion mapping) and extend the range of this application.
Cities demand urgent transformations in order to become more affordable, livable, sustainable, walkable and comfortable spaces. Hence, important changes have to be made in the way cities are ...understood, diagnosed and planned. The current paper puts urban accessibility into the centre of the public policy and planning agenda, as a transferable approach to transform cities into better living environments. To do so, a practical example of the City of Monterrey, Mexico, is presented at two planning scales: the metropolitan and local level. Both scales of analysis measure accessibility to main destinations using walking and cycling as the main transport modes. The results demonstrate that the levels of accessibility at the metropolitan level are divergent, depending on the desired destination, as well as on the planning processes (both formal and informal) from different areas of the city. At the local level, the Distrito Tec Area is diagnosed in terms of accessibility to assess to what extent it can be considered a part of a 15 minutes city. The results show that Distrito Tec lacks the desired parameters of accessibility to all destinations for being a 15 minutes city. Nevertheless, there is a considerable increase in accessibility levels when cycling is used as the main travelling mode. The current research project serves as an initial approach to understand the accessibility challenges of the city at different planning levels, by proving useful and disaggregated data. Finally, it concludes providing general recommendations to be considered in planning processes aimed to improve accessibility and sustainability.
Urban planning has a crucial role in helping cities meet the United Nations’ Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. ...These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community’s urban region based on their accessibility performance.
Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the ...possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions (Fx, Fy, Fz), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding Fz it reached 0.92.
In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional ...process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which results in a tiresome process for simple tasks such as pick and replace an item from a surface. Here, we take a semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and then the user only has to decide on the start of the movement and the final position of the effector (goal-selection approach). Under these conditions, we implemented pick-and-place tasks with a disk as an item and two target areas placed on the table at arbitrary positions. An artificial vision (AV) algorithm was used to obtain the positions of the items expressed in the robot frame through images captured with a webcam. Then, the AV algorithm is integrated into the inverse kinematics model to perform the manipulation tasks. As proof-of-concept, different users were trained to control the pick-and-place tasks through the process-control and semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.
A Challenge based Learning (CBL) activity, with the participation of an industry partner, regarding automotive design for undergraduate engineering students is presented. During an academic semester, ...senior students in an specialization course became familiar with technical aspects regarding electric vehicles (EV) and learned about battery pack sizing and motor sizing as well. Students worked on a challenging case study to convert a VW Type 1 sedan with an internal combustion engine (ICE) into an EV. The work done by the students involved real-data, simulations, cost analysis, 3D scanning, multiple CAD design iterations, and manufacturing processes. The achieved outcomes of the case were: an improved dynamic behavior and more sym-metrical weight distribution compared with the original vehicle, as well as a comparable power and energy delivery were also achieved at a lower cost compared with commercial conversion solutions. These results are evidence of the skills developed by the students as the CBL activity was developed and shows a promising project option for engineering students. It is observed how challenging students through a motivating situation that demands their intellectual curiosity, seeking to solve problems of global interest, and focusing on an application in their area of expertise, contributes to their professional development. This is an option for future final capstone projects in engineering.
The integration of new technologies and data analysis methods ushered in innovative teaching practices that depart from conventional methods. The advent of information technologies and the rapid ...adoption of dynamic teaching tools have transformed classrooms into interactive spaces where students and educators engage in real-time feedback and collaborative learning experiences. This study employs electroencephalography (EEG) and machine learning to evaluate the effectiveness of various teaching modalities on students' cognitive performance. EEG, a non-invasive technique that measures brain activity through electrical signals, offers insights into cognitive processes by analyzing event-related potentials (ERPs) and frequency-domain features. The Power Spectral Density (PSD) and Fast Fourier Transform (FFT) techniques are utilized to analyze EEG signals and identify patterns of brain activity in response to different learning environments. The study encompasses EEG data acquisition, pre-processing, and feature extraction, particularly focusing on the Task Engagement Index (TEI) as a measure of cognitive workload and engagement. Employing machine learning algorithms, such as the Multilayer Perceptron (MLP), the proposed algorithm is able to predict students' cognitive performance based on EEG features, categorized in three classes of increasing performance (score). Frequency domain analysis reveals distinct patterns of brain activity within different learning contexts, while the model evaluation underscores the efficacy of the MLP algorithm in predicting cognitive performance; and correlation analysis highlights specific EEG features that significantly influence cognitive performance. The analysis included frequency domain assessment, revealing distinct power patterns between text and video learning groups. MLP algorithm outperformed other models in accuracy, precision, recall, and F1 score evaluations. Statistical analysis highlighted correlations between EEG features and cognitive performance. Results showed similar performance for Text (around 80%) and Video (around 86%) (three-class) predictive models. Confusion matrices confirmed accurate classification. Moreover, models were tested with twenty random samples from each group, obtaining 90% and 95% accuracies for Video and Text respectively. In addition, The study identified cognitive load variations, showcased MLP's effectiveness, and emphasized EEG feature correlations in predicting cognitive performance.
For athletes, coaches, or rehabilitation patients, the systems currently used to perform biomechanical studies and the dependence on technical experts for interpreting analyses and results can limit ...organizational, logistical, and economic resources. In this project, a Recurrent Neural Network model was created to predict human joint accelerations through the automatic digitalization of human body movement using video and acceleration sensors. The project aimed to prevent injuries and fractures in athletes and the elderly population because there is a lack of tools that predicts the risk of these traumas as a preventive method. Acceleration data was collected using Matlab mobile installed in cell phones attached to the arms and legs of volunteers doing physical tasks (walking, running, jumping). Experiments were video recorded, and machine learning models were trained using acceleration and video using Python libraries. After model evaluation, we observed that the selected model could predict the best on the XY axes and the worst on the Z axis, probably due to predicting a three-dimensional feature with a two-dimensional input. A biomechanical Digital Twin was created by combining the information from wearable devices, computer vision, and machine learning algorithms. This tool was able to estimate human joint accelerations (up to an extent) during movements with more refinement; it can help to evaluate movement performance within exercises or tasks and aid in injury/fracture risk prediction.
This work presents a real-time biofeedback tool that employs wearables and the Internet of Things with educational applications to improve students' learning and retention. We aimed to create a web ...platform using the Internet of Things (IoT) and Machine Learning (ML) architecture to predict students' performance, analyze mental fatigue, and provide real-time quantitative biofeedback to identify the best learning modality. Thus, the main goal was to develop a system that allows students to learn and improve their projects. We integrated the analysis of real-time biometric signals, machine learning algorithms, and web services as we observed their behavior under different learning modalities, seeking to improve cognitive performance. For this, 23 volunteers filled out the ten-question Fatigue Assessment Scale questionnaire about mental fatigue, validated with the P300 waves acquired during auditory-oddball (AO) tests. Synchronized data acquisition was achieved using Enophones and an E4 wristband. To develop predictive models, we collected the biometric data and incorporated it into an ML algorithm to visualize students' performance in real time. The system can accommodate other wearable systems with new features in further experiments. Thus, we believe this current development has the potential to further revolutionize traditional teaching with this methodology and future enhancements.
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