1,720,976 research outputs found
Affect expression in social robots: Combining non-verbal affect expression techniques
No full textWith the advent of social robots which are designed to be 'social', human-like interactions have become a necessity. It is natural for us to use a plethora of emotions to convey additional information or to make an interaction more engaging. But emotion expression is not commonly associated with robots. Many humanoid robots cannot generate facial expressions to portray various emotions. Studies have shown that robots are multi-modal systems which can employ multiple channels to express an emotion. This thesis explores the non-verbal emotion expression techniques and their expressive capabilities. We found that some emotions are easier to express than others, and a single technique cannot express all the emotions. We chose a few emotions and systematically determined the best technique for each of them
Socially interactive agents for robotic neurorehabilitation training: conceptualization and proof-of-concept study
Full text linkIntroductionIndividuals with diverse motor abilities often benefit from intensive and specialized rehabilitation therapies aimed at enhancing their functional recovery. Nevertheless, the challenge lies in the restricted availability of neurorehabilitation professionals, hindering the effective delivery of the necessary level of care. Robotic devices hold great potential in reducing the dependence on medical personnel during therapy but, at the same time, they generally lack the crucial human interaction and motivation that traditional in-person sessions provide.MethodsTo bridge this gap, we introduce an AI-based system aimed at delivering personalized, out-of-hospital assistance during neurorehabilitation training. This system includes a rehabilitation training device, affective signal classification models, training exercises, and a socially interactive agent as the user interface. With the assistance of a professional, the envisioned system is designed to be tailored to accommodate the unique rehabilitation requirements of an individual patient. Conceptually, after a preliminary setup and instruction phase, the patient is equipped to continue their rehabilitation regimen autonomously in the comfort of their home, facilitated by a socially interactive agent functioning as a virtual coaching assistant. Our approach involves the integration of an interactive socially-aware virtual agent into a neurorehabilitation robotic framework, with the primary objective of recreating the social aspects inherent to in-person rehabilitation sessions. We also conducted a feasibility study to test the framework with healthy patients.Results and discussionThe results of our preliminary investigation indicate that participants demonstrated a propensity to adapt to the system. Notably, the presence of the interactive agent during the proposed exercises did not act as a source of distraction; instead, it positively impacted users' engagement
In the face and heart of data scarcity in Industry 5.0: exploring applicability of facial and physiological AI models for operator well-being in human-robot collaboration
Full text linkOver the past decade, research has focused on integrating collaborative robots, or cobots, into assembly lines. The envisioned future industrial workplaces involve close collaboration between human workers and cobots. With the advent of Industry 5.0, human-centered approaches to facilitate human-robot collaboration (HRC) have gained significant traction. These approaches go beyond ensuring physical safety, emphasizing the mental health and well-being of industrial workers. To achieve this goal, cobots have to be equipped with capabilities to detect real-time worker states. Despite various investigations into user states related to well-being in different domains, the manifestations of these states in industrial settings are relatively unexplored. Hence, a critical gap exists in our understanding of whether machine learning models developed for other contexts are applicable to industrial HRC.
Many aspects of existing datasets pose challenges to the applicability of the machine learning models in industrial settings. On the one hand, most datasets for well-being-related states (e.g., pain, distraction) are typically small and lack variation in recording conditions, raising concerns about whether models trained on these datasets learn generic or dataset-specific features. On the other hand, although states like stress are well-researched, there are limited public datasets involving HRC tasks. This limitation is exacerbated by the lack of long-term studies involving industrial HRC tasks, limiting our understanding of worker states (e.g., boredom, flow) that emerge over a long period of familiar and repetitive tasks. These limitations of existing datasets form the motivation for the works presented in this thesis.
This thesis explores applicability through multiple lenses: transferability (leveraging features from a related task), generalizability (ensuring models perform well on multiple datasets), replicability (testing approaches on various datasets and recording conditions), reproducibility (recreating industrial HRC experiences), and versatility (utilizing features/models for multiple tasks). The investigations of this thesis are presented in two parts. The first part addresses transferability, generalizability, and replicability by utilizing transfer learning techniques to train various models and assess them using explainable AI methods and cross-dataset evaluations. The second part addresses reproducibility and versatility by analyzing user studies in simulated industrial HRC scenarios with durations ranging from half an hour to several days. The results of this thesis not only demonstrate approaches to develop models applicable to industrial HRC settings but also identify potential avenues for improvement. These findings form the foundations for developing models that enhance human-robot collaboration in industrial environments by focusing on both efficiency and worker well-being
On the Generalizability of ECG-based Stress Detection Models
Full text linkStress is prevalent in many aspects of everyday life including work,
healthcare, and social interactions. Many works have studied handcrafted
features from various bio-signals that are indicators of stress. Recently, deep
learning models have also been proposed to detect stress. Typically, stress
models are trained and validated on the same dataset, often involving one
stressful scenario. However, it is not practical to collect stress data for
every scenario. So, it is crucial to study the generalizability of these models
and determine to what extent they can be used in other scenarios. In this
paper, we explore the generalization capabilities of Electrocardiogram
(ECG)-based deep learning models and models based on handcrafted ECG features,
i.e., Heart Rate Variability (HRV) features. To this end, we train three HRV
models and two deep learning models that use ECG signals as input. We use ECG
signals from two popular stress datasets - WESAD and SWELL-KW - differing in
terms of stressors and recording devices. First, we evaluate the models using
leave-one-subject-out (LOSO) cross-validation using training and validation
samples from the same dataset. Next, we perform a cross-dataset validation of
the models, that is, LOSO models trained on the WESAD dataset are validated
using SWELL-KW samples and vice versa. While deep learning models achieve the
best results on the same dataset, models based on HRV features considerably
outperform them on data from a different dataset. This trend is observed for
all the models on both datasets. Therefore, HRV models are a better choice for
stress recognition in applications that are different from the dataset
scenario. To the best of our knowledge, this is the first work to compare the
cross-dataset generalizability between ECG-based deep learning models and HRV
models.Comment: Published in Proceedings of 2022 21st IEEE International Conference
on Machine Learning and Applications (ICMLA
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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