979 research outputs found

    Dynamic Adaptive System for Robot-Assisted Motion Rehabilitation

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    This paper presents a dynamic adaptive system for administration of robot-assisted therapy. The main novelty of the proposed approach is to close patient in the loop and use multisensory data (such as motion, forces, voice, muscle activity, heart rate, and skin conductance) to adaptively and dynamically change the complexity of the therapy and real-time displays of an immersive virtual reality system in accordance with specific patient requirements. The proposed rehabilitation system can be considered as a complex system that is composed of the following subsystems: data acquisition, multimodal human–machine interface, and adaptable control system. This paper shows the description of the developed fuzzy controller used as the core of the adaptable control subsystem. Finally, experimental results with ten subjects are reported to show the performance of the proposed solution

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Applying machine learning: a multi-role perspective

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    Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference

    How Does Sense of Agency Develop Across Adolescence?

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    A sense of agency (SoA) refers to an individual’s awareness of their control over their voluntary actions and the sensory consequences of those actions. Experiencing a veridical SoA is imperative to basic functioning as it facilitates effective goal-directed action. Despite this, a consensus on the trajectory at which the capacity to experience a SoA develops from childhood to adulthood has remained absent from past literature. To resolve this issue, SoA development was investigated by evaluating the influence of age on the functional efficiency of the forward model; the cognitive framework believed to generate a SoA. More specifically, the current research examined the extent to which children, adolescents and adults could, i) accurately predict the outcome of their action, and ii) update their action-outcome knowledge following post-action feedback; two skills indicative of a precise forward model. A synchronisation-continuation task (chapter 3) was used to assess the impact of age on both the capacity to form veridical action-outcome predictions and update action-outcome knowledge in children, adolescents and adults. To isolate the effect of age on action-outcome prediction, a cued reaction time task (chapter 4) and a goal-switching task (chapter 5) were also administered to children, adolescents, and adults. Likewise, an outcome learning task (chapter 6) was used to assess how post-action learning changes from adolescence to adulthood. It was revealed that the frequency at which individuals engage in action-outcome prediction (chapter 4) and the quality of those predictions (chapters 3 and 5) improves with age. Similarly, the accuracy (chapter 3) and magnitude (chapter 6) to which individuals can update action-outcome knowledge in response to feedback was also found to refine with age. Moreover, the results of this thesis extend prior knowledge by suggesting that forward model precision, and thereby, the capacity to experience a SoA, develops with age across childhood, adolescence, and young adulthood

    Cognitive Decay And Memory Recall During Long Duration Spaceflight

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    This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors

    Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

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    [Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

    Relações entre características do autismo, variáveis emocionais e o processamento olfativo na população geral

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    Although altered sensory processing is recognized as a key-feature of Autism Spectrum Disorder (henceforth “autism”), olfactory functioning is still poorly understood in this condition. Considering the role of olfaction in human social communication and well-being, it is crucial to investigate which variables are related to the often-observed inconsistent results concerning olfactory functioning in autism. Study of the expression of autism traits and other autism-related variables in the general population may be useful to understand which specific dimensions are related to the often-observed symptoms, alterations, and heterogeneity in the autism spectrum, including in the olfactory domain. The present work sought to contribute to the multidimensional assessment of anxiety and autism traits in adults of the general population, as well as to the understanding of the multivariate relationships between autism characteristics, olfactory processing, anxiety, and alexithymia. Study 1 and Study 2 aimed to extend the available evidence about the psychometric properties of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) and the Autism Spectrum Quotient (AQ). Results supported the adequacy of both instruments to measure anxiety and autism traits, respectively, in a multidimensional perspective. Consistent with the literature, Study 1 found support for a four-factor, as well as a two-factor structure within the state and traits forms of the STICSA. Moreover, measurement invariance across sex groups, and good nomological validity were also supported for the STICSA. Results also suggested that the cognitive and somatic dimensions of trait anxiety, as measured by the STICSA, are differently related with the subjective and psychophysiological responses in distinct emotional contexts. Results of Study 2 further supported a three-factor structure of the AQ, consistent with previous studies, as well as the role of alexithymia, particularly difficulties in identifying feelings, as a mediator of the relationship between autism traits and trait anxiety. Study 3 analyzed the impact of the social skills and attention to detail dimensions of autism traits, and cognitive/somatic trait anxiety, on the olfactory abilities of the general population. Results emphasized the roles of sex, attention to detail and trait-somatic anxiety as significant predictors of odor discrimination abilities. Finally, Study 4 provided an integrative review about olfactory processing in autism and how advancing research in this area may benefit the knowledge and practice regarding social cognition and behavior in autism. The findings of this research highlight the need to explore the distinct dimensions of autism-related variables to better understand their complex relationships and impact in the functioning of the spectrum, including in olfactory functioning.Embora alterações no processamento sensorial sejam uma característica-chave da Perturbação do Espetro do Autismo (daqui em diante “autismo”), o funcionamento olfativo ainda é pouco compreendido nesta condição. Considerando o papel do olfato na comunicação, interação social e bem-estar, é crucial investigar que variáveis estão relacionadas com os resultados inconsistentes frequentemente observados no âmbito do processamento olfativo no autismo. Estudar a expressão de traços de autismo na população geral, bem como a expressão multidimensional de outras variáveis relacionadas, pode ser útil para compreender que dimensões estão relacionadas com os sintomas, alterações e heterogeneidade frequentemente observados no autismo, incluindo no domínio olfativo. O presente trabalho pretendeu contribuir para a avaliação multidimensional da ansiedade e de traços de autismo em adultos da população geral, bem como para uma melhor compreensão da relação multivariada entre as características do autismo, processamento olfativo, ansiedade e alexitimia. O Estudo 1 e o Estudo 2 tiveram como objetivo estender a evidência disponível sobre as propriedades psicométricas do State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) e do Autism Spectrum Quotient (AQ). Os resultados suportaram a adequação de ambos os instrumentos para medir ansiedade e traços de autismo, respetivamente, numa perspetiva multidimensional. Em linha com a literatura, o Estudo 1 providenciou suporte para uma estrutura de quatro fatores, bem como para uma estrutura de dois fatores dentro das dimensões de ansiedade traço e estado do STICSA. Observou-se ainda invariância fatorial considerando a variável sexo, assim como boa validade nomológica. Os resultados também sugeriram que as dimensões cognitivas e somáticas da ansiedade traço, medidas pelo STICSA, estão relacionadas de forma distinta com as respostas subjetiva e psicofisiológica em diferentes contextos emocionais. Os resultados do Estudo 2, de modo consistente com estudos anteriores, suportaram uma estrutura de três fatores do AQ, bem como o papel da alexitimia, particularmente das dificuldades em identificar sentimentos e emoções, como mediadora da relação entre traços de autismo e ansiedade traço. O Estudo 3 analisou o impacto das dimensões de traços de autismo relacionadas com as capacidades sociais e atenção para os detalhes, e da ansiedade traço cognitiva/somática, nas capacidades olfativas da população geral. Os resultados evidenciaram o papel das variáveis sexo, atenção para os detalhes e ansiedade traço somática como preditores significativos da capacidade de discriminação olfativa. Por fim, o Estudo 4 apresentou uma revisão integrativa sobre o processamento olfativo no autismo, e como o avanço da investigação nesta área pode beneficiar o conhecimento e a prática no âmbito da cognição e comportamento social. Os resultados desta investigação destacam a importância de explorar as diferentes dimensões das variáveis relacionadas com o autismo para melhor compreender a complexidade das suas relações e impacto no funcionamento do espetro, incluindo no que diz respeito ao funcionamento olfativo.Programa Doutoral em Psicologi

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation
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