323,489 research outputs found
Model-based control of FES embedding simultaneous voluntary user effort
No full textThere are over 1.2 million people in the UK with upper or lower limb impairment following stroke. Artificial activation of muscle can be achieved using functional electrical stimulation (FES), which is the most prevalent assistive technology used in the rehabilitation of stroke patients. Significant clinical research shows that effective treatment requires electrical pulses to be delivered to muscles in a manner which supports voluntary effort, during performance of repetitive rehabilitation tasks. This has motivated using electromyography (EMG) to measure the voluntary contraction and then to control electrical stimulation supplied to impaired muscles. However, existing FES control schemes using EMG are predominantly open loop and fail to provide accurate assistance to achieve the intended movement. In this thesis, a model of dynamic interaction between voluntary and evoked muscle activation is initially developed, embedding both nonlinear recruitment and activation dynamics. This enables the proposed model-based, hybrid EMG/FES control scheme to be derived, allowing the dual objectives of tracking and volitional intention support to be optimised. Extension of the model-based EMG/FES structure to embed iterative learning control (ILC) is then undertaken in order to augment the tracking accuracy by learning from experience to update the control action. Experimental results show that the identification scheme is accurate and suitable for clinical application. Further results show that the model-based ILC framework using hybrid activation reduces the tracking error by between 27% and 70% compared to previous FES approaches which neglect voluntary action. Identifying a model of EMG/FES muscle activation is time-consuming and accuracy is degraded by fatigue and other time-varying properties. To address these issues, an adaptive control scheme is then developed termed ‘estimation-based multiple model ILC’ (EMMILC). This control framework automatically selects a controller based on the most appropriate model chosen from an underlying set of ‘candidate models’. A design procedure is proposed to generate a set of models based on distributions of fatigue tests. Results indicate that EMMILC framework has a significant improvement in tracking between 50% and 112% despite the changing model over time due to fatigue effect compared to standard ILC designed using a single model. Additional results confirm the potential of the proposed framework to be applied without the need of model identification
Model-based control of FES embedding simultaneous volitional EMG measurement
No full textThere are over one million people in the UK with upper limb impairment following stroke. Artificial activation of muscle can be achieved using functional electrical stimulation (FES), and enable recovery by facilitating task practice. Significant clinical research supports the utility of FES for both orthotic and therapeutic purposes, and shows that the effectiveness is maximised when applied concurrently with a patient’s voluntary effort. Voluntary effort can be captured using electromyography (EMG), however existing FES control schemes using EMG are predominantly open-loop and fail to provide accurate assistance.In this paper, a model of the dynamic interaction between voluntary and evoked muscle activation is developed, embedding both nonlinear recruitment and activation dynamics. Then an identification method is proposed suitable for clinical application. This enables a model-based, hybrid EMG/FES control scheme to be developed, allowing the dual objectives of tracking and volitional intention support to be optimized for the first time. Experimental results show that the tracking performance of the controller is far more effective compared to previous FES approaches which neglect voluntary action.</p
Iterative learning control of FES with embedded simultaneous volitional EMG
No full textSeventeen million people are left with limb impairment following stroke, and there is an urgent need for new effective rehabilitation technology. Functional electrical stimulation (FES) has been shown to facilitate motor re-learning by artificially activating muscles during practice of motor tasks, however clinical outcomes depend on how closely FES matches the intended motion. Electromyography (EMG) signals recorded from muscle can capture voluntary intention, and have successfully been employed in openloop FES controllers. Likewise, model-based controllers using force and/or position data have yielded accurate movement control in clinical trials. Iterative learning control (ILC) in particular has been successful in clinical tests, since the process of rehabilitationis inherently iterative. This paper develops a new control strategy to combine both EMG and model-based control. It begins by developing a novel hybrid model of muscle dynamics incorporating both EMG and FES. ILC is then employed to enable precise control over both variables, thereby offering substantial improvements over existing control schemes in the domain of stroke rehabilitation. Experimental results confirm efficacy of the hybrid control scheme, as well as its suitability for clinical application
Iterative learning control of functional electrical stimulation in the presence of voluntary user effort
No full textWorldwide 17 million people are left with impairment to their upper or lower limb following stroke. Functional electrical stimulation (FES) is a method of artificially activating muscles using electrical pulses and is the most common rehabilitation technology. A significant body of clinical research confirms that successful rehabilitation requires FES to be applied in a way that supports voluntary intention during repeated attempts at functional tasks. Electromyography (EMG) measures the voluntary contraction of muscles and has been used to directly control FES in openloop, however it is limited by poor accuracy. On the other hand, model-based feedback control can provide high accuracy, but does not explicitly promote voluntary intention.A new dynamic model of the muscle activation, generated by combined voluntary nerve signals and FES, is developed in this paper. It includes both nonlinear recruitment and linear activation dynamics. An efficient identification procedure is then formulated which can be applied to people with stroke. A model-based hybrid EMG/FES control scheme is then derived based on the model structure, allowing tracking and volitional intention support to be simultaneously optimised for the first time. Exploiting the repeated nature of rehabilitation, the control framework is then extended to further improve tracking accuracy. That is achieved by learning from experience through iterative learning control. The framework is experimentally tested with results confirming it can deliver greater performance compared to existing FES approaches, which do not consider voluntary action in the model or controller
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
The construction of Karen Karnak: The multi-author-function
No full textThis thesis is situated within the comparatively recent developments of Web 2.0 and the emergence of interactive WikiMedia, and explores the mode of authorship within a Read/Write culture compared to that of a Read/Only tradition. The hypothesis of this study is that the role of the audience has become merged with the author, and as such, represents new functions and attributes, distinct from a more conventional concept of authorship, in which the roles of audience and author are more separate. Read/Write and participatory culture, as defined by this study, is focused on collaboration, and includes the influences of D.I.Y. culture, Open-Source practices and the production of text by multiple authors. Multi-authorship presents a re-thinking of several concepts which support the notion of the individual author, since the focus of multi-authorship is not on attribution and ownership of a finished text, but on the continued malleability of a text. Modes of multi-authorship, demonstrated in the use of the pseudonyms Alan Smithee and Karen Eliot, represent declarative authors whose names signify multiple origins, whilst concurrently indicating a distinct body of work. The function of these names form an important context to this study, since primary research involves the construction of an experimental mode of multi-authorship utilising WikiMedia technology and the interaction of thirty nine participants, who are invited to create a body of work under the collective pseudonym Karen Karnak. The data generated by this experiment is analysed using aspects of Michel Foucault's author-function to identify and determine power structures inherent in the WikiMedia context. The interplay of power structures, including concepts such as identity, ownership and the body of work, affect the resulting mode of authorship and contribute to the construction of Karen Karnak, suggesting further areas of research into the emerging multi-author
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
Language Change and SA-OT: The case of sentential negation
No full textSimulated Annealing for Optimality Theory (SA-OT) updates Optimality Theory by adding a model of performance to a theory of linguistic competence. Our aim is to show that SA-OT can contribute to language change simulations. Performance "errors" are considered to be one of the causes of variation and change. We have chosen to model the evolution of sentential negation (SN). The descriptive background adopts Jespersen's Cycle, according to which the evolution of sentential negation follows three main stages (1. pre-verbal, 2. discontinuous, and 3. post-verbal). Therefore, we advance a novel model for SN, based on SA-OT. It reproduces the three pure and the two observed mixed stages, whereas it correctly predicts the lack of an intermediate stage between 3 and 1. The success of the approach corroborates the computational, performance-based approach to the data. Finally, we employ the iterated learning paradigm to reproduce historical changes in a "simulated corpus study". This enterprise turns out to be more difficult than one would naively believe.Appeared open access as: Computational Linguistics in the Netherlands Journal (CLIN), vol. 1 (2011), pp. 21-40, and is available at http://www.clinjournal.org/sites/default/files/Lopopolo.pdfA. Lopopolo and Biró, T., “Language Change and SA-OT. The case of sentential negation”, Computational Linguistics in the Netherlands Journal, vol. 1, pp. 21-40, 2011.Peer Reviewe
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