Avoiding Spurious Submovement Decompositions II: A Scattershot Algorithm

Rohrer, Brandon R.; Hogan, Neville

doi:10.1007/s00422-006-0055-y

Cited by 67 publications

(57 citation statements)

References 19 publications

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“…The second method uses a stochastic "scattershot" global nonlinear minimization algorithm. It is probabilistic in nature; i.e., the results are globally optimal with probability close but not equal to unity, but it requires approximately four orders of magnitude less time to compute [53][54].…”

Section: Extracting Submovementsmentioning

confidence: 99%

Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery

Hogan

Krebs²,

Rohrer³

et al. 2006

JRRD

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Abstract-Robotics and related technologies have begun to realize their promise to improve the delivery of rehabilitation therapy. However, the mechanism by which they enhance recovery remains unclear. Ultimately, recovery depends on biology, yet the details of the recovery process remain largely unknown; a deeper understanding is important to accelerate refinements of robotic therapy or suggest new approaches. Fortunately, robots provide an excellent instrument platform from which to study recovery at the behavioral level. This article reviews some initial insights about the process of upper-limb behavioral recovery that have emerged from our work. Evidence to date suggests that the form of therapy may be more important than its intensity: muscle strengthening offers no advantage over movement training. Passive movement is insufficient; active participation is required. Progressive training based on measures of movement coordination yields substantially improved outcomes. Together these results indicate that movement coordination rather than muscle activation may be the most appropriate focus for robotic therapy.

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Section: Extracting Submovementsmentioning

confidence: 99%

Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery

Hogan

Krebs²,

Rohrer³

et al. 2006

JRRD

Self Cite

327

200

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“…Rohrer and Hogan outline various types of roughly bell shaped functions representing submovements and present algorithms for fitting sums of bell-shaped functions to kine matic data [20] [21]. The types of bell-shaped functions include the Gaussian curve, support-bounded log-normal curve, and the minimum jerk curve.…”

Section: A Submovement Decompositionmentioning

confidence: 99%

“…The types of bell-shaped functions include the Gaussian curve, support-bounded log-normal curve, and the minimum jerk curve. In this paper, the planar velocity of the monkey's hand is decomposed into minimum jerk curves, similar methods to the ones applied by [21]. In the subsequent RPCA analysis, submovements with small amplitude and/or long duration are ignored to avoid artifacts of overfitting.…”

Section: A Submovement Decompositionmentioning

confidence: 99%

Low-rank representation of neural activity and detection of submovements

Chang

Chen

Gowda

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-In this study, Robust Principal Component Anal ysis (RPCA) is applied to neural spike datasets to extract neural signatures that signify the onset of submovements, a type of motor primitive. Given neural activity recorded from rhesus macaques during a set of reaches between targets in a horizontal plane, we aim to identify common event-related neural features and validate sub movement-based motor prim itives inferred from the hand velocity profiles. Such features represent common dynamic patterns across many experimental trials and may be used as a signature to detect discrete events such as submovement onset. We present RPCA, a method well suited for extracting data matrices' low-rank component and this method allows (1) removal of task-irrelevant signal from data, (2) identification of task-related dynamic patterns, and (3) detection of sub movements. We also explored using the Random Projection (RP) technique and applying RP to data prior to applying RPCA improved the sub movement prediction performance by de-sparsifying neural data while preserving certain statistical characteristics of aggregate neural activity.

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“…Since every linguist agrees that 'peripheral', difficult cases must be learned inductively on the basis of the input, constructionists point out that there is no reason to assume that the more general, regular, frequent cases cannot possibly be. "On the same wavelength, Tomasello (2003,(104)(105) remarks that "not only must there be a mechanism for learning the idiosyncratic, but this mechanism produces an output that has all of the properties of core grammar, except for maximal generality." In short, advocates of the usagebased approach take the position that core properties are ones with 'maximal generality' and, hence, properties whose effects appear with greater regularity in a language, as compared to peripheral phenomena.…”

mentioning

confidence: 99%

“…The velocity profile of the reconstruction F(t) is then given by the superposition of N submovements ẋ i (t) ( 1) where T 0i and T 0i +D are the start and finish times of the ith submovement, ẋ i (t). In contrast to Rohrer and Hogan (2006), where they compared F(t) to the tangential velocity, …”

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confidence: 99%

Proceedings of the 9th Conference of the Australasian Society for Cognitive Science

2010

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. Submissions to the conference were reviewed by the Program Committee, who came up with an excellent program of 106 presented papers (in three parallel-track sessions, and including eight submitted symposia) and 20 posters (on show throughout the conference, but highlighted in an enjoyable poster session over drinks on day 2). The full program, along with other information about the conference, remains online at http://www.maccs.mq.edu.au/ascs09. The Australasian Society for Cognitive Science has had an intermittent formal existence, driven mainly by the organization of its string of enjoyable, successful, and highly sociable conferences, which have taken place roughly every two years since the inaugural conference, organized by Peter Slezak, at the University of New South Wales in 1990 (a full list of past conferences is appended below).At the 9 th conference at Macquarie, 163 people attended, including substantial numbers from overseas and interstate, enjoying both the sweet Sydney spring weather and three stimulating days of talks and talk. There was no specific theme, but in addition to specialist research papers within specific sub/disciplines, we encouraged integrative papers which forge connections between sub/disciplines. This integrative ideal also informed invitations to our five excellent keynote speakers: Stephen Crain (Macquarie Centre for Cognitive Science), Jakob Hohwy (Philosophy, Monash University), Jason Mattingley (Psychology & Queensland Brain Institute, University of Queensland), Thomas Metzinger (Philosophy, Johannes Gutenberg-Universität, Mainz; & Institute for Advanced Study, Berlin, Germany, and Barbara Tversky (Psychology, Stanford University; & Columbia University, USA). Versions of three of these keynote talks appear in these proceedings: the papers by Jason Mattingley and Thomas Metzinger have been published elsewhere.Perhaps because the cognitive science community in Australasia is relatively small, compared at least with our counterparts in Asia, Europe, and North America, it retains a thoroughly multidisciplinary atmosphere. The programme confirmed the striking pluralism of the contemporary cognitive sciences, drawing on many distinctive disciplines as researchers on cognition are looking both down into the brain and out into the environment. Most contributions were from psychologists, with sizable numbers from philosophy and linguistics: but many other areas were also represented, including computational neuroscience, neuropsychiatry, clinical psychology, computer science, human-computer interaction, anthropology, education, history and philosophy of science, cognitive archaeology, cognitive ethology, English literature, and cultural history.All those who had presented papers and posters were invited to submit revised versions, following the conference, as full contributions to the published Proceedings. All submissions were reviewed afresh, and revisions were submitted in early 2010: we are delighted to thank our team of reviewers for their hard and effective work.Fifty-seven p...

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Avoiding Spurious Submovement Decompositions II: A Scattershot Algorithm

Cited by 67 publications

References 19 publications

Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery

Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery

Low-rank representation of neural activity and detection of submovements

Proceedings of the 9th Conference of the Australasian Society for Cognitive Science

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