Avoiding spurious submovement decompositions: a globally optimal algorithm

Rohrer, Brandon R.; Hogan, Neville

doi:10.1007/s00422-003-0428-4

Cited by 56 publications

(55 citation statements)

References 21 publications

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“…Second, and much more vexing, even if the submovement shape was known, the sequence of submovements obtained is exquisitely sensitive to the method used to identify them. We found that all of the prior methods that have been used were vulnerable to substantial misidentification [52]. By recasting submovement extraction as a global nonlinear optimization problem, we developed two reliable approaches.…”

Section: Extracting Submovementsmentioning

confidence: 99%

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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%

“…The first is based on a "branch-andbound" algorithm. It is powerful, with proven convergence properties, and can correctly identify submovements even in the presence of noise [52]. Unfortunately, it is computationally burdensome.…”

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

320

200

View full text Add to dashboard Cite

show abstract

“…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%

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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“…In general, global nonlinear optimization problems are difficult to solve. Although several submovement extraction algorithms have been proposed previously (Morasso and Mussa-Ivaldi 1982;Flash and Henis 1991;Milner 1992;Berthier 1996;Lee et al 1997;Burdet and Milner 1998), all of them are subject to finding local, rather than global, minima and to producing spurious decomposition results (Rohrer and Hogan 2003). Principal components analysis was evaluated for use in submovement extraction, as well, but was found to be inappropriate in that it extracted continuous time components that spanned the entire movement and that contained parts of several submovements.…”

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confidence: 99%

Avoiding Spurious Submovement Decompositions II: A Scattershot Algorithm

Rohrer

Hogan

2006

Biol Cybern

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Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. In previous work, a branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization, and hence, capable of avoiding spurious decompositions, was presented [Rohrer and Hogan (Biol Cybern 39:190-199, 2003)]. Here, we present a scattershot-type global nonlinear minimization algorithm that requires approximately four orders of magnitude less time to compute. A sensitivity analysis reveals that the scattershot algorithm can reliably detect changes in submovement parameters over time, e.g., over the course of neuromotor recovery.

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Avoiding spurious submovement decompositions: a globally optimal algorithm

Cited by 56 publications

References 21 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

Avoiding Spurious Submovement Decompositions II: A Scattershot Algorithm

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