A model of the neuro-musculo-skeletal system for human locomotion

Taga, Gentaro

doi:10.1007/bf00204049

Cited by 260 publications

(225 citation statements)

References 29 publications

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“…In this category we can count all the exploitation of synchronization, phase resetting etc. (such as in (Ermentrout & Kopell, 1994;Williamson, 1998;Ijspeert et al, 2005;Matsuoka, 1985Matsuoka, , 1987Schöner et al, 1990;Schöner & Kelso, 1988;Taga, 1994Taga, , 1995bTaga, , 1995aBuchli & Ijspeert, 2004a;22, 2003;Schöner & Santos, 2001;Santos, 2003Santos, , 2004Collins & Richmond, 1994;Endo et al, 2005)). As an example, in case of phase locking the oscillator is matching its frequency to the frequency of the input.…”

Section: Reactive: Temporary Entrainment and Shape Changesmentioning

confidence: 99%

“…It would of course be interesting to exploit such substrates for engineering applications. algorithmic dynamic algorithmic dynamic (Ermentrout & Kopell, 1994) R 1a (Williamson, 1998) R 1a R 1a (Matsuoka, 1985(Matsuoka, , 1987 R 1a (Schöner, Jiang, & Kelso, 1990) R 1a (Schöner & Kelso, 1988) R 1a (Endo et al, 2005) R 1a (Morimoto et al, 2006) R 1a (Righetti & Ijspeert, 2006a) R 1b (Taga, 1994(Taga, , 1995b(Taga, , 1995a R 1a (Buchli & Ijspeert, 2004a) R 1a (22, 2003 R 1a (Schöner & Santos, 2001;Santos, 2003Santos, , 2004 R 1a (Collins & Richmond, 1994) R 1a (Ijspeert, 2001) R 1a/2 (Zegers & Sundareshan, 2003) R 2b (Okada, Tatani, & Nakamura, 2002;Okada, Nakamura, & Nakamura, 2003) R 2a (Ijspeert, Nakanishi, & Schaal, 2002) R 2b (Ruiz, Owens, & Townley, 1998) R 2 (Galicki, Leistritz, & Witte, 1999;Leistritz, Galicki, Witte, & Kochs, 2002) R 2 (Righetti & Ijspeert, 2006b) R 1a/2b (Marbach & Ijspeert, 2005) A 1a (Nishii, 1999) A 1c/1a (Ermentrout, 1991) A 1c (Large, 1994(Large, , 1996 A 1a 1c (Buchli & Ijspeert, 2004b;Righetti, Buchli, & Ijspeert, 2006;Buchli, Righetti, & Ijspeert, 2005;Buchli et al, 2006) A 1c Table 3 Table classifying …”

Section: Adaptation: Lasting Changes To the Dynamicsmentioning

confidence: 99%

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Engineering entrainment and adaptation in limit cycle systems

2006

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Periodic behavior is key to life, and is observed in multiple instances and at multiple time scales in our metabolism, our natural environment, and our engineered environment. A natural way of modelling or generating periodic behavior is done by using oscillators, i.e. dynamical systems that exhibit limit cycle behavior. While there is extensive literature on methods to analyze such dynamical systems, much less work has been done on methods to synthesize an oscillator to exhibit some specific desired characteristics. The goal of this article is two-fold: (1) to provide a framework for characterizing and designing oscillators, and (2) to review how classes of well known oscillators can be understood and related to this framework.The basis of the framework is to characterize oscillators in terms of their fundamental temporal and spatial behavior, and in terms of properties that these two behaviors can be designed to exhibit. This focus on fundamental properties is important because it allows us to systematically compare a large variety of oscillators which might at first sight appear very different from each other. We identify several specifications that are useful for design, such as frequency-locking behavior, phaselocking behavior, and specific output signal shape. We also identify two classes of design methods by which these specifications can be met, namely off-line methods and on-line methods. By relating these specifications to our framework and by presenting several examples of how oscillators have been designed in the literature, this article provides a useful methodology and toolbox for designing oscillators for a wide range of purposes. In particular the focus on synthesis of limit cycle dynamical systems should be useful both for engineering and for computational modelling of physical or biological phenomena.

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Section: Reactive: Temporary Entrainment and Shape Changesmentioning

confidence: 99%

Section: Adaptation: Lasting Changes To the Dynamicsmentioning

confidence: 99%

Engineering entrainment and adaptation in limit cycle systems

2006

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“…Finally, sensory feedback ensures that adequate reflexes counteract disturbances. It has been shown in theoretical studies that a neural network with these properties can indeed generate stable two-legged locomotion [19].…”

Section: Central Pattern Generatormentioning

confidence: 99%

Speed dependence of averaged EMG profiles in walking

et al. 2002

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Electromyogram (EMG) profiles strongly depend on walking speed and, in pathological gait, patients do not usually walk at normal speeds. EMG data was collected from 14 muscles in two groups of healthy young subjects who walked at five different speeds ranging from 0.75 to 1.75 ms − 1 . We found that average EMG profiles varied in a predictable way with speed. The average EMG profile for each muscle at any speed could be estimated in a simple way from two functions, one constant and one proportionally increasing with walking speed. By taking into account the similarity among profiles within functional groups, the number of basic functions could be reduced further. Any average EMG profile among the 14 leg muscles studied at all speeds in the measured range could be predicted from six constant and ten speed-dependent basic patterns. These results can be interpreted in terms of a central pattern generator for human walking.

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“…However, the problem of using this class of method, in conjunction with sensory inputs delivered in real-time, has rarely been addressed. Some studies [4,5] report the use of sensory inputs in dynamic neural networks; however, the use of such neural networks does not allow the system designer to ensure behavioural properties, such as synchronization of the network with respect to the input. This is precisely the goal of this paper.…”

Section: Introductionmentioning

confidence: 99%

Online generation of cyclic leg trajectories synchronized with sensor measurement

Héliot

Espiau

2008

Robotics and Autonomous Systems

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The generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying off-line solutions exist for steady-state walking in absence of disturbances. The question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. This is precisely the problem addressed in this paper. Indeed, we consider the case where the "master" motion is measured by an position sensor embedded on a human leg. We propose a method to synchronize the motion of a robot or on other device with respect to the output signal of the sensor. The main goal is to estimate as accurately as possible the current phase along the gait cycle. We use for that purpose a model based on a nonlinear oscillator, to which we associate an observer. Introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. The paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input.

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A model of the neuro-musculo-skeletal system for human locomotion

Cited by 260 publications

References 29 publications

Engineering entrainment and adaptation in limit cycle systems

Engineering entrainment and adaptation in limit cycle systems

Speed dependence of averaged EMG profiles in walking

Online generation of cyclic leg trajectories synchronized with sensor measurement

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