Deterministic and stochastic features of rhythmic human movement

Abstract-This paper presents salient features of humanhuman interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and the follower such as a) the order of an autoregressive control policy that maps states of the follower to actions of the guider, b) how the guider may modulate the pulling force in response to the confidence level of the follower, and c) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a 3 rd order auto-regressive predictive control policy and followers tend to adopt 2 nd order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the confidence level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the confidence level of the follower monitored using a time varying virtual damped inertial model.Index Terms-Human robot interaction, hard rein, haptic communication, predictive and reactive control policies.

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“…4 and 5. These results are consistent with those of previous studies on stochastic human behavior [22], [23], [21] in similar contexts.…”

Section: Discussionsupporting

confidence: 83%

Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins

Ranasinghe

Sornkarn

Dasgupta

et al. 2016

IEEE Trans. Cybern.

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“…We do this by processing data like that shown in Fig. 2 to extract estimates of the terms involved in stochastic DEs (SDEs) for χ and Φ using the techniques in Gradišek et al (2000) (see also Friedrich et al (2000), Laing et al (2007), van Mourik et al (2006)). These SDEs will form our macroscopic model, and are assumed to linearly combine purely deterministic and purely stochastic components, i.e.…”

Section: Deriving a Macroscopic Model Choosing Macroscopic Variablesmentioning

confidence: 99%

Reduced models for binocular rivalry

2010

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Binocular rivalry occurs when two very different images are presented to the two eyes, but a subject perceives only one image at a given time. A number of computational models for binocular rivalry have been proposed; most can be categorised as either "rate" models, containing a small number of variables, or as more biophysicallyrealistic "spiking neuron" models. However, a principled derivation of a reduced model from a spiking model is lacking. We present two such derivations, one heuristic and a second using recently-developed data-mining techniques to extract a small number of "macroscopic" variables from the results of a spiking neuron model simulation. We also consider bifurcations that can occur as parameters are varied, and the role of noise in such systems. Our methods are applicable to a number of other models of interest.

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“…This is due to the fact that standard measures of movement variability reflect both noise and attractor strength (Court et al 2002). Frank et al (2003Frank et al ( , 2004 and Mourik et al (2006) have recently begun investigating whether the methods developed by Friedrich, Peinke and colleagues (e.g., Friedrich et al , 2000 can identify the deterministic (attractor strength) and stochastic (noise) components in rhythmic movement data. For example, van Mourik et al (2006) derived a method that involves inspecting movement trajectories in phase space and determining deterministic components in terms of drift coefficients and vector fields, and stochastic components in terms of diffusion coefficients and ellipse fields.…”

Section: Introductionmentioning

confidence: 97%

“…Frank et al (2003Frank et al ( , 2004 and Mourik et al (2006) have recently begun investigating whether the methods developed by Friedrich, Peinke and colleagues (e.g., Friedrich et al , 2000 can identify the deterministic (attractor strength) and stochastic (noise) components in rhythmic movement data. For example, van Mourik et al (2006) derived a method that involves inspecting movement trajectories in phase space and determining deterministic components in terms of drift coefficients and vector fields, and stochastic components in terms of diffusion coefficients and ellipse fields. A number of other researchers have also suggested that recurrence analysis can be used to isolate changes in movement variability caused by a change in noise and attractor strength independently [first by Pellecchia et al (2005) and Shockley (2002) then by Shockley and Turvey (2005) and Richardson et al (2005)].…”

Section: Introductionmentioning

confidence: 98%

Distinguishing the noise and attractor strength of coordinated limb movements using recurrence analysis

2006

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The variability of coupled rhythmic limb movements is assumed to be a consequence of the strength of a movement's attractor dynamic and a constant stochastic noise process that continuously perturbs the movement system away from this dynamic. Recently, it has been suggested that the nonlinear technique of recurrence analysis can be used to index the effects of noise and attractor strength on movement variability. To test this, three experiments were conducted in which the attractor strength of bimanual wrist-pendulum movements (using coordination mode, movement frequency and detuning), as well as the magnitude of stochastic perturbations affecting the variability of these movements (using a temporally fluctuating visual metronome) was manipulated. The results of these experiments demonstrate that recurrence analysis can index parametric changes in the attractor strength of coupled rhythmic limb movements and the magnitude of metronome induced stochastic perturbations independently. The results of Experiments 1 and 2 also support the claim that differences between the variability of inphase and antiphase coordination, and between slow and fast movement frequencies are due to differences in attractor strength. In contrast to the standard assumption that the noise that characterizes interlimb coordination remains constant for different magnitudes of detuning (Delta omega) the results of Experiment 3 suggest that the magnitude of noise increases with increases in |Delta omega|.

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Deterministic and stochastic features of rhythmic human movement

Cited by 55 publications

References 45 publications

Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins

Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins

Reduced models for binocular rivalry

Distinguishing the noise and attractor strength of coordinated limb movements using recurrence analysis

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