Chaotic mimic robots

Buscarino, Arturo; Camerano, Cristoforo; Fortuna, Luigi; Frasca, Mattia

doi:10.1098/rsta.2010.0028

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…However, the neural networks method is good at classifying the actions by the spatial data. The work in [34] proposed that the synchronization between the robots of a team was achieved by exploiting the paradigm of mirror neurons.…”

Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

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Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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“…Networks of oscillatory units have been recently studied widely [1][2][3][4]. These kinds of systems serve as a modeling basis for a variety of systems from neuroscience [5], pattern recognition [6], chemistry [7], biology [8], climatology [9][10][11], ecology [12], social systems [13], or engineering as for instance in robot coordination [14], communication [15], and sensor networks [16], and as a general concept for understanding complex self-organizing systems. Gaining knowledge about networks of coupled dynamical systems helps in understanding several phenomena, in particular synchronization, self-organization, and information transfer in complex systems.…”

Section: Introductionmentioning

confidence: 99%

Remote synchronization in star networks

et al. 2012

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We study phase synchronization in a network motif with a starlike structure in which the central node's (the hub's) frequency is strongly detuned against the other peripheral nodes. We find numerically and experimentally a regime of remote synchronization (RS), where the peripheral nodes form a phase synchronized cluster, while the hub remains free with its own dynamics and serves just as a transmitter for the other nodes. We explain the mechanism for this RS by the existence of a free amplitude and also show that systems with a fixed or constant amplitude, such as the classic Kuramoto phase oscillator, are not able to generate this phenomenon. Further, we derive an analytic expression which supports our explanation of the mechanism.

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“…This mechanism in the brain of monkeys is able to show the congruence between the observed and executed actions. Mirror neurons have several applications of interactions between human and robots in order to establish a cold resonance between human and machines (Buscarino et al, 2010).…”

Section: Resonance and Mirror Neuronsmentioning

confidence: 99%