Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with ...enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human–robot interfaces, safe human–robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.
Muscle coordination studies repeatedly show low-dimensionality of muscle activations for a wide variety of motor tasks. The basis vectors of this low-dimensional subspace, termed muscle synergies, ...are hypothesized to reflect neurally-established functional muscle groupings that simplify body control. However, the muscle synergy hypothesis has been notoriously difficult to prove or falsify. We use cadaveric experiments and computational models to perform a crucial thought experiment and develop an alternative explanation of how muscle synergies could be observed without the nervous system having controlled muscles in groups. We first show that the biomechanics of the limb constrains musculotendon length changes to a low-dimensional subspace across all possible movement directions. We then show that a modest assumption--that each muscle is independently instructed to resist length change--leads to the result that electromyographic (EMG) synergies will arise without the need to conclude that they are a product of neural coupling among muscles. Finally, we show that there are dimensionality-reducing constraints in the isometric production of force in a variety of directions, but that these constraints are more easily controlled for, suggesting new experimental directions. These counter-examples to current thinking clearly show how experimenters could adequately control for the constraints described here when designing experiments to test for muscle synergies--but, to the best of our knowledge, this has not yet been done.
Key points
It is theorized that the nervous system controls groups of muscles together as functional units, or ‘synergies’, resulting in correlated electromyographic (EMG) signals among muscles. ...However, such correlation does not necessarily imply group‐level neural control.
Oscillatory synchronization (coherence) among EMG signals implies neural coupling, but it is not clear how this relates to control of muscle synergies.
EMG was recorded from seven arm muscles of 10 adult participants rotating an upper limb ergometer, and EMG–EMG coherence, EMG amplitude correlations and their relationship with each other were characterized. A novel method to derive multi‐muscle synergies from EMG–EMG coherence is presented and these are compared with classically defined synergies.
Coherent alpha‐band (8–16 Hz) drive was strongest among muscles whose gross activity levels are well correlated within a given task.
The cross‐muscle distribution and temporal modulation of coherent alpha‐band drive suggests a possible role in the neural coordination/monitoring of synergies.
During movement, groups of muscles may be controlled together by the nervous system as an adaptable functional entity, or ‘synergy’. The rules governing when (or if) this occurs during voluntary behaviour in humans are not well understood, at least in part because synergies are usually defined by correlated patterns of muscle activity without regard for the underlying structure of their neural control. In this study, we investigated the extent to which comodulation of muscle output (i.e. correlation of electromyographic (EMG) amplitudes) implies that muscles share intermuscular neural input (assessed via EMG–EMG coherence analysis). We first examined this relationship among pairs of upper limb muscles engaged in an arm cycling task. We then applied a novel multidimensional EMG–EMG coherence analysis allowing synergies to be characterized on the basis of shared neural drive. We found that alpha‐band coherence (8–16 Hz) is related to the degree to which overall muscle activity levels correlate over time. The extension of this coherence analysis to describe the cross‐muscle distribution and temporal modulation of alpha‐band drive revealed a close match to the temporal and structural features of traditionally defined muscle synergies. Interestingly, the coherence‐derived neural drive was inversely associated with, and preceded, changes in EMG amplitudes by ∼200 ms. Our novel characterization of how alpha‐band neural drive is dynamically distributed among muscles is a fundamental step forward in understanding the neural origins and correlates of muscle synergies.
Key points
It is theorized that the nervous system controls groups of muscles together as functional units, or ‘synergies’, resulting in correlated electromyographic (EMG) signals among muscles. However, such correlation does not necessarily imply group‐level neural control.
Oscillatory synchronization (coherence) among EMG signals implies neural coupling, but it is not clear how this relates to control of muscle synergies.
EMG was recorded from seven arm muscles of 10 adult participants rotating an upper limb ergometer, and EMG–EMG coherence, EMG amplitude correlations and their relationship with each other were characterized. A novel method to derive multi‐muscle synergies from EMG–EMG coherence is presented and these are compared with classically defined synergies.
Coherent alpha‐band (8–16 Hz) drive was strongest among muscles whose gross activity levels are well correlated within a given task.
The cross‐muscle distribution and temporal modulation of coherent alpha‐band drive suggests a possible role in the neural coordination/monitoring of synergies.
Coherence analysis has the ability to identify the presence of common descending drive shared by motor unit pools and reveals its spectral properties. However, the link between spectral properties of ...shared neural drive and functional interactions among muscles remains unclear. We assessed shared neural drive between muscles of the thumb and index finger while participants executed two mechanically distinct precision pinch tasks, each requiring distinct functional coordination among muscles. We found that shared neural drive was systematically reduced or enhanced at specific frequencies of interest (~10 and ~40 Hz). While amplitude correlations between surface EMG signals also exhibited changes across tasks, only their coherence has strong physiological underpinnings indicative of neural binding. Our results support the use of intermuscular coherence as a tool to detect when coactivated muscles are members of a functional group or synergy of neural origin. Furthermore, our results demonstrate the advantages of considering neural binding at 10, ~20, and >30 Hz, as indicators of task-dependent neural coordination strategies.
It is often unclear whether correlated activity among muscles reflects their neural binding or simply reflects the constraints defining the task. Using the fact that high-frequency coherence between EMG signals (>6 Hz) is thought to reflect shared neural drive, we demonstrate that coherence analysis can reveal the neural origin of distinct muscle coordination patterns required by different tasks.
Much debate has arisen from research on muscle synergies with respect to both limb impedance control and energy consumption. Studies of limb impedance control in the context of reaching movements and ...postural tasks have produced divergent findings, and this study explores whether the use of synergies by the central nervous system (CNS) can resolve these findings and also provide insights on mechanisms of energy consumption. In this study, we phrase these debates at the conceptual level of interactions between neural degrees of freedom and tasks constraints. This allows us to examine the ability of experimentally-observed synergies--correlated muscle activations--to control both energy consumption and the stiffness component of limb endpoint impedance. In our nominal 6-muscle planar arm model, muscle synergies and the desired size, shape, and orientation of endpoint stiffness ellipses, are expressed as linear constraints that define the set of feasible muscle activation patterns. Quadratic programming allows us to predict whether and how energy consumption can be minimized throughout the workspace of the limb given those linear constraints. We show that the presence of synergies drastically decreases the ability of the CNS to vary the properties of the endpoint stiffness and can even preclude the ability to minimize energy. Furthermore, the capacity to minimize energy consumption--when available--can be greatly affected by arm posture. Our computational approach helps reconcile divergent findings and conclusions about task-specific regulation of endpoint stiffness and energy consumption in the context of synergies. But more generally, these results provide further evidence that the benefits and disadvantages of muscle synergies go hand-in-hand with the structure of feasible muscle activation patterns afforded by the mechanics of the limb and task constraints. These insights will help design experiments to elucidate the interplay between synergies and the mechanisms of learning, plasticity, versatility and pathology in neuromuscular systems.
Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume 'motor noise' leads to force variability and its 'signal ...dependence' (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by 'motor noise.' Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.
1 Department of Biomedical Engineering, University of Southern California and 2 Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California; 3 Sibley ...School of Mechanical and Aerospace Engineering and 4 Department of Mathematics, Cornell University, Ithaca, New York; 5 Departments of Applied Mathematics and Computer Science and Engineering, University of Washington, Seattle, Washington; and 6 Department of Cognitive Science, University of California San Diego, La Jolla, California
Submitted 1 March 2008;
accepted in final form 8 April 2009
Numerous observations of structured motor variability indicate that the sensorimotor system preferentially controls task-relevant parameters while allowing task-irrelevant ones to fluctuate. Optimality models show that controlling a redundant musculo-skeletal system in this manner meets task demands while minimizing control effort. Although this line of inquiry has been very productive, the data are mostly behavioral with no direct physiological evidence on the level of muscle or neural activity. Furthermore, biomechanical coupling, signal-dependent noise, and alternative causes of trial-to-trial variability confound behavioral studies. Here we address those confounds and present evidence that the nervous system preferentially controls task-relevant parameters on the muscle level. We asked subjects to produce vertical fingertip force vectors of prescribed constant or time-varying magnitudes while maintaining a constant finger posture. We recorded intramuscular electromyograms (EMGs) simultaneously from all seven index finger muscles during this task. The experiment design and selective fine-wire muscle recordings allowed us to account for a median of 91% of the variance of fingertip forces given the EMG signals. By analyzing muscle coordination in the seven-dimensional EMG signal space, we find that variance-per-dimension is consistently smaller in the task-relevant subspace than in the task-irrelevant subspace. This first direct physiological evidence on the muscle level for preferential control of task-relevant parameters strongly suggest the use of a neural control strategy compatible with the principle of minimal intervention. Additionally, variance is nonnegligible in all seven dimensions, which is at odds with the view that muscle activation patterns are composed from a small number of synergies.
Address for reprint requests and other correspondence: F. J. Valero-Cuevas, The University of Southern California, 3710 McClintock Ave, RTH 404, Los Angeles, California 90089-2905 (E-mail: valero{at}usc.edu )
The biotechnological production of biodiesel is based on transesterification/esterification reactions between a source of fatty acids and a short‐chain alcohol, usually methanol, catalysed by enzymes ...belonging to the class known as lipases. Several lipases used in industrial processes, although stable in the presence of other organic solvents, are inactivated by methanol at or below the concentration optimal for biodiesel production, making it necessary to use stepwise methanol feeding or pre‐treatment of the enzyme. In this review article we focus on what is currently know about methanol inactivation of lipases, a phenomenon which is not common to all lipase enzymes, with the goal of improving the biocatalytic process. We suggest that different mechanisms can lead to inactivation of different lipases, in particular substrate inhibition and protein unfolding. Attempts to improve the performances of methanol sensitive lipases by mutagenesis as well as process engineering approaches are also summarized
The biotechnological production of biodiesel is based on lipase‐catalysed transesterification between a source of fatty acids and short chain alcohols, usually methanol. The inactivation of lipases by methanol is reported as a major hurdle in the process. This review article reports the current knowledge about the basis of lipase inactivation and possible mechanisms, which are prerequisite for the design of more stable biocatalysts. The figure shows binding of water (red/white) and methanol (green) to the surface of Candida antarctica lipase B (blue) in toluene (not shown) as solvent (kindly provided by Michael Krone and Tobias Kulschewski, University of Stuttgart, using MegaMol™).
The National Institute of Standards and Technology Advanced Radiometer (NISTAR) onboard the Deep Space Climate Observatory (DSCOVR) provides continuous full-disk global broadband irradiance ...measurements over most of the sunlit side of the Earth. The three active cavity radiometers measure the total radiant energy from the sunlit side of the Earth in shortwave (SW; 0.2–4 µm), total (0.4–100 µm), and near-infrared (NIR; 0.7–4 µm) channels. The Level 1 NISTAR dataset provides the filtered radiances (the ratio between irradiance and solid angle). To determine the daytime top-of-atmosphere (TOA) shortwave and longwave radiative fluxes, the NISTAR-measured shortwave radiances must be unfiltered first. An unfiltering algorithm was developed for the NISTAR SW and NIR channels using a spectral radiance database calculated for typical Earth scenes. The resulting unfiltered NISTAR radiances are then converted to full-disk daytime SW and LW flux by accounting for the anisotropic characteristics of the Earth-reflected and emitted radiances. The anisotropy factors are determined using scene identifications determined from multiple low-Earth orbit and geostationary satellites as well as the angular distribution models (ADMs) developed using data collected by the Clouds and the Earth's Radiant Energy System (CERES).
Global annual daytime mean SW fluxes from NISTAR are about 6 % greater than those from CERES, and both show strong diurnal variations with daily maximum–minimum differences as great as 20 Wm−2 depending on the conditions of the sunlit portion of the Earth. They are also highly correlated, having correlation coefficients of 0.89, indicating that they both capture the diurnal variation.
Global annual daytime mean LW fluxes from NISTAR are 3 % greater than those from CERES, but the correlation between them is only about 0.38.
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