Cardiovascular and Muscular Consequences of Work-Matched Interval-Type of Concentric and Eccentric Pedaling Exercise on a Soft Robot.

Eccentric types of endurance exercise are an acknowledged alternative to conventional concentric types of exercise rehabilitation for the cardiac patient, because they reduce cardiorespiratory strain due to a lower metabolic cost of producing an equivalent mechanical output. The former contention has not been tested in a power- and work-matched situation of interval-type exercise under identical conditions because concentric and eccentric types of exercise pose specific demands on the exercise machinery, which are not fulfilled in current practice. Here we tested cardiovascular and muscular consequences of work-matched interval-type of leg exercise (target workload of 15 sets of 1-min bipedal cycles of knee extension and flexion at 30 rpm with 17% of maximal concentric power) on a soft robotic device in healthy subjects by concomitantly monitoring respiration, blood glucose and lactate, and power during exercise and recovery. We hypothesized that interval-type of eccentric exercise lowers strain on glucose-related aerobic metabolism compared to work-matched concentric exercise, and reduces cardiorespiratory strain to levels being acceptable for the cardiac patient. Eight physically active male subjects (24.0 years, 74.7 kg, 3.4 L O2 min-1), which power and endurance performance was extensively characterized, completed the study, finalizing 12 sets on average. Average performance was similar during concentric and eccentric exercise (p = 0.75) but lower than during constant load endurance exercise on a cycle ergometer at 75% of peak aerobic power output (126 vs. 188 Watt) that is recommended for improving endurance capacity. Peak oxygen uptake (-17%), peak ventilation (-23%), peak cardiac output (-16%), and blood lactate (-37%) during soft robotic exercise were lower during eccentric than concentric exercise. Glucose was 8% increased after eccentric exercise when peak RER was 12% lower than during concentric exercise. Muscle power and RFD were similarly reduced after eccentric and concentric exercise. The results highlight that the deployed interval-type of eccentric leg exercise reduces metabolic strain of the cardiovasculature and muscle compared to concentric exercise, to recommended levels for cardio-rehabilitation (i.e., 50-70% of peak heart rate). Increases in blood glucose concentration indicate that resistance to contraction-induced glucose uptake after the deployed eccentric protocol is unrelated to muscle fatigue.

Cognition from the bottom up: on biological inspiration, body morphology, and soft materials.

Traditionally, in cognitive science the emphasis is on studying cognition from a computational point of view. Studies in biologically inspired robotics and embodied intelligence, however, provide strong evidence that cognition cannot be analyzed and understood by looking at computational processes alone, but that physical system-environment interaction needs to be taken into account. In this opinion article, we review recent progress in cognitive developmental science and robotics, and expand the notion of embodiment to include soft materials and body morphology in the big picture. We argue that we need to build our understanding of cognition from the bottom up; that is, all the way from how our body is physically constructed.

Generating spatiotemporal joint torque patterns from dynamical synchronization of distributed pattern generators.

Pattern generators found in the spinal cord are no more seen as simple rhythmic oscillators for motion control. Indeed, they achieve flexible and dynamical coordination in interaction with the body and the environment dynamics giving to rise motor synergies. Discovering the mechanisms underlying the control of motor synergies constitutes an important research question not only for neuroscience but also for robotics: the motors coordination of high dimensional robotic systems is still a drawback and new control methods based on biological solutions may reduce their overall complexity. We propose to model the flexible combination of motor synergies in embodied systems via partial phase synchronization of distributed chaotic systems; for specific coupling strength, chaotic systems are able to phase synchronize their dynamics to the resonant frequencies of one external force. We take advantage of this property to explore and exploit the intrinsic dynamics of one specified embodied system. In two experiments with bipedal walkers, we show how motor synergies emerge when the controllers phase synchronize to the body's dynamics, entraining it to its intrinsic behavioral patterns. This stage is characterized by directed information flow from the sensors to the motors exhibiting the optimal situation when the body dynamics drive the controllers (mutual entrainment). Based on our results, we discuss the relevance of our findings for modeling the modular control of distributed pattern generators exhibited in the spinal cord, and for exploring the motor synergies in robots.

Comparison of nonlinear Granger causality extensions for low-dimensional systems.

The identification of hidden interdependences among the parts of a complex system is a fundamental issue. Typically, the objective is, given only a sequence of scalar measurements, to infer as much as possible about the internal dynamics of the system and about the interactions between its subsystems. In general, such interactions are not only nonlinear but also asymmetric. Constraints on the estimation of hidden relationships are further posed by noise and by the length of signals sampled from real world systems. The focus of this paper is causal dependences between bivariate time series. We especially focus on the nonlinear extension of Granger causality with polynomial terms of the conventional embedding vector. In this paper, we study the performance of this measure in comparison with three alternative methods proposed recently in low-dimensional and low-order-nonlinearity systems. Those methods are tested with three different artificial chaotic maps with several noise contamination setups. As a result, we find that the polynomial embedding technique successfully detects asymmetric (causal) dependences between bivariate time series in many low-dimensional cases.

Detecting direction of causal interactions between dynamically coupled signals.

The problem of temporal localization and directional mapping of the dynamic interdependencies between parts of a complex system is addressed. We present a technique that weights the sampled values so as to minimize the mutual prediction error between pairs of measured signals. The reliability of the detected intermittent causal interactions is maximized by (a) smoothing the weight landscape through regularization, and (b) using a nonlinear (polynomial) variant of the conventional embedding vector. The effectiveness of the proposed technique is demonstrated by studying three numerical examples of dynamically coupled chaotic maps and by comparing it with two other measures of causal dependency.

Self-organization, embodiment, and biologically inspired robotics.

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Information transfer at multiple scales.

In the study of complex systems a fundamental issue is the mapping of the networks of interaction between constituent subsystems of a complex system or between multiple complex systems. Such networks define the web of dependencies and patterns of continuous and dynamic coupling between the system's elements characterized by directed flow of information spanning multiple spatial and temporal scales. Here, we propose a wavelet-based extension of transfer entropy to measure directional transfer of information between coupled systems at multiple time scales and demonstrate its effectiveness by studying (a) three artificial maps, (b) physiological recordings, and (c) the time series recorded from a chaos-controlled simulated robot. Limitations and potential extensions of the proposed method are discussed.

Mapping information flow in sensorimotor networks.

Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior.

Methods for quantifying the informational structure of sensory and motor data.

Embodied agents (organisms and robots) are situated in specific environments sampled by their sensors and within which they carry out motor activity. Their control architectures or nervous systems attend to and process streams of sensory stimulation, and ultimately generate sequences of motor actions, which in turn affect the selection of information. Thus, sensory input and motor activity are continuously and dynamically coupled with the surrounding environment. In this article, we propose that the ability of embodied agents to actively structure their sensory input and to generate statistical regularities represents a major functional rationale for the dynamic coupling between sensory and motor systems. Statistical regularities in the multimodal sensory data relayed to the brain are critical for enabling appropriate developmental processes, perceptual categorization, adaptation, and learning. To characterize the informational structure of sensory and motor data, we introduce and illustrate a set of univariate and multivariate statistical measures (available in an accompanying Matlab toolbox). We show how such measures can be used to quantify the information structure in sensory and motor channels of a robot capable of saliency-based attentional behavior, and discuss their potential importance for understanding sensorimotor coordination in organisms and for robot design.