Publications

Reaching and grasping (R&G) is a skilled voluntary movement which is critical for animals. In this work, we aim to identify muscle synergy patterns from R&G movements in rats and show how these patterns can be used to characterize such movements and investigate their consistency and repeatability. For that purpose, we analyzed the electromyographic (EMG) activity of five forelimb muscles recorded while the animals were engaged in R&G tasks. Our dataset included 200 R&G attempts from three different rats. Non-negative matrix factorization was used to decompose EMG signals and extract muscle synergies. We compared all pairs of attempts and created cross-validated models to study intra- and inter-subject variability. We found that three synergies were enough to accurately reconstruct the EMG envelopes. These muscle synergies and their corresponding activation coefficients were very similar for all the attempts in the database, providing a general pattern to describe the movement. Results suggested that the movement strategy adopted by an individual in its different attempts was highly repetitive, but also resembled the strategies adopted by the other animals. Inter-subject variability was not much higher than intra-subject variability. This study is a proof-of-concept, but the proposed approaches can help to establish whether there is a stereotyped pattern of neuromuscular activity in R&G movement in healthy rats, and the changes that occur in animal models of acute neurological injuries. Research on muscle synergies could elucidate motor control mechanisms, and lead to quantitative tools for evaluating upper limb motor impairment after an injury.

JTD Keywords: Electromyography, Motor control, Muscle synergies, Reaching and grasping, Upper limb

In the past decade, society has experienced notable growth in a variety of technological areas. However, the Fourth Industrial Revolution has not been embraced yet. Industry 4.0 imposes several challenges which include the necessity of new architectural models to tackle the uncertainty that open environments represent to cyber-physical systems (CPS). Waste Electrical and Electronic Equipment (WEEE) recycling plants stand for one of such open environments. Here, CPSs must work harmoniously in a changing environment, interacting with similar and not so similar CPSs, and adaptively collaborating with human workers. In this paper, we support the Distributed Adaptive Control (DAC) theory as a suitable Cognitive Architecture for managing a recycling plant. Specifically, a recursive implementation of DAC (between both single-agent and large-scale levels) is proposed to meet the expected demands of the European Project HR-Recycler. Additionally, with the aim of having a realistic benchmark for future implementations of the recursive DAC, a micro-recycling plant prototype is presented.

JTD Keywords: Cognitive architecture, Distributed Adaptive Control, Recycling plant, Navigation, Motor control, Human-Robot Interaction

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behavior relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts.

JTD Keywords: Active inference, Cerebellum, Computational model, Motor control, Perceptual learning

Teleoperation, by adequately adapting computer interfaces, can benefit from the knowledge on human factors and psychomotor models in order to improve the effectiveness and efficiency in the execution of a task. While scaling is one of the performances frequently used in teleoperation tasks that require high precision, such as surgery, this article presents a scaling method that considers the system dynamics as well. The proposed dynamic scaling factor depends on the apparent position and velocity of the robot and targets. Such scaling improves the performance of teleoperation interfaces, thereby reducing user's workload.

JTD Keywords: Human-robot interaction, Throughput, Scaling functions, Motor control performance

Teleoperation, by adequately adapting computer interfaces, can benefit from the knowledge on human factors and psychomotor models in order to improve the effectiveness and efficiency in the execution of a task. While scaling is one of the performances frequently used in teleoperation tasks that require high precision, such as surgery, this article presents a scaling method that considers the system dynamics as well. The proposed dynamic scaling factor depends on the apparent position and velocity of the robot and targets. Such scaling improves the performance of teleoperation interfaces, thereby reducing user's workload.

JTD Keywords: Human-robot interaction, Motor control performance, Scaling functions, Throughput