Jorge Antonio Reyes-Avendaño

Gesture recognition enables intuitive human–robot communication, where surface electromyography (sEMG) provides a minimally invasive interface for detecting motor activity. However, the lack of systematic evaluation of processing pipelines represents a critical barrier to reliable subject-independent deployment. This work presents a systematic evaluation of sEMG processing pipelines for three-gesture recognition (Neutral, Ask, Take) using a Bagged Trees classifier under Leave-One-Subject-Out (LOSO) cross-validation across ten participants. A mixed-effects ANOVA over 1872 experimental configurations revealed that the preprocessing pipeline is the dominant factor affecting classification accuracy, followed by inter-subject variability, while window size and overlap exhibit smaller but statistically significant effects. A consistency-based elbow analysis identified a compact subset of five features that reduced input dimensionality by 94.79% while improving accuracy from 68.10% to 69.86% and reducing training time by 51.62%. Bayesian hyperparameter optimization was also tested, but it did not yield statistically significant improvements over the five-feature baseline; given the limited cohort (n = 10), this indicates the absence of a detectable difference and points to inter-subject variability as a leading factor limiting performance rather than model configuration. These findings provide empirically grounded guidelines for designing computationally efficient sEMG-based gesture recognition systems for collaborative robotics.

Recognizing human movement intention in less controlled environment is the step needed to go beyond strict laboratory settings in order to improve intuitive and reliable human-robot interaction systems. By taking this into account, the present study evaluates the feasibility of decoding the intention to perform hand-arm gestures in self-paced tasks from electroencephalography (EEG) and electromyography (EMG) collected with wearable dry-electrode devices. An experimental protocol was designed to emulate three gestures in human-robot interaction: Neutral, Ask, and Take. The protocol was implemented in two laboratories using different EEG headsets, Ultracortex Mark IV and Muse 2, and a common EMG device, Mindrove armband. Signals were preprocessed and segmented around the start of movement to be classified with a Bidirectional Long Short-Term Memory network with a fully connected output layer (BiLSTM-FC). Although the typical waveform of intention, movement-related cortical potential (MRCP), could not be reliably identified, the results showed that the combination of EEG andEMGimproves the discriminability of intention patterns, achieving a mean of 88.95% in accuracy, and a maximum of 95.98% using the Ultracortex Mark IV and the Mindrove armband. These findings demonstrate the robustness of multimodal data gathered with accessible wearable technology to develop hybrid brain-computer interfaces.

Human–robot collaboration (HRC) can be defined as the close interaction between a human user and a robot working together to accomplish a specific task. True collaboration, however, can only be realized when humans and robots can share the same workspace simultaneously and move freely within it. To address these problems, Learning from Demonstrations (LfD) helps robots become competent in solving plenty of complicated tasks, greatly reducing programming times and allowing task generalization. However, complex robot tasks require complex path planning modeling for a robot to move from one place to another in a heavily constrained workspace following a collision-free path. To this end, a robot programming framework based on Dynamic Movement Primitives (DMPs) is proposed. The framework derives and implements a solution for robot path planning and includes a new DMP formulation with volumetric obstacle avoidance for robot LfD. The formulation equips robotic systems with the capability of online adaptation in the presence of dynamic obstacles. Quantitative evaluations demonstrate high success rates (>96% in tested scenarios) in collision avoidance and typical trajectory adaptation times in the order of milliseconds (<5 ms), supporting its applicability. These methods have been applied in both simulation and real robotic scenarios using a UR10e collaborative robot from Universal Robots for testing and validation purposes. The results indicate that the proposed approach can effectively make the robot follow a user-defined trajectory and learn how to adapt it to avoid collisions with volumetric obstacles of different shapes and poses in an unconstrained human–robot collaborative environment.

Background: Droplet microfluidics uses immiscible phases to manipulate discrete volumes inside microchannels, and it is applied in synthesis of biomolecules, encapsulation, drug delivery and cell analysis. The use of aqueous two‐phase systems (ATPS) is a strategy for the fractionation of biomolecules. Here, we combined both techniques to fabricate a microfluidic device based on a polymer–salt ATPS, composed of polyethylene glycol (PEG) 8000 and phosphate salt buffer (PO ₄ ), capable of generating droplets and partitioning red fluorescent protein (RFP), used as a model of study. Partition behavior, droplet size and frequency were characterized, employing a polydimethylsiloxane microdevice for this purpose. Results: The microdevice exhibited either a two‐phase or droplet behavior in the absence and presence of RFP. In both scenarios, the model protein was partitioned towards the salt‐rich phase (PO ₄ ), as in traditional ATPS. In addition, by controlling fluid velocity, different regimes of droplet formation were observed, i.e. dripping and jetting. RFP was partitioned and encapsulated in both regimes. The microdevices exhibited a partition coefficient, , of 0.34 ± 0.016, which is comparable with that obtained in traditional systems ( = 0.39 ± 0.012). Droplet size diminished as , , increased. Droplet breakup time has the same effect when increasing velocity ratio, while frequency of droplet formation increased. COMSOL computational simulations were performed, exhibiting good agreement with experimental results. Conclusion: The microfluidic device proved to be a useful platform to generate droplets by using polymer PEG–salt ATPS under controlled conditions. Besides, it allowed partitioning and encapsulation of the model protein. This microfluidic platform presents a viable alternative for protein separation and encapsulation, which can further support high‐value‐biomolecule separation. © 2025 Society of Chemical Industry (SCI).

By observing the actions taken by operators, it is possible to determine the risk level of a work task. One method for achieving this is the recognition of human activity using biosignals and inertial measurements provided to a machine learning algorithm performing such recognition. The aim of this research is to propose a method to automatically recognize physical exertion and reduce noise as much as possible towards the automation of the Job Strain Index (JSI) assessment by using a motion capture wearable device (MindRove armband) and training a quadratic support vector machine (QSVM) model, which is responsible for predicting the exertion depending on the patterns identified. The highest accuracy of the QSVM model was 95.7%, which was achieved by filtering the data, removing outliers and offsets, and performing zero calibration; in addition, EMG signals were normalized. It was determined that, given the job strain index’s purpose, physical exertion detection is crucial to computing its intensity in future work.

Autonomous Vehicles (AVs) are a novel technology possessing a vast and diversified body of literature which requires in-depth studies to drive further advances. One crucial aspect of autonomous operation is achieving precise and reliable localization to enable safe navigation in different environments. This paper provides a concise overview of the state of the art in localization techniques for Autonomous Navigation Systems (ANS), focusing on summarizing the most significant concepts and methodologies based on a collection of previous comprehensive literature reviews. Different approaches for AVs localization are presented, followed by a custom categorization of prevailing techniques. Visual Odometry (VO) and Simultaneous Localization and Mapping (SLAM) are emphasized as the most promising localization methods. Challenges and research opportunities, including multi-sensor fusion and vehicular networks, are highlighted. The aim of this work is to shed light on the foundations essential attributes of localization methods, thereby facilitating further research in autonomous mobility projects.

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.