Currently, research on gesture recognition systems has been on the rise due to the capabilities these systems provide to the field of human–machine interaction, however, gesture recognition in prosthesis and orthesis has been carried out through the use of an extensive amount of channels and electrodes to acquire the EMG (Electromyography) signals, increasing the cost and complexity of these systems. The scientific literature shows different approaches related to gesture recognition based on the analysis of EMG signals using deep learning models, highlighting the recurrent neural networks with deep learning structures. This paper presents the implementation of a Recurrent Neural Network (RNN) model using Long-short Term Memory (LSTM) units and dense layers to develop a gesture classifier for hand prosthesis control, aiming to decrease the number of EMG channels and the overall model complexity, in order to increase its scalability for embedded systems. The proposed model requires the use of only four EMG channels to recognize five hand gestures, greatly reducing the number of electrodes compared to other approaches found in the literature. The proposed model was trained using a dataset for each gesture EMG signals, which were recorded for 20 s using a custom EMG armband. The model reached an accuracy of to 99% for the training and validation stages, and an accuracy of 87 ± 7% during real-time testing. The results obtained by the proposed model establish a general methodology for the reduction of complexity in the recognition of gestures intended for human.machine interaction for different computational devices.

This review investigates the latest advancements in Multi-Robot Systems (MRSs) and soft robotics, with a particular focus on their integration and emerging opportunities. An MRS extends principles from distributed artificial intelligence and coordination frameworks, enabling efficient collaboration in robotic applications such as object manipulation, navigation, and transportation. Soft robotics employs flexible materials and biomimetic designs to improve adaptability in unstructured environments, with applications in manufacturing, sensing, actuation, and modeling. Unlike previous reviews, which often address these fields independently, this work emphasizes their integration, identifying key challenges such as nonlinear dynamics, hyper-redundant configurations, and adaptive control. This review discusses recent advancements in locomotion, coordination, and simulation, offering insights into the development of adaptive and collaborative robotic systems across diverse applications.

Soft robotics have broken the rigid wall of interaction between humans and robots due to their own definition and manufacturing principles, allowing robotic systems to adapt to humans and enhance or restore their capabilities. In this research we propose a dexterous bioinspired soft active hand prosthesis based in the skeletal architecture of the human hand. The design includes the imitation of the musculoskeletal components and morphology of the human hand, allowing the prosthesis to emulate the biomechanical properties of the hand, which results in better grips and a natural design. CAD models for each of the bones were developed and 3D printing was used to manufacture the skeletal structure of the prosthesis, also soft materials were used for the musculoskeletal components. A myoelectric control system was developed using a recurrent neural network (RNN) to classify the hand gestures using electromyography signals; the RNN model achieved an accuracy of 87% during real time testing. Objects with different size, texture and shape were tested to validate the grasping performance of the prosthesis, showing good adaptability, soft grasping and mechanical compliance to object of the daily life.

This paper presents a new soft robotic hand exoskeleton for rehabilitation and movement assistance. The proposed device is based on a novel design of a PneuNet‐type pneumatic actuator; this novel actuator design allows larger bending motions while requiring less operating pressure; additionally, the actuator has an embedded sensor that allows an accurate trajectory tracking and control. The proposed design allows safe and controlled flexion movements for each one of the fingers, which is essential for effective rehabilitation and assistance tasks. The exoskeleton effectiveness was assessed by a physical therapist, and a modified version of the Sollerman test was used. Results obtained from the evaluation procedures provided insights into the exoskeleton performance and its efficacy. Preliminary conclusions are drawn based on the findings, and potential areas for further improvement are discussed. This research contributes to the development of an advanced soft robotic hand exoskeleton for rehabilitation and movement assistance, offering controlled flexion movements and improved functionality. This study demonstrates the potential of soft actuator technology in rehabilitation, presenting new possibilities for hand trauma recovery.

Soft robotic exoskeletons have emerged as a transformative solution for rehabilitation and assistance, offering greater adaptability and comfort than rigid designs. Myoelectric control, based on electromyography (EMG) signals, plays a key role in enabling intuitive and adaptive interaction between the user and the exoskeleton. This review analyzes recent advancements in myoelectric control strategies, emphasizing their integration into soft robotic exoskeletons. Unlike previous studies, this work highlights the unique challenges posed by the deformability and compliance of soft structures, requiring novel approaches to motion intention estimation and control. Key contributions include critically evaluating machine learning-based motion prediction, model-free adaptive control methods, and real-time validation strategies to enhance rehabilitation outcomes. Additionally, we identify persistent challenges such as EMG signal variability, computational complexity, and the real-time adaptability of control algorithms, which limit clinical implementation. By interpreting recent trends, this review highlights the need for improved EMG acquisition techniques, robust adaptive control frameworks, and enhanced real-time learning to optimize human-exoskeleton interaction. Beyond summarizing the state of the art, this work provides an in-depth discussion of how myoelectric control can advance rehabilitation by ensuring more responsive and personalized exoskeleton assistance. Future research should focus on refining control schemes tailored to soft robotic architectures, ensuring seamless integration into rehabilitation protocols. This review is a foundation for developing intelligent soft exoskeletons that effectively support motor recovery and assistive applications.