Development of the Biomech-Wrist: A 3-DOF Exoskeleton for Rehabilitation and Training of Human Wrist.

This work describes a three-degrees-of-freedom rehabilitation exoskeleton robot for wrist articulation movement: the Biomech-Wrist. The proposed development includes the design requirements based on the biomechanics and anthropometric features of the upper limb, the mechanical design, electronic instrumentation, software design, manufacturing, control algorithm implementation, and the experimental setup to validate the functionality of the system. The design requirements were set to achieve human wrist-like movements: ulnar-radial deviation, flexion-extension, and pronation-supination. Then, the mechanical design considers the human range of motion with proper torques, velocities, and geometry. The manufacturing consists of 3D-printed elements and tubular aluminum sections resulting in lightweight components with modifiable distances. The central aspect of the instrumentation is the actuation system consisting of three brushless motors and a microcontroller for the control implementation. The proposed device was evaluated by considering two control schemes to regulate the trajectory tracking on each joint. The first scheme was the conventional proportional-derivative controller, whereas the second was proposed as a first-order sliding mode. The results show that the Biomech-Wrist exoskeleton can perform trajectory tracking with high precision ( RMSEmax = 0.0556 rad) when implementing the sliding mode controller.

Forearm sEMG data from young healthy humans during the execution of hand movements.

This work provides a complete dataset containing surface electromyography (sEMG) signals acquired from the forearm with a sampling frequency of 1000 Hz. The dataset is named WyoFlex sEMG Hand Gesture and recorded the data of 28 participants between 18 and 37 years old without neuromuscular diseases or cardiovascular problems. The test protocol consisted of sEMG signals acquisition corresponding to ten wrist and grasping movements (extension, flexion, ulnar deviation, radial deviation, hook grip, power grip, spherical grip, precision grip, lateral grip, and pinch grip), considering three repetitions for each gesture. Also, the dataset contains general information such as anthropometric measures of the upper limb, gender, age, laterally of the person, and physical condition. Likewise, the implemented acquisition system consists of a portable armband with four sEMG channels distributed equidistantly for each forearm. The database could be used for the recognition of hand gestures, evaluation of the evolution of patients in rehabilitation processes, control of upper limb orthoses or prostheses, and biomechanical analysis of the forearm.

Home-care nursing controlled mobile robot with vital signal monitoring.

This study describes the development (design, construction, instrumentation, and control) of a nursing mobile robotic device to monitor vital signals in home-cared patients. The proposed device measures electrocardiography potentials, oxygen saturation, skin temperature, and non-invasive arterial pressure of the patient. Additionally, the nursing robot can supply assistance in the gait cycle for people who require it. The robotic device's structural and mechanical components were built using 3D-printed techniques. The instrumentation includes electronic embedded devices and sensors to know the robot's relative position with respect to the patient. With this information together with the available physiological measurements, the robot can work in three different scenarios: (a) in the first one, a robust control strategy regulates the mobile robot operation, including the tracking of the patient under uncertain working scenarios leading to the selection of an appropriate sequence of movements; (b) the second one helps the patients, if they need it, to perform a controlled gait-cycle during outdoors and indoors excursions; and (c) the third one verifies the state of health of the users measuring their vital signs. A graphical user interface (GUI) collects, processes, and displays the information acquired by the bioelectrical amplifiers and signal processing systems. Moreover, it allows easy interaction between the nursing robot, the patients, and the physician. The proposed design has been tested with five volunteers showing efficient assistance for primary health care. Graphical Abstract Main stages of the home-care nursing controlled mobile robot.

Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements.

Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section's diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.

Adaptive sliding-mode trajectory tracking control for state constraint master-slave manipulator systems.

This study aims to propose an adaptive state-dependent gain finite-time convergent controller (using the fundamentals of the sliding mode theory) that solves the trajectory tracking for a class of state constraint master-slave robotic system (M-SRS) formed by two manipulators with the same number of articulations. The control design considers the effect of state constraints by implementing a state dependent adaptive gain. A Lyapunov-stability analysis leads to design the gain variation laws yielding proving the finite-time convergence of the sliding surface as well as the asymptotic convergence of the tracking error. The state constraints of the slave system motivate the characterization of the convergence-time as a function of the bounded uncertainties affecting the M-SRS dynamics. The forward-complete setting of the M-SRS justified the application of a robust and exact differentiator which estimated the articulation velocities for the slave robot. The estimated velocities are used as part of the realization of the output feedback controller. Numerical simulations demonstrate that the proposed control scheme provides a smaller quadratic norm of the tracking error compared with the obtained with other controllers (proportional-derivative and conventional sliding modes). The proposed control approach satisfies the state constraints while the sliding manifold converges to the origin in finite-time as justified by the theoretical stability analysis.

Non-singular terminal sliding-mode control for a manipulator robot using a barrier Lyapunov function.

This study introduces a design of robust finite-time controllers that aims to solve the trajectory tracking of robot manipulators with full-state constraints. The control design is based on the construction of a distributed state constraint non-singular terminal sliding mode (CNTSM). The CNTSM design includes the gain self-adapting tuning method, which can ensure finite-time convergence to the sliding surface aside from the states to its corresponding reference trajectories. The implementation of the time-varying gain ensures the fulfillment of the accurate tracking for the references while the position and velocity constraints are satisfied permanently. A barrier Lyapunov function is proposed to develop the finite-time stability analysis of the designed controllers. The CNTSM realization uses the tracking error as well as its estimated derivative, which is calculated using a variant of adaptive super-twisting algorithm operating as robust differentiator. The proposed CNTSM is numerically evaluated on a two-link RM with uncertain inertia and Coriolis matrices. Simulation and experimental results evidence the efficiency of the CNTSM controller demonstrating a better tracking performance while the full-state constraints are satisfied in counterpart with the classical non-singular terminal sliding mode which is not able to keep such restrictions.

Musculoskeletal Neural Network path generator for a virtual upper-limb active controlled orthosis.

In this paper, a non-parametric model of the neuromusculoskeletal system for the biceps brachii is presented. The model serves to generate angular paths for the control of a virtual active orthosis. The path generator uses a differential neural network (DNN) identifier that obtains the reference angular position and velocities using the raw electromyographic (EMG) signals as input. The model is validated using experimental data. The training and closed-loop implementation of the proposed model are described. The control strategy ensures that the user reaches a set-point with a predefined position constraint and that the device follows the natural reference path that corresponds to the raw EMG signal.

Adaptive output control of a mobile manipulator hanging from a quadcopter unmanned vehicle.

This article summarizes the comprehensive design of an adaptive control that solves the trajectory tracking of a four-rotor unmanned aerial vehicle supplied by an own-designed grasping device. The controller design uses the regular proportional-derivative (PD) structure which is common to solve regulation, as well as tracking problems for robotic systems. The PD structure needs the estimation of the time-derivative to be exerted on-line. The super-twisting algorithm served to operate as a decentralized estimator of the derivatives for the tracking errors. The controller gains were adjusted by the differential laws aimed to track attainable reference trajectories. The adaptive strategy adjusts the controller gains enforcing the convergence of both the estimation and the tracking errors. A numerical example showed the observer/controller performance. A comparison with a classic non-adaptive PD controller confirms the effectiveness on the tracking task by the proposed design. In addition, a set of virtual numerical evaluations using the parameters of a real quadcopter system confirmed the performance benefits of the adaptive controller based on the PD structure aided with the estimation of the velocity obtained by the observer.

Integrated wearable and self-carrying active upper limb orthosis.

The aim of this study was to develop a prototype of an orthotic system that can be used as a support tool in the rehabilitation of the upper limb. The construction of this device was motivated by the increasing number of subjects suffering from full or partial loss of the upper limb function as a consequence of spinal cord injuries, strokes, occupational syndromes and sports injuries. The majority of procedures used in upper limb rehabilitation consist of repetitive movements enforced by physiotherapists; a robotic device executing the same tasks seems to be a plausible solution if the orthosis can be programmed and controlled automatically. This study reports the mechanical design, electronic instrumentation and automatic control of an upper limb orthosis made of plastic polymer that makes the orthosis a wearable and self-carrying device. The orthosis consisted of a mechatronic device with five joints. The pieces made by a three-dimensional plastic printer were used to construct the device leading to a total weight of 2.6 kg. The application of a robust automatic controller based on the sliding-mode theory forces the movement of the arm, while taking into account the constraints in each angular displacement of the orthosis. A set of reference trajectories designed to represent the usual movements of a healthy upper limb served for evaluating the controller execution. The orthosis was tested on 15 volunteers with a maximum experimental steady-state error of 2% in the angular deviation of all articulations with respect to their reference trajectories.