Personalized Touch-Based Exergame System for Unilateral and Bilateral Rehabilitation Training

Background: Natural and intuitive interfaces that monitor and promote upper limb task-specific training need to be developed. This article presents the development and testing of a touch-based game system for training and assessment of unilateral (ULR) and bilateral (BLR) reaching movements. Interaction becomes intuitive and simple by introducing in-game touch and pressure onto virtual targets projected on a custom-made large touch panel. Materials and Methods: A custom-made App integrates exergames and a biomechanical model with advanced algorithms for movement analysis. It processes and manages data from a motion-tracking sensor and a large touch panel equipped with 1222 (26 × 47) piezoresistive sensors, including high-speed readout electronics and algorithms to measure touch points and contact forces during fingertip interaction. An experiment was conducted to evaluate the experience, motivation, and movements of healthy and stroke subjects when interacting with the proposed system. The panel height, dispersion of virtual targets, and required contact force were customized based on motor skills of each group of subjects. Results: Both groups of subjects showed high level of motivation and user experience when interacting with the virtual environments. Stroke subjects performed the task slower and traveled a similar path length than healthy subjects, but with shorter range of motion. The mechanical work and potential energy profiles of both groups are consistent with those achieved when reaching real objects. Conclusions: The proposed contact-based exergames are a feasible solution for performing natural and intuitive therapeutic ULR and BLR exercises. They elicit appropriate reaching movements and contact forces in healthy and stroke subjects. The spatial and temporal attributes of the proposed solution can be customized to influence the movement and energy expenditure of specific joints.

Computing Pressure-Deformation Maps for Braided Continuum Robots

This paper presents a method for computing sensorimotor maps of braided continuum robots driven by pneumatic actuators. The method automatically creates a lattice-like representation of the sensorimotor map that preserves the topology of the input space by arranging its nodes into clusters of related data. Deformation trajectories can be simply represented with adjacent nodes whose values smoothly change along the lattice curve; this facilitates the computation of controls and the prediction of deformations in systems with unknown mechanical properties. The proposed model has an adaptive structure that can recalibrate to cope with changes in the mechanism or actuators. An experimental study with a robotic prototype is conducted to validate the proposed method.

Biomechanical and functional effects of shoulder kinesio taping® on cerebral palsy children interacting with virtual objects

The reaching of objects is usually practiced by CP children in conventional or Virtual Reality-based therapies to enhance motor skill performance. Recently, Kinesio Taping® method has been studied to increase mechanical stability and improve functional movement of the upper limb; however, its influence on CP children´s upper limb motion has been rarely quantified due to lack of sensory measurement. Therefore, in this paper, we evaluate the biomechanical and functional effects of applying shoulder Kinesio Taping® on CP children in the reaching-transporting of virtual objects, by using a low-cost tracking device, exact robust differentiation of data and a simple nonlinear biomechanical dynamic model of the trunk and arm.

Characterization and classification of kinesthetic motor imagery levels

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

HRpI System Based on Wavenet Controller with Human Cooperative-in-the-Loop for Neurorehabilitation Purposes

There exist several methods aimed at human-robot physical interaction (HRpI) to provide physical therapy in patients. The use of haptics has become an option to display forces along a given path so as to it guides the physiotherapist protocol. Critical in this regard is the motion control for haptic guidance to convey the specifications of the clinical protocol. Given the inherent patient variability, a conclusive demand of these HRpI methods is the need to modify online its response with neither rejecting nor neglecting interaction forces but to process them as patient interaction. In this paper, considering the nonlinear dynamics of the robot interacting bilaterally with a patient, we propose a novel adaptive control to guarantee stable haptic guidance by processing the causality of patient interaction forces, despite unknown robot dynamics and uncertainties. The controller implements radial basis neural network with daughter RASP1 wavelets activation function to identify the coupled interaction dynamics. For an efficient online implementation, an output infinite impulse response filter prunes negligible signals and nodes to deal with overparametrization. This contributes to adapt online the feedback gains of a globally stable discrete PID regulator to yield stiffness control, so the user is guided within a perceptual force field. Effectiveness of the proposed method is verified in real-time bimanual human-in-the-loop experiments.