Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.

Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.