Exoskeletons are mechatronic systems worn by a person in such a way that the physical interface permits a direct transfer of mechanical power and exchange of information. Upper limb robotic exoskeletons may be helpful for people with disabilities and/or limb weakness or injury. Tremor is the most common movement disorder in neurological practice. In addition to medication, rehabilitation programs, and deep brain stimulation, biomechanical loading has appeared as a potential tremor suppression alternative. This paper introduces the robotic exoskeleton called WOTAS (wearable orthosis for tremor assessment and suppression) that provides a means of testing and validating nongrounded control strategies for orthotic tremor suppression. This paper describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components. Two control strategies developed for tremor suppression with exoskeletons are described. These two strategies are based on biomechanical loading and notch Altering the tremor through the application of internal forces. Results from experiments using these two strategies on patients with tremor are summarized. Finally, results from clinical trials are presented, which indicate the feasibility of ambulatory mechanical

There is a need for wearable powered upper limb exoskeletons able to apply forces to the upper limb for use by people with disabilities and/or limb weakness or injury. The robotic exoskeleton called WOTAS (Wearable Orthosis for Tremor Assessment and Suppression) presented in this paper will provide a means of testing non-grounded control strategies in order to help these people. For instance, biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. This article describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components.

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p < 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.