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Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom (DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1 ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.

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Stroke is a debilitating clinical condition resulting from a brain infarction or hemorrhage that poses significant challenges for motor function restoration. Previous studies have shown the potential of applying transcranial direct current stimulation (tDCS) to improve neuroplasticity in patients with neurological diseases or disorders. By modulating the cortical excitability, tDCS can enhance the effects of conventional therapies. While upper-limb recovery has been extensively studied, research on lower limbs is still limited, despite their important role in locomotion, independence, and good quality of life. As the life and social costs due to neuromuscular disability are significant, the relatively low cost, safety, and portability of tDCS devices, combined with low-cost robotic systems, can optimize therapy and reduce rehabilitation costs, increasing access to cutting-edge technologies for neuromuscular rehabilitation. This study explores a novel approach by utilizing the following processes in sequence: tDCS, a motor imagery (MI)-based brain-computer interface (BCI) with virtual reality (VR), and a motorized pedal end-effector. These are applied to enhance the brain plasticity and accelerate the motor recovery of post-stroke patients. The results are particularly relevant for post-stroke patients with severe lower-limb impairments, as the system proposed here provides motor training in a real-time closed-loop design, promoting cortical excitability around the foot area (Cz) while the patient directly commands with his/her brain signals the motorized pedal. This strategy has the potential to significantly improve rehabilitation outcomes. The study design follows an alternating treatment design (ATD), which involves a double-blind approach to measure improvements in both physical function and brain activity in post-stroke patients. The results indicate positive trends in the motor function, coordination, and speed of the affected limb, as well as sensory improvements. The analysis of event-related desynchronization (ERD) from EEG signals reveals significant modulations in Mu, low beta, and high beta rhythms. Although this study does not provide conclusive evidence for the superiority of adjuvant mental practice training over conventional therapy alone, it highlights the need for larger-scale investigations.

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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.

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In this work, a visual interface for the assistance of a robotic wheelchair's navigation is presented. The visual interface is developed for the navigation in confined spaces such as narrows corridors or corridor-ends. The interface performs two navigation modus: non-autonomous and autonomous. The non-autonomous driving of the robotic wheelchair is made by means of a hand-joystick. The joystick directs the motion of the vehicle within the environment. The autonomous driving is performed when the user of the wheelchair has to turn (90, 90 or 180 degrees) within the environment. The turning strategy is performed by a maneuverability algorithm compatible with the kinematics of the wheelchair and by the SLAM (Simultaneous Localization and Mapping) algorithm. The SLAM algorithm provides the interface with the information concerning the environment disposition and the pose -position and orientation-of the wheelchair within the environment. Experimental and statistical results of the interface are also shown in this work.

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This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual. The adaptation training strategy is based on incremental versions of the expectation-maximization, segmental k-means and Bayesian approaches. Performance of the training methods was assessed through experiments considering the QT and ST-T databases. The results obtained show that the incremental training improves beat segmentation and ischemia detection performance with the advantage of low computational effort.

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BACKGROUND: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. RESULTS: Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. CONCLUSION: Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

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