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Introduction: Integrating technology and active learning methods into Laboratory activities would be a transformative educational experience to familiarize physical therapy (PT) students with STEM backgrounds and STEM-based new technologies. However, PT students struggle with technology and feel comfortable memorizing under expositive lectures. Thus, we described the difficulties, uncertainties, and advances observed by faculties on students and the perceptions about learning, satisfaction, and grades of students after implementing laboratory activities in a PT undergraduate course, which integrated surface-electromyography (sEMG) and kinematic technology combined with active learning methods. Methods: Six cohorts of PT students (n = 482) of a second-year PT course were included. The course had expositive lectures and seven laboratory activities. Students interpreted the evidence and addressed different motor control problems related to daily life movements. The difficulties, uncertainties, and advances observed by faculties on students, as well as the students' perceptions about learning, satisfaction with the course activities, and grades of students, were described. Results: The number of students indicating that the methodology was "always" or "almost always," promoting creative, analytical, or critical thinking was 70.5% [61.0-88.0%]. Satisfaction with the whole course was 97.0% [93.0-98.0%]. Laboratory grades were linearly associated to course grades with a regression coefficient of 0.53 and 0.43 R-squared (p < 0.001). Conclusion: Integrating sEMG and kinematics technology with active learning into laboratory activities enhances students' engagement and understanding of human movement. This approach holds promises to improve teaching-learning processes, which were observed consistently across the cohorts of students.

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The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases.

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Cross talk is an important source of error in interpreting surface electromyography (EMG) signals. Here, we aimed at characterizing cross talk for three groups of synergistic muscles by the identification of individual motor unit action potentials. Moreover, we explored whether spatial filtering (single and double differential) of the EMG signals influences the level of cross talk. Three experiments were conducted. Participants (total 25) performed isometric contractions at 10% of the maximal voluntary contraction (MVC) with digit muscles and knee extensors and at 30% MVC with plantar flexors. High-density surface EMG signals were recorded and decomposed into motor unit spike trains. For each muscle, we quantified the cross talk induced to neighboring muscles and the level of contamination by the nearby muscle activity. We also estimated the influence of cross talk on the EMG power spectrum and intermuscular correlation. Most motor units (80%) generated significant cross-talk signals to neighboring muscle EMG in monopolar recording mode, but this proportion decreased with spatial filtering (50% and 42% for single and double differential, respectively). Cross talk induced overestimations of intermuscular correlation and has a small effect on the EMG power spectrum, which indicates that cross talk is not reduced with high-pass temporal filtering. Conversely, spatial filtering reduced the cross-talk magnitude and the overestimations of intermuscular correlation, confirming to be an effective and simple technique to reduce cross talk. This paper presents a new method for the identification and quantification of cross talk at the motor unit level and clarifies the influence of cross talk on EMG interpretation for muscles with different anatomy.NEW & NOTEWORTHY We proposed a new method for the identification and quantification of cross talk at the motor unit level. We show that surface EMG cross talk can lead to physiological misinterpretations of EMG signals such as overestimations in the muscle activity and intermuscular correlation. Cross talk had little influence on the EMG power spectrum, which indicates that conventional temporal filtering cannot minimize cross talk. Spatial filter (single and double differential) effectively reduces but not abolish cross talk.

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This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient's tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.

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Muscle fatigue is an important field of study in sports medicine and occupational health. Several studies in the literature have proposed methods for predicting muscle fatigue in isometric con-tractions using three states of muscular fatigue: Non-Fatigue, Transition-to-Fatigue, and Fatigue. For this, several features in time, spectral and time-frequency domains have been used, with good performance results; however, when they are applied to dynamic contractions the performance decreases. In this paper, we propose an approach for analyzing muscle fatigue during dynamic contractions based on time and spectral domain features, Permutation Entropy (PE) and biomechanical features. We established a protocol for fatiguing the deltoid muscle and acquiring surface electromiography (sEMG) and biomechanical signals. Subsequently, we segmented the sEMG and biomechanical signals of every contraction. In order to label the contraction, we computed some features from biomechanical signals and evaluated their correlation with fatigue progression, and the most correlated variables were used to label the contraction using hierarchical clustering with Ward's linkage. Finally, we analyzed the discriminant capacity of sEMG features using ANOVA and ROC analysis. Our results show that the biomechanical features obtained from angle and angular velocity are related to fatigue progression, the analysis of sEMG signals shows that PE could distinguish Non-Fatigue, Transition-to-Fatigue and Fatigue more effectively than classical sEMG features of muscle fatigue such as Median Frequency.

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BACKGROUND: Parkinson's disease is a neurological disorder that promotes motor changes in the body. OBJECTIVE: The aim of this study was to investigate the impairment of the stomatognathic function regarding molar bite force, electromyographic activity and thickness of the craniocervical muscles in patients with Parkinson's disease in comparison with those in asymptomatic controls. METHODS: Twenty-four subjects were divided into two groups, a Parkinson's disease group (n = 12) and a control group (n = 12). The subjects were evaluated on the basis of molar bite force, electromyographic activity (rest, right and left laterality, protrusion, maximum voluntary contraction) and thickness (rest and maximum voluntary contraction) of the right and left temporal (anterior portion), masseter and sternocleidomastoid muscles. The results were submitted to a multivariate analysis of variance (MANOVA) to compare the means of the two independent groups, considering diagnosis of Parkinson's disease and craniocervical muscles as independent variables. For the post hoc comparisons, Bonferroni correction was used (P < 0.05). RESULTS: Parkinson's disease group presented lower mean values both sides for maximal molar bite force, significant increases in the electromyographic activities during mandibular tasks, lower mean thickness values of the masseter and sternocleidomastoid muscles, and higher mean thickness values of the temporalis muscles (anterior portion). CONCLUSION: The results suggest that patients with Parkinson's disease may present functional changes of the stomatognathic system, related to bite force, electromyographic activity and thickness of the craniocervical muscles. The greater temporal muscle thickness in Parkinson's disease patients may compromise their daily life activities, especially with respect to chewing and nutrition.

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Resumen Los robots proporcionan nuevas formas de terapia para pacientes con desórdenes neurológicos. Las terapias de marcha asistidas con exoesqueletos pueden incrementar la duración y la intensidad de los entrenamientos para los pacientes y reducir el esfuerzo físico del terapeuta. Sin embargo, el uso de estos dispositivos para el entrenamiento de la marcha limita la interacción física entre el terapeuta y el paciente, en comparación con la terapia manual. Una apropiada realimentación de las funciones corporales y biomecánicas en la interacción con el sistema robótico facilita la evaluación del desempeño del paciente, motivándolo en el reaprendizaje de la marcha con resultados superiores. Este artículo presenta el diseño de una interfaz de usuario para un exoesqueleto de miembros inferiores para asistencia en la marcha y en terapias de rehabilitación. Se consideraron aspectos técnicos y clínicos para proporcionar ventajas del exoesqueleto durante las terapias, estableciendo una herramienta de apoyo para la configuración, monitoreo y registro de los parámetros involucrados. Se propuso un esquema de realimentación sensorial para el paciente acerca de la actividad muscular, la presión ejercida en diferentes puntos de los pies y algunas variables biomecánicas. Finalmente, se valida la herramienta con sujetos sanos por medio de un test de usabilidad propuesto.

Abstract The inclusion of robots in rehabilitation allow advantages for generate newer therapies in neurologic disorder patients. Assistive gait therapies using robots, like exoskeletons, allow increase the time and intensity training for patients while the strenuous labor of therapist is reduced. However, the physic interaction between therapist and patient in training with robots is limited, in relation to the traditional manual therapy. An appropriated feedback of biological and biomechanics functions in the robot interaction during training provides an easier performance evaluation of the patient for the therapist. Further, biofeedback gives a motivation to the patient and encourages him for gait relearning with higher effects than conventional. This paper presents a user interface design for a lower limb exoskeleton for human gait assistance in rehabilitation. Clinical and technical criteria for increasing the advantages of the exoskeleton in therapy were considered. A biofeedback scheme about muscle activity, plantar pressure and some biomechanics variables, for the patient is proposed. Finally, a validation for this tool with healthy subjects by a usability test was carried out.

Resumo A inclusão de robôs na reabilitação fornecem vantagens que promovem novas formas de terapia em pacientes com desordens neurológicas. Terapias de marcha assistidas por exoesqueletos permitem o aumento da duração e da intensidade dos exercícios com os pacientes, reduzindo o esforço físico dos terapeutas. Não entanto, o uso desses dispositivos para o treino da marcha limita a interação física entre o terapeuta e paciente, em comparação com a terapia manual. Uma apropriada realimentação das funções corporais e biomecânicas na interação com o sistema robótico facilita a avaliação do progresso do paciente, motiva e incentiva ao paciente na reaprendizagem da marcha gerando efeitos superiores aos convencionais. Neste artigo apresenta-se o desenho de uma interface de usuário para um exoesqueleto de membros inferiores para assistência na marcha e nas terapias de reabilitação. São considerados aspectos técnicos e clínicos para fornecer maiores vantagens do exoesqueleto durante as terapias, estabelecendo uma ferramenta de suporte para configuração, monitoramento e registro dos parâmetros envolvidos. Foi proposto um sistema de realimentação sensorial para o paciente sobre a atividade muscular, a pressão em diferentes pontos dos pés e algumas variáveis biomecânicas. Finalmente, é apresentada a ferramenta de validação para indivíduos saudáveis utilizando um teste de usabilidade proposto.

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Abstract Introduction: Stroke is a leading cause of neuromuscular system damages, and researchers have been studying and developing robotic devices to assist affected people. Depending on the damage extension, the gait of these people can be impaired, making devices, such as smart walkers, useful for rehabilitation. The goal of this work is to analyze changes in muscle patterns on the paretic limb during free and walker-assisted gaits in stroke individuals, through accelerometry and surface electromyography (sEMG). Methods The analyzed muscles were vastus medialis, biceps femoris, tibialis anterior and gastrocnemius medialis. The volunteers walked three times on a straight path in free gait and, further, three times again, but now using the smart walker, to help them with the movements. Then, the data from gait pattern and muscle signals collected by sEMG and accelerometers were analyzed and statistical analyses were applied. Results The accelerometry allowed gait phase identification (stance and swing), and sEMG provided information about muscle pattern variations, which were detected in vastus medialis (onset and offset; p = 0.022) and biceps femoris (offset; p = 0.025). Additionally, comparisons between free and walker-assisted gaits showed significant reduction in speed (from 0.45 to 0.30 m/s; p = 0.021) and longer stance phase (from 54.75 to 60.34%; p = 0.008). Conclusions Variations in muscle patterns were detected in vastus medialis and biceps femoris during the experiments, besides user speed reduction and longer stance phase when the walker-assisted gait is compared with the free gait.

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Abstract Professional cyclists often adopt a competition-start standing posture, which has been shown to improve performance. The biomechanical basis of this is unclear, and might be due to a greater mechanical advantage or increased key muscle activity. Previous observations in steady state cycling showed greater activation of the tibialis anterior, erector spinae, and biceps brachii when adopting a standing vs. seated-riding posture. Little is known regarding the effect of riding posture on activation during a standing start. Eleven cyclists performed standing starts in seated and standing-postures using stationary-cycle and on the track. Electromyography of the gastrocnemius medialis, tibialis anterior, erector spinae, and biceps brachii was recorded during first and subsequent pedal strokes. Results showed that the gastrocnemius medialis did not modify activity. The tibialis anterior, erector spinae, and biceps brachii activity was increased during the standing posture compared to seated, only during the first pedal stroke. These increased activation intensities were accompanied by a corresponding 10% increase in bike speed during the first 5 meters following a standing start in the standing posture compared to the seated one. Adopting a standing posture during a standing start improves performance through greater initial acceleration.

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The aging process leads to a gradual loss of muscle mass and muscle performance, leading to a higher functional dependence. Within this context, many studies have demonstrated the benefits of a combination of physical exercise and low level laser therapy (LLLT) as an intervention that enhances muscle performance in young people and athletes. The aim of this study was to evaluate the effects of combination of LLLT and strength training on muscle performance in elderly women. For this, a hundred elderly women were screened, and 48 met all inclusion criteria to participate in this double-blind placebo-controlled trial. Volunteers were divided in three groups: control (CG = 15), strength training associated with placebo LLLT (TG = 17), and strength training associated with active LLLT (808 nm, 100 mW, 7 J) (TLG = 16). The strength training consisted of knee flexion-extension performed with 80 % of 1-repetition maximum (1-RM) during 8 weeks. Several outcomes related to muscle performance were analyzed through the 6-min walk test (6-MWT), isokinetic dynamometry, surface electromyography (SEMG), lactate concentration, and 1-RM. The results revealed that a higher work (p = 0.0162), peak torque (p = 0.0309), and power (p = 0.0223) were observed in TLG compared to CG. Furthermore, both trained groups increased the 1-RM load (TG vs CG: p = 0.0067 and TLG vs CG: p < 0.0001) and decreased the lactate concentration in the third minute after isokinetic protocol (CG vs TLG: p = 0.0289 and CG vs TG: p = 0.0085). No difference in 6-MWT and in fatigue levels were observed among the groups. The present findings suggested that LLLT in combination with strength training was able to improve muscle performance in elderly people.

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BACKGROUND: Recently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders. Such schemes usually provide specific compressors, which are tuned for SEMG data, or employ preprocessing techniques, before the two-dimensional encoding procedure, in order to provide a suitable data organization, whose correlations can be better exploited by off-the-shelf encoders. Besides preprocessing input matrices, one may also depart from those approaches and employ an adaptive framework, which is able to directly tackle SEMG signals reassembled as images. METHODS: This paper proposes a new two-dimensional approach for SEMG signal compression, which is based on a recurrent pattern matching algorithm called multidimensional multiscale parser (MMP). The mentioned encoder was modified, in order to efficiently work with SEMG signals and exploit their inherent redundancies. Moreover, a new preprocessing technique, named as segmentation by similarity (SbS), which has the potential to enhance the exploitation of intra- and intersegment correlations, is introduced, the percentage difference sorting (PDS) algorithm is employed, with different image compressors, and results with the high efficiency video coding (HEVC), H.264/AVC, and JPEG2000 encoders are presented. RESULTS: Experiments were carried out with real isometric and dynamic records, acquired in laboratory. Dynamic signals compressed with H.264/AVC and HEVC, when combined with preprocessing techniques, resulted in good percent root-mean-square difference [Formula: see text] compression factor figures, for low and high compression factors, respectively. Besides, regarding isometric signals, the modified two-dimensional MMP algorithm outperformed state-of-the-art schemes, for low compression factors, the combination between SbS and HEVC proved to be competitive, for high compression factors, and JPEG2000, combined with PDS, provided good performance allied to low computational complexity, all in terms of percent root-mean-square difference [Formula: see text] compression factor. CONCLUSION: The proposed schemes are effective and, specifically, the modified MMP algorithm can be considered as an interesting alternative for isometric signals, regarding traditional SEMG encoders. Besides, the approach based on off-the-shelf image encoders has the potential of fast implementation and dissemination, given that many embedded systems may already have such encoders available, in the underlying hardware/software architecture.

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