Tremor is the most common movement disorder and differs from other disorders by its repetitive, stereotyped movements, with regular frequency and amplitude. The three most frequent pathological forms of it are the essential tremor (ET), the Parkinson's disease (PD) tremor, and the enhanced physiological tremor. The ET and PD tremor affect the older population mostly. Although there are cases of tremor reported since ancient times, there is currently no consensus about its causes or about its main differential characteristics. In this article, we present a review of the methods more frequently used in measurement and analysis of tremor and the difficulties encountered in the research for the identification of methodologies that allow a significant advance in the study of tremor.

We have now sufficient evidence that using electrical biosignals in the field of Alternative and Augmented Communication is feasible. Additionally, they are particularly suitable in the case of people with severe motor impairment, e.g. people with high-level spinal cord injury or with locked-up syndrome. Developing solutions for them implies that we find ways to use sensors that fit the user's needs and limitations, which in turn impacts the specifications of the system translating the user's intentions into commands. After devising solutions for a given user or profile, the system should be evaluated with an appropriate method, allowing a comparison with other solutions. This paper submits a review of the way three bioelectrical signals - electromyographic, electrooculographic and electroencephalographic - have been utilised in alternative communication with patients suffering severe motor restrictions. It also offers a comparative study of the various methods applied to measure the performance of AAC systems.

We propose a new method for detecting the onset of the stretch reflex response for assessment of spasticity based on the Tonic Stretch Reflex Threshold (TSRT). Our strategy relies on a three-stage approach to detect the onset of the reflex EMG activity: (i) Reduction of baseline activity by means of Empirical Mode Decomposition; (ii) Extraction of the complex envelope of the EMG signal by means of Hilbert Transform (HT) and; iii) A double threshold decision rule. Simulated and real EMG data were used to evaluate and compare our method (TSRT-EHD) against three other popular methods described in the literature to assess TSRT ('Kim', 'Ferreira' and 'Blanchette'). Four different groups of signals containing simulated evoked stretch reflex EMG activities were generated: groups A and B without spontaneous EMG activity at rest and signal-to-noise ratio (SNR) of 10dB and 20dB respectively; groups C and D with spontaneous EMG activity at rest, as observed frequently in spastic muscles, and SNR of 10dB and 20dB respectively. The results with simulated data showed a significantly higher accuracy of TSRT-EHD for detecting the onset of the reflex EMG activity in groups C and D when compared to the other methods. Analyses using real data from five post stroke spastic subjects demonstrated that the TSRTs generated by each method were dramatically different from one another. Nevertheless, only TSRT-EHD provided valid measures across all subjects.

International audienceAssistive technology is dedicated to people who suffers from disabilities or pathologies that limit their daily life. It provides support in alternative communication, accessibility, mobility and cognitive domains. An example of this type of technology is the electric wheelchair that allows the person to move more freely and independently. However, most models are operated with a joystick, limiting their use to those who can manipulate it. Electromyographic signals can be used as an alternative method for controlling the vehicle by people with severe motor disabilities. Virtual simulators are good tools for testing different types of control and teaching future users how to drive it before moving on to a real model. This paper presents an electromyography system adapted for controlling a virtual electric wheelchair simulator by using facial muscles. The designed system was successfully tested by a volunteer in a pilot study, providing an alternative way for controlling a wheelchair by users who can't operate a joystick

The results showed that the stretch protocols were not all the same and that the method of increasing was most suitable for performing manual passive stretches to evaluate TSRT in these patients. Another analysis was the correlation between MAS and tonic stretch reflex threshold; a weak correlation was observed between the increasing and decreasing methods, and moderate correlation was observed between the random methods. Implications for Rehabilitation We demonstrated that the protocol of execution of passive stretches influences in the measurement of the tonic stretch reflex threshold (TSRT). We recommend the method of increasing velocity for performing manual passive stretches. We also build software with a reliable biological data acquisition system, which makes acquisition and processing of data in real time. In this way, the TSRT is a promising quantitative measure to assess post-stroke spasticity, calculated automatically. We also we provided the use of portable instruments to facilitate the assessment of spasticity in clinical practice.

Introdução: A malária é uma doença endêmica na Amazônia Legal Brasileira, apresentando riscos diferentes para cada região. O Município de Cantá, no Estado de Roraima, apresentou para todo o período estudado, um dos maiores índices parasitários anuais do Brasil, com valor sempre maior que 50. O presente estudo visa à utilização de uma rede neural artificial para previsão da incidência da malária nesse município, a fim de auxiliar os coordenadores de saúde no planejamento e gestão dos recursos.

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The choice of a good topology for a deep neural network is a complex task, essential for any deep learning project. This task normally demands knowledge from previous experience, as the higher amount of required computational resources makes trial and error approaches prohibitive. Evolutionary computation algorithms have shown success in many domains, by guiding the exploration of complex solution spaces in the direction of the best solutions, with minimal human intervention. In this sense, this work presents the use of genetic algorithms in deep neural networks topology selection. The evaluated algorithms were able to find competitive topologies while spending less computational resources when compared to state-of-the-art methods.