Muscle Tone Assessment by Machine Learning Using Surface Electromyography.

Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity since some suggest it is related to a state of preparation for movement. Muscle tone is primarily regulated by the central nervous system, and individuals with neurological disorders may lose the ability to control normal tone and can exhibit abnormalities. Currently, these abnormalities are mostly evaluated using subjective scales, highlighting a lack of objective assessment methods in the literature.

WheelSimPhysio-2023 dataset: Physiological and questionnaire-based dataset of immersive multisensory wheelchair simulator from 58 participants.

This data paper presents a unique multimodal dataset collected from a comprehensive experiment using a wheelchair training simulator. The dataset consists of quantitative and qualitative data that represents the user's experience and performance. Participants engaged in a series of navigational tasks in a simulated environment under two distinct system configuration conditions: a.

Analysis of ankle muscle activity: A study on static balance with eyes closed and high-heeled shoes.

Background: Changes in sensory afferent interfere with the control of postural stability by the central nervous system. Wearing high-heeled shoes is an example of an external disturbance that changes sensory inputs and results in several postural adjustments to control stability. Thus, our purpose is to investigate the influence of high-heeled shoes and visual absence on maintenance of static balance and on ankle muscle activity among young women.

Post-stroke functional assessments based on rehabilitation games and their correlation with clinical scales: A scoping review.

Considering that stroke is one of the main causes of adult impairment and the growing interest in Virtual Reality (VR) as a potential assessment and treatment tool for the rehabilitation of stroke patients, a scoping review was conducted to check whether user's motion data obtained from VR games and simulations can be clinically valid. This was done by reviewing studies on parameters for assessing the functional skills and rehabilitation progress using data from VR games or simulations. Then, identifying the most widely used and validated parameters for the quantification of motor ability in a virtual environment and suggesting challenges for future research.

Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density.

Similar to short-term memory, working memory cannot hold information for a long period of time. Studies have shown that binaural beats (BB) can stimulate the brain through sound, affecting working memory function. Although the literature is not conclusive regarding the effects of BB stimulation (stim) on memory, some studies have shown that gamma-BB stim (40 Hz) can increase attentional focusing and improve visual working memory.

Wrist Rigidity Evaluation in Parkinson's Disease: A Scoping Review.

(1) Background: One of the main cardinal signs of Parkinson's disease (PD) is rigidity, whose assessment is important for monitoring the patient's recovery. The wrist is one of the joints most affected by this symptom, which has a great impact on activities of daily living and consequently on quality of life. The assessment of rigidity is traditionally made by clinical scales, which have limitations due to their subjectivity and low intra- and inter-examiner reliability.

Virtual reality and serious game therapy for post-stroke individuals: A preliminary study with humanized rehabilitation approach protocol.

Objective: The current study proposed the development and preliminary validation of a humanized training approach for upper limb rehabilitation of chronic post-stroke individuals, using serious game (SG) and virtual reality (VR) technologies.

Materials And Methods: Ten individuals with chronic stroke participated in the study. Accompanied by a health professional, 15 sessions of the SG were performed in a laboratory, in a humanized way, lasting between 30 and 45 min each.

Multidimensional Assessment of Individuals with Parkinson's Disease: Development and Structure Validation of a Self-Assessment Questionnaire.

(1) Background: Several instruments are used to assess individuals with Parkinson's disease (PD). However, most instruments necessitate the physical presence of a clinician for evaluation, were not designed for PD, nor validated for remote application. (2) Objectives: To develop and validate a self-assessment questionnaire that can be used remotely, and to assess the respondents' health condition.

Skills assessment metrics of electric powered wheelchair driving in a virtual environment: a survey.

The purpose of this review is to present studies on the parameters for assessing the skills of users of electric wheelchair driving simulators in a virtual environment. In addition, this study also aims to identify the most widely used and validated parameters for the quantification of electric wheelchair driving ability in a virtual environment and to suggest challenges for future research. To carry out this research, the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were adopted.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review.

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual's interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user's attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications.

Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic.

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus.

Measurement of post-stroke spasticity based on tonic stretch reflex threshold: implications of stretch velocity for clinical practice.

Purpose: The most commonly used method for the clinical evaluation of spasticity is the modified Ashworth scale (MAS), which is subjective. In this regard, the spasticity assessment through the tonic stretch reflex threshold, which is an objective method, has emerged as an alternative. It is based on the value of the dynamic stretch reflex threshold, which is measured at different stretch velocities.

An improved approach for measuring the tonic stretch reflex response of spastic muscles.

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').

Alternative communication systems for people with severe motor disabilities: a survey.

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.

[Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima].

Introduction: Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources.

A review on techniques for tremor recording and quantification.

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.