Marco Aurélio Benedetti Rodrigues

Hydrotherapy is widely used in rehabilitation because it reduces mechanical loading while preserving neuromuscular and cardiovascular stimulation. However, the biomechanical characterization of deep-water running remains limited, particularly when using accessible wearable systems for cycle-based movement analysis. This study aimed to evaluate the concurrent validity and agreement of a low-cost accelerometry device for cycle-based analysis of deep-water running, using a commercial accelerometry system as the reference measurement system. Twenty-one healthy participants performed a 25 m deep-water running task with simultaneous data acquisition from mechanically coupled sensors to ensure alignment. A total of 75 synchronized cycles were processed using a standardized pipeline that included filtering, synchronization, cycle detection, and parameter extraction. Statistical analysis was conducted using the Wilcoxon signed-rank test, intraclass correlation coefficient, Spearman’s correlation, Bland–Altman analysis, and error metrics. The results showed good agreement for temporal and volumetric variables, including cycle duration (ICC = 0.84), cumulative acceleration (ICC = 0.82), and area under the curve (ICC = 0.68). However, lower agreement and systematic bias were observed for intensity-related variables, particularly RMS and peak acceleration, despite more than 92% of cycles falling within the 95% limits of agreement (LoA). These findings suggest that the proposed device provides acceptable agreement for temporal and volumetric variables during deep-water running and may represent a low-cost alternative for movement monitoring in aquatic environments. However, intensity-related variables should be interpreted with caution due to the systematic differences observed between systems.

This study applies phase plane analysis of medio-lateral, anteroposterior, and vertical directions for the coordination assessment of whole-body (WB) center of mass (COM) movement during the impulse phase of a standard maximum vertical jump (MVJ) with long, short, and no countermovement (CM). A video system and force platform were used, with the amplitudes of WB COM excursion obtained from image-based motion capture at each anatomical direction, and the 2D and 3D mean radial distance were compared under long, short, and no CM conditions. The estimate of the population mean length was used as a measure of distribution concentration, and the Rayleigh statistical test for circular data was applied with the sample distribution critical value. Watson’s U² goodness-of-fit test for the von Mises distribution was used with the mean direction and concentration factor. The applied metrics led to the detection of shared and specific features in the global and phase plane analysis of WB COM movement coordination in the medio-lateral, anteroposterior, and vertical directions during long, short, and no CM conditions in relation to MVJ performance assessed from ground reaction force (GRF) through the force platform. Thus, long, short, and no CM impulses share lower amplitudes of WB COM excursion in the medio-lateral direction and mean radial distance to its mean, whereas the anteroposterior and vertical excursion of WB COM, along with the 2D transversal and 3D spatial length of the WB COM path, present as potential predictors of MVJ performance, with distinct behavior in long CM compared to short and no CM. Additionally, the applied workflow on generalized phase plane analysis led to the detection, through complementary metrics, of the anatomical WB COM movement directions with higher coordination based on phase concentration tests at 5% significance, in line with MVJ performance under different CM conditions.

Background: A cerebral vascular accident (CVA) is an event that compromises the central nervous system (CNS), leading to functional limitations. Virtual Reality (VR) has been proposed as a novel technological tool for rehabilitation, as it extends beyond treatment, providing motivation to patients. This study aims to evaluate the evidence of gait and balance rehabilitation using Virtual Reality (VR) in a population of adults and elderly individuals post-stroke in personalized virtual environments and commercially available gaming systems. Methods: The databases used in this study were selected as major research sources in the health and engineering fields. They include Pubmed, IEEE, PMC, Sciencedirect, Springer, and Bireme. Article searches were conducted, restricting the publication period to 2014-2024. The search descriptors followed the MESH/DECS terms: Stroke, Virtual Reality, Gait, Balance and Ecercise Therapy. Controlled trials comparing VR to conventional rehabilitation were included. Population criteria included adults and the elderly post-stroke, excluding children, cerebral palsy, and other neurological disorders associated with gait and balance disturbances. Results: The search in the established databases yielded 1,711 studies, of which 115 were excluded due to duplication, 1,403 after title and abstract screening, and 182 after full-text reading. Thus, 11 studies met the inclusion criteria. Conclusions: VR rehabilitation moderately improves outcomes compared to conventional rehabilitation in adults and the elderly post-stroke. However, current interventions have small sample sizes to precisely evaluate their overall benefits. Future research should analyze the activities performed, participation, and assessment measures, and investigate commercially available VR systems.

Cardiovascular diseases represent the primary cause of mortality in contemporary society. However, the majority of these conditions can be effectively diagnosed at an early stage through the use of Electrocardiography (ECG). ECG with high resolution, recorded from the body’s surface, has the capability to detect subtle intracardiac potentials with low amplitude, thereby reducing the need for invasive diagnostic procedures. Enhanced resolution in ECG acquisition enables the generation of more detailed signals, which increases the potential for identifying additional features compared to conventional methods. This study introduces a platform employing a 24-bit analog-to-digital converter for real-time acquisition of ECG signals. These signals are transmitted to a web-based system for subsequent processing and analysis. The research aims to validate the technological feasibility of acquiring ECG signals at 24-bit resolution, facilitating immediate clinical interventions and providing a foundation for future investigations. A prototype device was tested by capturing patient ECG signals, which were processed using advanced filters to identify key areas of clinical relevance.