Explore the vast range of titles available.

Axial postural abnormalities in Parkinson’s Disease (PD) are traditionally assessed using clinical rating scales, although picture-based assessment is considered the gold standard. This study evaluates the reliability and clinical relevance of two markerless body-tracking frameworks, the RGB-D-based Microsoft Azure Kinect (providing the reference KIN_3D model) and the RGB-only Google MediaPipe Pose (MP), using a synchronous dual-camera setup. Forty PD patients performed a 60 s static standing task. We compared KIN_3D with three MP models (at different complexity levels) across horizontal, vertical, sagittal, and 3D joint angles. Results show that lower-complexity MP models achieved high congruence with KIN_3D for trunk and shoulder alignment (ρ > 0.75), while the lateral view significantly improved tracking of sagittal angles (ρ ≥ 0.72). Conversely, the high-complexity model introduced significant skeletal distortions. Clinically, several angular parameters emerged as robust metrics for postural assessment and global motor impairments, while sagittal angles correlated with motor complications. Unexpectedly, a more upright frontal alignment was associated with greater freezing of gait severity, suggesting that static postural metrics may serve as proxies for dynamic gait performance. In addition, both RGB-only and RGB-D frameworks effectively discriminated between postural severity clusters. While the higher-complexity MP model should be avoided due to inaccurate 3D reconstructions, our findings demonstrate that low- and medium-complexity MP models represent a reliable alternative to RGB-D sensors for objective postural assessment in PD, facilitating the widespread application of objective posture measurements in clinical contexts.

Microsoft Kinect is an increasingly common tool for low-cost and portable measurement of human movements. The aim of this study was to investigate the potential reliability and reproducibility of Microsoft Kinect Azure in the sit-and-reach test conducted under standardized test conditions in a controlled training environment with elite female weightlifters. The sit-and-reach test was performed with Microsoft Kinect Azure and manual methods and also the reliability and validity were analyzed by comparing the wrist, elbow, shoulder and trunk joint angle measurements with the digital goniometer. Twenty-one healthy female weightlifter athletes who won medals in international championships were included in the study. Sit-and-reach testing was performed using Microsoft Kinect Azure and manual methods in accordance with the standards of the American College of Sports Medicine. The flexion range of motions of each athlete’s wrist, elbow, shoulder and trunk joint angles were measured with a digital goniometer and Microsoft Kinect Azure. Statistical analyses were performed using IBM SPSS 25.0 and Python 3.9.12 software. Descriptive statistics were calculated for all variables, and differences between Kinect and manual methods were evaluated with the Mann-Whitney U test and Wilcoxon Signed-Rank Test. Linear relationships between measurements were analyzed with Spearman correlation coefficients. Intraclass Correlation Coefficient was calculated for the concordance analysis and presented with Bland-Altman plots. The level of significance was accepted as p  < 0.05. The findings suggest that Kinect Azure may provide good reproducibility and agreement with manual goniometric measurements for joint angles under the specific structured conditions tested. For parameters such as the elbow, shoulder, wrist, trunk angles and distance traveled, high ICC values (> 0.90) indicated strong consistency between the methods. Kinect Azure may serve as a reliable alternative to manual measurement methods under standardized testing conditions. Further studies are recommended to examine its generalizability to other populations and test environments.

Materials that can change shape in response to external parameters are becoming essential in developing future technologies in robotics, aerospace, and smart systems. This study focuses on the development of fiber-reinforced composites embedded with shape memory alloys (SMA) to enable localized bending and twisting deformations. By carefully designing the internal fiber structure and incorporating SMA wires, the composites were fabricated to deform in specific ways when activated. Multiple design variants were created to study how changes in fiber orientation, number of SMA wires and localized stiffness influence the deformation behavior. A depth-sensing camera setup with synchronized Azure Kinect devices was used to capture and analyze the resulting 3D shape changes. The obtained data facilitated the identification and tracking of specific points on the composite, enabling precise deformation measurement. The results highlight the significant impact of fiber angles and stiff sections on guiding bending and twisting behavior, contributing valuable insights toward the design of adaptive smart materials with controlled, complex deformation capabilities.

This article presents an indoor assistive system that addresses the challenges faced by visually impaired individuals. The proposed system helps the visually impaired individuals to move indoor and make them independent of any external assistance. The proposed system consists of a camera with a processing unit and an accompanying Time-of-Flight sensor providing an efficient, convenient and cost-effective solution. The proposed system achieves average object detection accuracy of 73.34% and a 5% error margin in detecting the distance and length of detected objects. The performance comparison with two existing systems shows that the proposed system provides a very close performance to the benchmarks with advantages of portability easy-to-use and no requirement for cloud services.