Search Results (521)

Background: Parkinson’s disease (PD) is a neurodegenerative condition that affects quality of life due to motor (gait, balance) and cognitive alterations, raising the risk of falling. Virtual reality (RV) and dancing have shown benefits for speed of walking, balance, and postural stability, as well as decreased risk of falls. Objective: The goal of this study was to analyze the effectiveness of RV and dancing using a Kinect Xbox 360 video game to improve walking speed and motor performance and reduce the risk of falls in patients with PD. Method: This is a pre-experimental study with a simple pre-post design, involving a single group of 14 patients diagnosed with PD in stages 1 to 4 of the Hoehn and Yahr (H and Y) scale, from the National Institute of Neurology (INNN). Before and after the intervention, motor tests, the Unified Parkinson’s Disease Rating Scale (UPDRS-III), the Timed Up and Go (TUG) test, and the Tinetti were applied. The intervention consisted of 16 bi-weekly sessions, which included warm-up, coordination exercises, 10 songs, and cool-down. Results: Effects of the RV intervention were observed on improvements in motor tests (z = −2.640, p = 0.008), gait (z = −3.316, p = 0.001), balance (TUG) (z = −2.966, p = 0.001), and on the UPDRS-III scale (total index) (z = −3.048, p = 0.002). An increase in the difficulty level of dancing was also observed (X² = 144.13, p < 0.01). Conclusions: The virtual reality intervention with dancing improved motor performance, including increased walking speed, enhanced postural stability, reduced stiffness and bradykinesia, and a decreased risk of falls Full article

Background/Objectives: Outpatient stroke rehabilitation is expanding as inpatient episodes shorten. Virtual reality (VR) exergaming can extend practice and standardize progression, but setting-specific effectiveness and implementation factors remain unclear. This scoping review mapped VR exergaming in outpatient stroke care and identified technology typologies and functional outcomes. Methods: Guided by the JBI Manual and PRISMA-ScR, searches of MEDLINE, Embase, CENTRAL, Scopus, and Web of Science were conducted in April 2025. The study included adults post-stroke undergoing VR exergaming programs with movement tracking delivered in clinic-based outpatient or home-based outpatient settings. Interventions focused on functional rehabilitation using interactive VR. Results: Sixty-six studies met the criteria, forty-four clinic-based and twenty-two home-based. Serious games accounted for 65% of interventions and commercial exergames for 35%. Superiority on a prespecified functional endpoint was reported in 41% of trials, 29% showed within-group improvement only, and 30% found no between-group difference; effects were more consistent in supervised clinic programs than in home-based implementations. Signals were most consistent for commercial off-the-shelf and camera-based systems. Gloves or haptics and locomotor platforms were promising but less studied. Head-mounted display interventions showed mixed findings. Adherence was generally high, and adverse events were infrequent and mild. Conclusions: VR exergaming appears clinically viable for outpatient stroke rehabilitation, with the most consistent gains in supervised clinic-based programs; home-based effects are more variable and sensitive to dose and supervision. Future work should compare platform types by therapeutic goal; embed mechanistic measures; strengthen home delivery with dose control and remote supervision; and standardize the reporting of fidelity, adherence, and cost. Full article

Estimated population growth and increased demand for food production bring with them the evident need for more efficient and sustainable production systems. Because of this, computer vision plays a fundamental role in the development and application of solutions that help producers with the issues that limit livestock production in Brazil and the world. In addition to being stressful for the producer and the animal, the conventional pig weighing system causes productive losses and can compromise meat quality, being considered a practice that does not value animal welfare. The objective was to develop a computational procedure to predict the live weight of pigs in the growth and finishing phases, through the volume of the animals extracted through the processing of 3D images, as well as to analyze the real and estimated biometric measurements to define the relationships of these with live weight and volume obtained. The study was conducted at Roçadinho farm, in the municipality of Capoeiras, located in the Agreste region of the state of Pernambuco, Brazil. The variables weight and 3D images were obtained using a Kinect^®—V2 camera and biometric measurements of 20 animals in the growth phase and 24 animals in the finishing phase, males and females, from the crossing of Pietrain and Large White, totaling 44 animals. To analyze the images, a program developed in Python (PyCharm Community Edition 2020.1.4) was used, to relate the variables, principal component analyses and regression analyzes were performed. The coefficient of linear determination between weight and volume was 73.3, 74.1, and 97.3% for pigs in the growing, finishing, and global phases, showing that this relationship is positive and satisfactorily expressed the weight of the animals. The relationship between the real and estimated biometric variables had a more expressive coefficient of determination in the global phase, having presented values between 77 and 94%. Full article

Assessing the fall risk of a patient in a busy clinical setting is challenging. Tests such as the timed-up-and-go test and narrow beam walking are difficult to perform due to space restrictions. Moreover, it is not easy to directly connect the results of these tests to fundamental biomechanical principles of gait stability, which emphasize the interplay between the movements of the body’s center of mass (CoM) and its base of support (BoS). Herein, we show how a 1.2 m-long treadmill and a single “time-of-flight” Azure Kinect camera can capture the CoM-BoS interplay within 5 min. The CoM was calculated by dividing the body into 14 segments determined from 20 joint positions measured by the Kinect camera’s body tracking SDK. By tracking the CoM and joint positions from stride to stride, we can evaluate different gait stability metrics using a markerless, contactless, space-efficient approach. A large digital database of CoM movements relative to foot placement will be useful for the future development of statistical and machine learning techniques for identifying subjects at higher risk of falling. Full article

Frailty (a syndrome resulting in reduced physical function) assessments and fall risk assessments rely heavily on in-person evaluations and subjective interpretation, limiting scalability and access. Markerless motion capture (MMC) offers a promising solution for remote, objective assessment, but key kinematic parameters associated with frailty and fall risk remain unclear. This scoping review synthesized evidence from MEDLINE, Embase, Scopus, and CINAHL (inception to October 2024). Eligible studies used MMC to assess adults and compared outcomes to validated frailty or fall risk measures. Of 8048 studies, 39 met the inclusion criteria: 30 evaluated fall risk, 7 evaluated frailty, and 2 evaluated both, including 3114 participants (mean age 75.8; 42% male). Microsoft Kinect was used in 75% of the studies. An average of 23 features was extracted per study. Gait analysis was the most common MMC assessment for fall risk, identifying gait speed, stride length, and step width as key parameters. Frailty-related features were less consistent, with two studies identifying power, speed degradation, power reduction, range of motion, and elbow flexion time during a 20 s arm test. Future studies require standardization of methods and improved reporting of data loss. Despite the emerging nature of the field, MMC shows potential for the identification of fall risk and frailty. Full article

Three-dimensional (3D) motion capture is inaccessible, and the Microsoft Kinect is an alternative to measure surrogates of knee valgus that may contribute to anterior cruciate ligament (ACL) injury risk. We evaluated the influence of sex and body size on the agreement between methods. A total of 40 (10 per sex and BMI group) participants were included. The Kinect and motion capture measured knee ankle separation ratio (KASR) and knee abduction angles (KAAs). Intraclass correlation coefficients (ICCs) evaluated agreement between methods. 2 (sex) by 2 (BMI) by 2 (method) ANOVA compared kinematics between groups. Agreement between methods was moderate-to-good for KASR (initial contact ICCs 0.667–0.86; peak flexion ICCs 0.766–0.882). Agreement for KAA was low-to-moderate (initial contact ICCs 0.128–0.575; peak flexion ICCs 0.315–0.760). There was a BMI-by-method interaction for KASR at initial contact (p < 0.01) and a main effect of method (p < 0.01). There were BMI-by-method interactions for KAA (initial contact p > 0.01; peak knee flexion p < 0.01). The high BMI group had greater KAAs than the low BMI group, but only using motion capture. The Kinect is an alternative for measuring KASR, but not KAA. The high BMI group had greater KAAs than the low BMI group, but only when measured with motion capture. Full article

Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies—APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera—against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera’s field of view. Gait data from 20 older adults (mean age $70.06\pm 9.45$ years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland–Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE $=0.00$–$6.12$, $r=0.92$–$1.00$), followed closely by the Azure Kinect (MAE $=0.01$–$6.07$, $r=0.68$–0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations. Full article

Background: Clinical assessment of balance and postural disorders is usually carried out through several common practices including tests such as the Subjective Visual Vertical (SVV) and Limit of Stability (LOS). Nowadays, several cutting-edge technologies have been proposed as supporting tools for stability evaluation. Extended Reality (XR) emerges as a powerful instrument. This proof-of-concept study aims to assess the feasibility and potential clinical utility of a novel MR-based framework integrating HoloLens 2, Wii Balance Board, and Azure Kinect for multimodal balance assessment. An innovative test is also introduced, the Innovative Dynamic Balance Assessment (IDBA), alongside an MR version of the SVV test and the evaluation of their performance in a cohort of healthy individuals. Results: All participants reported SVV deviations within the clinically accepted ±2° range. The IDBA results revealed consistent sway and angular profiles across participants, with statistically significant differences in posture control between opposing target directions. System outputs were consistent, with integrated parameters offering a comprehensive representation of postural strategies. Conclusions: The MR-based framework successfully delivers integrated, multimodal measurements of postural control in healthy individuals. These findings support its potential use in future clinical applications for balance disorder assessment and personalized rehabilitation. Full article

Operatic singing, traditionally taught through empirical and subjective methods, demands innovative approaches to enhance its pedagogical effectiveness today. This paper introduces a novel integration of advanced skeletal tracking technology into a prototype framework for operatic singing pedagogy research. Using the Microsoft Kinect Azure DK sensor, this prototype extracts detailed data on spinal, cervical, and shoulder alignment and movement data, with the aim of quantifying biomechanical movements during vocal performance. Preliminary results confirmed high face validity and biomechanical relevance. The incorporation of skeletal-tracking technology into vocal pedagogy research could help clarify certain technical aspects of singing and enhance sensorimotor feedback for the training of operatic singers. Full article

Stroke often leads to severe motor impairment, especially in the upper limbs, greatly reducing a patient’s ability to perform daily tasks. Effective rehabilitation is essential to restore function and improve quality of life. Traditional therapies, while useful, may lack engagement, leading to low motivation and poor adherence. Gamification—using game-like elements in non-game contexts—offers a promising way to make rehabilitation more engaging. The authors explore a gamified rehabilitation system designed in Unity 3D using a Kinect V2 camera. The game includes key features such as adjustable difficulty, real-time and predominantly positive feedback, user friendliness, and data tracking for progress. The evaluations were conducted with 18 healthy participants, most of whom had prior virtual reality experience. About 77% found the application highly motivating. While the gameplay was well received, the visual design was noted as lacking engagement. Importantly, all users agreed that the game offers a broad range of difficulty levels, making it accessible to various users. The results suggest that the system has strong potential to improve rehabilitation outcomes and encourage long-term use through enhanced motivation and interactivity. Full article

With the rapid development of sports analytics and artificial intelligence, accurate human pose estimation in badminton is becoming increasingly important. However, challenges such as the lack of domain-specific datasets and the complexity of athletes’ movements continue to hinder progress in this area. To address these issues, we propose an enhanced pose estimation framework tailored to badminton players, built upon an improved YOLOv8-Pose architecture. In particular, we introduce an efficient local attention (ELA) mechanism that effectively captures fine-grained spatial dependencies and contextual information, thereby significantly improving the keypoint localization accuracy and overall pose estimation performance. To support this study, we construct a dedicated badminton pose dataset comprising 4000 manually annotated samples, captured using a Microsoft Kinect v2 camera. The raw data undergo careful processing and refinement through a combination of depth-assisted annotation and visual inspection to ensure high-quality ground truth keypoints. Furthermore, we conduct an in-depth comparative analysis of multiple attention modules and their integration strategies within the network, offering generalizable insights to enhance pose estimation models in other sports domains. The experimental results show that the proposed ELA-enhanced YOLOv8-Pose model consistently achieves superior accuracy across multiple evaluation metrics, including the mean squared error (MSE), object keypoint similarity (OKS), and percentage of correct keypoints (PCK), highlighting its effectiveness and potential for broader applications in sports vision tasks. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

Accurate range of motion (ROM) assessment is essential for evaluating musculoskeletal function and guiding rehabilitation, particularly in pediatric populations. Traditional methods, such as optical motion capture and handheld goniometry, are often limited by cost, accessibility, and inter-rater variability. This study evaluated the feasibility and accuracy of the Microsoft Azure Kinect-powered Monitored Augmented Rehabilitation System (MARS) compared to Kinovea. Sixty-five pediatric participants (ages 5–18) performed standardized shoulder and elbow movements in the frontal and sagittal planes. ROM data were recorded using MARS and compared to Kinovea. Measurement reliability was evaluated using intraclass correlation coefficients (ICC3k), and accuracy was evaluated using root mean squared error (RMSE) analysis. MARS demonstrated excellent reliability with an average ICC3k of 0.993 and met the predefined accuracy threshold (RMSE ≤ 8°) for most movements, with the exception of sagittal elbow flexion. These findings suggest that MARS is a reliable, accurate, and cost-effective alternative for clinical ROM assessment, offering a markerless solution that enhances measurement precision and accessibility in pediatric rehabilitation. Future studies should enhance accuracy in sagittal plane movements and further validate MARS against gold-standard systems. Full article

Background: Chronic musculoskeletal disorders (CMDs) represent a leading cause of global disability and diminished quality of life, and they are often resistant to conventional physiotherapy. Emerging technologies such as virtual reality (VR), augmented reality (AR), and exergaming are increasingly used to enhance rehabilitation outcomes, yet their comparative effectiveness remains unclear. Objective: To systematically evaluate the effectiveness of VR, AR, and exergaming interventions in improving pain, function, balance, and psychological outcomes among adults with CMDs. Methods: This systematic review and exploratory meta-analysis followed PRISMA 2020 guidelines and was prospectively registered (PROSPERO: CRD42024589007). A structured search was conducted in PubMed, Cochrane CENTRAL, Scopus, and PEDro (up to 1 May 2025). Eligible studies were randomized controlled trials (RCTs) involving adults (≥18 years) with CMDs receiving VR, AR, or exergaming-based rehabilitation. Risk of bias was assessed using the PEDro scale and the Downs and Black checklist. Where feasible, standardized mean differences (SMDs) for pain outcomes were pooled using a random-effects model. Results: Thirteen RCTs (n = 881 participants) met the inclusion criteria. Interventions spanned immersive VR, AR overlays, exergaming platforms (e.g., Kinect, Wii), and motion-tracking systems. Pain, function, and quality of life improved in most studies. An exploratory meta-analysis of eight RCTs (n = 610) yielded a significant pooled effect favoring VR/AR interventions for pain reduction (SMD = −1.14; 95% CI: −1.63 to −0.75; I² = 0%). Exergaming showed consistent improvements in physical performance, while immersive VR was more effective for kinesiophobia and psychological outcomes. AR was underrepresented, with only one study. Risk of bias was generally low; however, publication bias could not be excluded due to limited funnel plot power (n < 10). Conclusions: VR, AR, and exergaming are effective adjuncts to conventional rehabilitation for CMDs, improving pain and function with high patient adherence. Nevertheless, gaps in long-term data, economic evaluation, and modality comparison persist. Future RCTs should address these limitations through standardized, inclusive, and longitudinal design. Full article

Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. However, existing gait recognition methods exhibit limitations in complex scenarios. To address these, we construct a dual-Kinect V2 system that focuses more on gait skeleton joint data and related acoustic signals. This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). Our recognition methods serve as the foundation, and the decision support mechanism is used to evaluate the compatibility of various modules within our system. On this basis, our main contributions are as follows: (1) an improved gait skeleton joint AdaBoost recognition method based on Circle Chaotic Mapping and Gramian Angular Field (GAF) representations; (2) a data-adaptive gait-related acoustic signal AdaBoost recognition method based on GAF and a Parallel Convolutional Neural Network (PCNN); and (3) an amalgamation of the Triangulation Topology Aggregation Optimizer (TTAO) and D-SET, providing a robust and innovative decision support mechanism. These collaborations improve the overall recognition accuracy and demonstrate their considerable application values. Full article