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The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.

In recent years, the application of ergonomics to workplace safety monitoring has gained increasing interest from companies and public institutions, allowing for the evaluation of the potential impact that dangerous situations may have on workers during their routine activities. This study presents a method for real-time monitoring of human physiological and motor responses to simulated workplace hazards during virtual reality safety training. The setup allows for precise measurements of both physiological and postural parameters during simulated scenarios. Moreover, a representative case study involving the sudden arrival of a forklift in a warehouse is presented. Five healthy participants were exposed to this scenario, with changes in heart rate variability and trunk posture being captured. The results demonstrate the effectiveness of sensor-based monitoring in detecting stress responses and postural adaptations to hazardous stimuli. This approach provides a basis for understanding human responses in simulated hazardous environments and may help to optimize safety training aimed at increasing workers’ risk perception and improving overall workplace safety. Although based on a small sample, the findings provide preliminary insights into the feasibility of sensor-based monitoring during VR safety training.

The Trendelenburg test is widely used to assess hip abductor function, but interpretation is typically subjective and only moderately reliable. Compensatory trunk lean can mask subtle pelvic drop, further limiting diagnostic accuracy. Artificial intelligence (AI) based markerless motion analysis allows objective quantification of pelvic, trunk, and knee angles using standard video recordings. This single-centre cross-sectional feasibility study was conducted in an Irish orthopaedic unit. Twelve adults were enrolled: seven post–total hip arthroplasty (THA) and five with native hip pathology. Each patient performed a standardised single-leg Trendelenburg test on both legs while being recorded with a single posteriorly placed smartphone camera. Videos were analysed offline using an AI-based markerless motion application (OnForm) to derive coronal-plane pelvic obliquity, trunk lean, and knee angle change between bipedal and single-leg stance. Continuous outcomes were summarised as medians with interquartile ranges (IQR) and ranges. Pre-specified thresholds (pelvic drop ≥ 4°, trunk lean ≥ 5°, knee angle change ≥ 3°) were used to describe the frequency of marked deviations. All patients completed the protocol with analysable recordings. The median video capture time was 32.5 s (IQR 23.5–36.0; range 19–42) and the median analysis time was 184.5 s (IQR 178.5–196.5; range 168–207), giving a median total workflow time of 215.5 s (IQR 203.5–232.5; range 193–244) per patient. Median worst contralateral pelvic obliquity was 0.0° (IQR − 1.0° to + 1.5°; range − 5° to + 6°). Median maximum trunk lean was 4.5° (IQR 2.8°–9.0°; range 2°–10°). Median coronal-plane knee angle change was 3.0° (IQR 2.0°–4.0°; range 1°–8°). Post-THA patients showed greater trunk compensation than those with native hips (median maximal trunk lean 9.0° vs 3.0°; median difference 6.0°), with trunk lean ≥ 5° in 5/7 post-THA and 1/5 native-hip patients. Knee deviations ≥ 3° were seen in 8 patients (67%). AI-assisted single-camera analysis of the Trendelenburg test is feasible, rapid, and clinically informative. The method consistently quantified pelvic, trunk, and knee angles and demonstrated that post-THA patients frequently compensate with trunk lean rather than contralateral pelvic drop. This approach could enhance objective documentation of Trendelenburg performance and support postoperative rehabilitation monitoring. These findings are preliminary and hypothesis-generating; larger controlled studies with asymptomatic controls and reference standards are required to validate accuracy and clinical utility.

Walking ability is essential for maintaining functional independence, but it can be impaired by conditions like hemiplegia resulting from a stroke event. In post-stroke populations, accurately assessing gait anomalies is crucial for rehabilitation to promote functional recovery, and to prevent falls or injuries. The aim of this study is to evaluate gait-related parameters using a solution based on a single RGB-D camera, specifically Microsoft Azure Kinect DK (MAK), on a short walkway in both healthy (n= 27) and post-stroke individuals with hemiplegia (n= 20). The spatio-temporal and center of mass (CoM) parameters estimated by this approach were compared with those obtained from a gold standard motion capture (MoCap) system for instrumented 3D gait analysis. The overall findings demonstrated high levels of accuracy (> 93%), and strong correlations (r > 0.9) between the parameters estimated by the two systems for both healthy and hemiplegic gait. In particular, some spatio-temporal parameters showed excellent agreement in both groups, while CoM displacements exhibited slightly lower correlation values in healthy individuals. The results of the study suggest that a solution based on a single optical sensor could serve as an effective intermediate tool for gait analysis, not only in clinical settings or controlled environments but also in those contexts where gold standard systems are not feasible.

Remote exercise supervision and functional movement assessment require sensing pipelines that can capture body motion, interpret protocol progression, and provide meaningful feedback within the same runtime environment. This paper presents ORAMA, an integrated computer vision platform for the execution and remote monitoring of digital exercises and clinically oriented assessment protocols related to physical fitness, mobility, balance, and health. The system combines ZED 2i stereo capture and depth-aware body tracking with a protocol-driven software architecture that includes a computer-vision pipeline, an exercise and assessment engine, a real-time feedback layer, persistent session handling, structured output generation, and a chatbot-assisted interaction path. Unlike solutions that focus only on movement recognition, ORAMA organizes each task as an explicit executable protocol with calibration stages, state transitions, task-specific metrics, and live visual guidance. The paper analyzes the system architecture, reviews the surrounding literature on virtual coaching and rehabilitation-oriented computer vision, and demonstrates representative user-interface and runtime views for both assessment and exercise scenarios. The present work reports a prototype architecture and representative operational demonstrations, rather than a completed clinical validation or participant-based efficacy study. The resulting platform shows how markerless 3D body tracking can be embedded within a unified and interpretable environment for guided exercise, functional testing, and remote follow-up without requiring wearable sensors.

Musculoskeletal disorders such as rheumatoid arthritis and osteoarthritis affect millions worldwide and are projected to rise sharply by 2050, highlighting the importance of scalable telerehabilitation. This paper introduces Rehabi, a mobile, user-friendly tele-rehabilitation platform that centrally integrates markerless motion for biomechanical assessment and monitoring. Rehabi development followed a user-centered methodology, combining questionnaires, interviews, and natural language processing to elicit requirements from patients and clinicians. The system architecture was implemented in accordance with Clean Architecture principles to ensure modularity and scalability. In a pilot clinical validation of the markerless motion algorithm integrated into Rehabi, 14 post-arthroplasty patients showed moderate agreement for hip flexion (ICC = 0.686) and good agreement for knee flexion (ICC = 0.801). Although the sample was small, the results show a promising trend suggesting that mobile markerless motion capture may be a viable option for remote assessment and monitoring.

This work proposes to improve the accuracy of joint angle estimates obtained from an RGB-D sensor. It is based on a constrained extended Kalman Filter that tracks inputted measured joint centers. Since the proposed approach uses a biomechanical model, it allows physically consistent constrained joint angles and constant segment lengths to be obtained. A practical method that is not sensor-specific for the optimal tuning of the extended Kalman filter covariance matrices is provided. It uses reference data obtained from a stereophotogrammetric system but it has to be tuned only once since it is task-specific only. The improvement of the optimal tuning over classical methods in setting the covariance matrices is shown with a statistical parametric mapping analysis. The proposed approach was tested with six healthy subjects who performed four rehabilitation tasks. The accuracy of joint angle estimates was assessed with a reference stereophotogrammetric system. Even if some joint angles, such as the internal/external rotations, were not well estimated, the proposed optimized algorithm reached a satisfactory average root mean square difference of 9.7 ∘ and a correlation coefficient of 0.8 for all joints. Our results show that an affordable RGB-D sensor can be used for simple in-home rehabilitation when using a constrained biomechanical model.

Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.

Kinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the quantification of human movement. Common marker-based optical motion capture systems are time intensive and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those from a standard marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras and seven infrared optical motion capture cameras, providing synchronized markerless and marker-based data for comparison. The average root mean square distance (RMSD) between corresponding joint centers was less than 2.5 cm for all joints except the hip, which was 3.6 cm. Lower limb segment angles relative to the global coordinate system indicated the global segment pose estimates from both systems were very similar, with RMSD of less than 5.5° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings indicate that the markerless system would be a suitable alternative technology in cases where the practical benefits of markerless data collection are preferred.

(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.