Search Results (57)

A review of the methods and applications of marker-based and markerless-based motion capture and inertial measurement units for clinical gait analysis is offered to provide readers with an important historical and legacy-guided perspective. Advantages and limitations of these methods are delineated in light of Cappozzo’s ‘considerations on clinical gait evaluation’ and Brand and Crowninshield’s ‘comment on criteria for patient evaluation tools’. Critical summaries of each manuscript that make up this Special Issue reflect consideration of the notable comments by the legacy biomechanists who had the insights to frame important issues. 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

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

Gait recovery after stroke is a primary goal of rehabilitation, therefore it is imperative to develop technologies that accurately identify gait impairments after stroke. Markerless motion capture (MMC) is an emerging technology that has been validated in healthy individuals. Our study aims to evaluate the validity of MMC against an instrumented walkway system (IWS) commonly used to evaluate gait in stroke survivors. Nineteen participants performed three comfortable speed (CS) and three fastest speed (FS) walking trials simultaneously recorded with IWS and MMC system, KinaTrax (HumanVersion 8.2, KinaTrax Inc., Boca Raton, FL, USA). Pearson’s correlation coefficient and intraclass correlation coefficient (ICC (3,1), 95%CI) were used to evaluate the agreement and consistency between systems. Furthermore, Bland–Altman plots were used to estimate bias and Limits of Agreement (LoA). For both CS and FS, agreements between MMC and IWS were good to excellent in all parameters except for non-paretic single-limb support time (SLS), which revealed moderate agreement during CS. Additionally, stride width and paretic SLS showed poor agreement in both conditions. Biases eliminated systematic errors, with variable LoAs in all parameters during both conditions. Findings indicated high validity of MMC in measuring spatiotemporal gait parameters in stroke survivors. Further validity work is warranted. Full article

The ability to quantify equine kinematics is essential for clinical evaluation, research, and performance feedback. However, current methods are challenging to implement. This study presents a motion capture methodology for horses, where three-dimensional, full-body kinematics are calculated without instrumentation on the animal, offering a more scalable and labor-efficient approach when compared with traditional techniques. Kinematic trajectories are calculated from multi-camera video data. First, a neural network identifies skeletal landmarks (markers) in each camera view and the 3D location of each marker is triangulated. An equine biomechanics model is scaled to match the subject’s shape, using segment lengths defined by markers. Finally, inverse kinematics (IK) produces full kinematic trajectories. We test this methodology on a horse at three gaits. Multiple neural networks (NNs), trained on different equine datasets, were evaluated. All networks predicted over 78% of the markers within 25% of the length of the radius bone on test data. Root-mean-square-error (RMSE) between joint angles predicted via IK using ground truth marker-based motion capture data and network-predicted data was less than 10 degrees for 25 to 32 of 35 degrees of freedom, depending on the gait and data used for network training. NNs trained over a larger variety of data improved joint angle RMSE and curve similarity. Marker prediction error, the average distance between ground truth and predicted marker locations, and IK marker error, the distance between experimental and model markers, were used to assess network, scaling, and registration errors. The results demonstrate the potential of markerless motion capture for full-body equine kinematic analysis. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

Background: Marker-based motion capture is the current gold standard for three-dimensional (3D) gait analysis. This is a highly technical analysis that is time-consuming, and marker application can trigger anxiety in children. One potential solution is to use markerless camera systems instead. The objective of this study was to compare 3D lower limb gait kinematics in children using both marker-based and markerless motion capture methods. Methods: Ten typically developing children (age 6–13 yrs) completed five barefoot walks at a self-selected speed. A 10-camera marker-based system (Oqus, Qualisys) and a 7-camera markerless system (Miqus, Qualisys) captured synchronised gait data at 85 Hz. Generalised Additive Mixed Models were fitted to the data to identify the random effects of measurement systems, age, and time across the gait cycle. The root-mean-square difference (RMSD) was used to compare the differences between systems. Results: Significant interactions and differences were observed between the marker-based and markerless systems for most joint angles and planes of motion, particularly with regard to time and age. Conclusions: Despite differences across all kinematic profiles, the RMSD in this study was comparable to previously published results. Alternative model definitions and kinematic crosstalk in both systems likely explain the differences. Age differences were not consistent across joint levels, suggesting a larger sample size is required to determine how maturation may affect markerless tracking. Further investigation is required to understand the deviations and differences between systems before implementing markerless technology in a clinical setting. Full article

Subtle locomotor asymmetries are common in horses and may go unnoticed during routine pre-race clinical inspections, particularly when based solely on subjective evaluation. This study aimed to describe vertical head and pelvic movement asymmetries in racehorses that passed official pre-race inspections at a traditional racing event. Twenty-four horses were analysed using a markerless AI-based gait analysis system while trotting in-hand and during lungeing in both directions. Asymmetry parameters (HDmin, HDmax, PDmin, and PDmax) were extracted from video recordings, with values ≥0.5 considered clinically relevant. Vertical asymmetries were detected in 71% of horses during straight-line evaluation and in 79% during at least one lungeing direction. Some horses showed relevant asymmetries only under specific movement conditions, underscoring the complementary role of straight-line and lungeing assessments in comprehensive gait evaluation. These results suggest that objective gait analysis could enhance pre-race veterinary assessments, especially in traditional racing, where horses are subjected to significant biomechanical stress, including variable surface properties and repetitive directional loading. In such complex and dynamic environments, relying solely on visual assessment may result in the underdiagnosis of subtle locomotor alterations. The AI-based tools offer potential to improve the detection of subtle irregularities and support evidence-based decisions in performance horse management. Further investigations are warranted to validate the clinical relevance of currently adopted asymmetry thresholds, refine their diagnostic value, and support their integration into standardized pre-race evaluation protocols. Full article

Objective: We developed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) based on marker trajectories obtained from a markerless motion capture system. Methods: Gait data were collected using two cameras and three force plates. Each gait dataset contained kinematic data and kinetic data from the stance phase. A multi-layer perceptron (MLP) and convolutional neural network (CNN) were constructed to estimate each component of GRF and COP based on the three-dimensional trajectories of the markers. A total of 100 samples were randomly selected as the test set, and the estimation performance was evaluated using the correlation coefficient (r) and relative root mean square error (rRMSE). Results: The r-values for MLP in each GRF component ranged from 0.918 to 0.989, with rRMSEs between 5.06% and 12.08%. The r-values for CNN in each GRF component ranged from 0.956 to 0.988, with rRMSEs between 6.03–9.44%. For the COP estimation, the r-values for MLP ranged from 0.727 to 0.982, with rRMSEs between 6.43% and 27.64%, while the r-values for CNN ranged from 0.896 to 0.977, with rRMSEs between 6.41% and 7.90%. Conclusions: It is possible to estimate GRF and COP from markerless motion capture data. This approach provides an alternative method for measuring kinetic parameters without force plates during gait analysis. Full article

The aim of this pilot study was to investigate the feasibility of markerless tracking to assess upper body movements of children with and without human immunodeficiency virus encephalopathy (HIV-E). Sagittal and frontal video recordings were used to track anatomical landmarks with the DeepLabCut pre-trained human model in five children with HIV-E and five typically developing (TD) children to calculate shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension and trunk lateral sway. Differences in joint angle trajectories of the two cohorts were investigated using a one-dimensional statistical parametric mapping method. Children with HIV-E showed a larger range of motion in shoulder abduction and trunk sway than TD children. In addition, they showed more shoulder extension and more lateral trunk sway compared to TD children. Markerless tracking was feasible for 2D movement analysis and sensitive to observe expected differences in upper limb and trunk sway movements between children with and without HIVE. Therefore, it could serve as a useful alternative in settings where expensive gait laboratory instruments are unavailable, for example, in clinical centers in low- to middle-income countries. Future research is needed to explore 3D markerless movement analysis systems and investigate the reliability and validity of these systems against the gold standard 3D marker-based systems that are currently used in clinical practice. Full article

(1) Background: Marker-based optical motion tracking is the gold standard in gait analysis; however, markerless solutions are rapidly emerging today. Algorithms like Openpose can track human movement from a video. Few studies have assessed the validity of this method. This study aimed to assess the reliability of Openpose in measuring the kinematics and spatiotemporal gait parameters. (2) Methods: This analysis used simultaneously recorded video and optoelectronic motion capture data. We assessed 20 subjects with different gait impairments (healthy, right hemiplegia, left hemiplegia, paraparesis). The two methods were compared using computing absolute errors (AEs), intraclass correlation coefficients (ICCs), and cross-correlation coefficients (CCs) for normalized gait cycle joint angles. (3) Results: The spatiotemporal parameters showed an ICC between good to excellent, and the absolute error was very small: cadence AE = 1.63 step/min, Mean Velocity AE = 0.16 m/s. The Range of Motion (ROM) showed a good to excellent agreement in the sagittal plane. Furthermore, the normalized gait cycle CCC values indicated moderate to strong coupling in the sagittal plane. (4) Conclusions: We found Openpose to be accurate for sagittal plane gait kinematics and for spatiotemporal gait parameters in the healthy and pathological subjects assessed. Full article

Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases power consumption and computational complexity. The relationship between sampling frequencies and accuracies remains unclear. We thus investigated the specific influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds and recommended sufficient sampling rates. Seventeen healthy subjects wore IMUs on their thigh, shank, and foot and performed walking (1.2 m/s) and running (2.2 m/s) trials on a treadmill, and a motion testbed with an IMU was used to mimic high-frequency cyclic human movements up to 3.0 Hz. Four widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture (OMC) orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. Further, accelerometer sampling rates exceeding 100 Hz even resulted in decreased accuracy because excessive orientation updates using distorted accelerations and angular velocity introduced more error than merely using angular velocity. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds. Full article

Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease associated with alterations in posture and gait. The aim of this study was to assess the gait of AS patients using pedobarography and a markerless motion capture system. This is the first study of this population to combine these two methods. Twelve AS patients and twelve healthy controls were enrolled in this study. An instrumented gait analysis of both groups was performed using pedobarography and Microsoft Kinect v2. The AS group was significantly older than the controls (p < 0.05). The AS group showed a significantly lower relative pressure distribution in the front-right quadrant (p = 0.01) and a significantly higher relative pressure distribution in the rear-right quadrant (p = 0.05) on the static pedobarography. The AS group also had a higher peak force in the midfoot on the dynamic pedobarography (p < 0.05). The AS group had a significantly shorter stride length (p = 0.01). No significant differences between the groups were found in their hip flexion/extension and adduction/abduction, knee flexion, or ankle dorsiflexion/plantarflexion angles. This study shows significant alterations in the pedobarographic and spatiotemporal, but not in the kinematic, gait parameters of AS patients. These alterations represent a feature of AS and not antalgic adjustments. Rehabilitation programs for AS patients could be tailored according to the results of an instrumented gait analysis and should include balance and gait exercises. Full article

This study aimed to assess the intraday reliability of markerless gait analysis using an RGB-D camera versus a traditional three-dimensional motion analysis (3DMA) system with and without a simulated walking assistant. Gait assessments were conducted on 20 healthy adults walking on a treadmill with a focus on spatiotemporal parameters gathered using the RGB-D camera and 3DMA system. The intraday reliability of the RGB-D camera was evaluated using intraclass correlation coefficients (ICC 1, 1), while its consistency with the 3DMA system was determined using ICC (2, 1). The results demonstrated that the RGB-D camera provided high intraday reliability and showed strong consistency with 3DMA measurements regardless of the presence of an assistant. The Bland–Atman analysis indicated no significant systematic bias, with the minimum detectable change remaining within acceptable clinical ranges. These findings highlight the potential of the RGB-D camera for reliable markerless gait analysis in clinical environments in which walking assistance may be needed, thereby expanding its applicability in patients with various impairment degrees. Future research should validate these results in patient populations and explore their utility for measuring kinematic parameters. Full article

Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. Full article