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Background: Abnormalities in gait kinetics in patients with Parkinson's disease (PD) who have suffer from gait impairment have been noted using a conventional inverse dynamic analysis derived by marker-based motion capture system and force plate, which are typically mounted in the laboratory floor. Despite the high accuracy of this approach in tracking markers' trajectories and acquiring ground reaction forces (GRFs), its dependence on laboratory-mounted equipment restricts its potential use in wider variety of clinical applications.Research Question: Would a full-body musculoskeletal model driven by a single depth sensor data only produce comparable gait kinetic parameters, including GRFs and lower extremity joints moments, for elderly participants, both healthy and those diagnosed with PD?Methods: Nine patients diagnosed with PD and 11 healthy age-matched control participants performed three over-ground gait trials. Full-body kinematic data were collected using a depth sensor and a musculoskeletal model have been constructed using AnyBody musculoskeletal modeling system to predict the three-dimensional GRFs and lower extremity joint moments. Predicted kinetic parameters for both PD and control groups were compared during the braking and propulsive phases of the gait cycle. In addition, ensemble curve analysis with 90% confidence intervals were constructed to compare between group differences across the stance phase of the gait cycle.Results: The findings of this study showed that the PD exhibited a significantly lower braking peak vertical GRF and propulsion peak horizontal GRF while no significant between-group differences were found in peak lower extremity joint moments. However, the PD showed significant alterations in lower extremity joint moments during the early and late phases of stance, which indicate a difference in ambulation strategy.Significance: The proposed method adopting full-body musculoskeletal model driven by a depth sensor data proves that it has the potential to be a portable and cost-effective gait analysis tool in the clinical setting. [ABSTRACT FROM AUTHOR]

Gait patterns differ between healthy elders and those with Parkinson's disease (PD). A simple, low-cost clinical tool that can evaluate kinematic differences between these populations would be invaluable diagnostically; since gait analysis in a clinical setting is impractical due to cost and technical expertise. This study investigated the between group differences between the Kinect and a 3D movement analysis system (BTS) and reported validity and reliability of the Kinect v2 sensor for gait analysis. Nineteen subjects participated, eleven without (C) and eight with PD (PD). Outcome measures included spatiotemporal parameters and kinematics. Ankle range of motion for C was significantly less during ankle swing compared to PD ( p = 0.04) for the Kinect. Both systems showed significant differences for stride length (BTS (C 1.24 ± 0.16, PD= 1.01 ± 0.17, p = 0.009), Kinect (C= 1.24 ± 0.17, PD= 1.00 ± 0.18, p = 0.009)), gait velocity (BTS (C= 1.06 ± 0.14, PD= 0.83 ± 0.15, p = 0.01), Kinect (C= 1.06 ± 0.15, PD= 0.83 ± 0.16, p = 0.01)), and swing velocity (BTS (C= 2.50 ± 0.27, PD= 2.12 ± 0.36, p = 0.02), Kinect (C= 2.32 ± 0.25, PD= 1.95 ± 0.31, p = 0.01)) between groups. Agreement (Range ICC = 0.93–0.99) and consistency (Range ICC = 0.94–0.99) were excellent between systems for stride length, stance duration, swing duration, gait velocity, and swing velocity. The Kinect v2 can was sensitive enough to detect between group differences and consistently produced results similar to the BTS system. [ABSTRACT FROM AUTHOR]

A cost-effective, clinician friendly gait assessment tool that can automatically track patients' anatomical landmarks can provide practitioners with important information that is useful in prescribing rehabilitative and preventive therapies. This study investigated the validity and reliability of the Microsoft Kinect v2 as a potential inexpensive gait analysis tool. Ten healthy subjects walked on a treadmill at 1.3 and 1.6m·s-1, as spatiotemporal parameters and kinematics were extracted concurrently using the Kinect and three-dimensional motion analysis. Spatiotemporal measures included step length and width, step and stride times, vertical and mediolateral pelvis motion, and foot swing velocity. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. The absolute agreement and relative consistency between the two systems were assessed using interclass correlations coefficients (ICC2,1), while reproducibility between systems was established using Lin's Concordance Correlation Coefficient (rc). Comparison of ensemble curves and associated 90% confidence intervals (CI90) of the hip, knee, and ankle joint angles were performed to investigate if the Kinect sensor could consistently and accurately assess lower extremity joint motion throughout the gait cycle. Results showed that the Kinect v2 sensor has the potential to be an effective clinical assessment tool for sagittal plane knee and hip joint kinematics, as well as some spatiotemporal temporal variables including pelvis displacement and step characteristics during the gait cycle. [ABSTRACT FROM AUTHOR]

Background: Gait analysis is burdened by time and equipment costs, interpretation, and accessibility of three-dimensional motion analysis systems. Evidence suggests growing adoption of gait testing in the shift toward evidence-based medicine. Further developments addressing these barriers will aid its efficacy in clinical practice. Previous research aiming to develop gait analysis systems for kinetics estimation using the Kinect V2 have provided promising results yet modified approaches using the latest hardware may further aid kinetics estimation accuracy RESEARCH QUESTION: Can a single Azure Kinect sensor combined with a musculoskeletal modeling approach provide kinetics estimations during gait similar to those obtained from marker-based systems with embedded force platforms?Methods: Ten subjects were recruited to perform three walking trials at their normal speed. Trials were recorded using an eight-camera optoelectronic system with two embedded force plates and a single Azure Kinect sensor. Marker and depth data were both used to drive a musculoskeletal model using the AnyBody Modeling System. Predicted kinetics from the Azure Kinect-driven model, including ground reaction force (GRF) and joint moments, were compared to measured values using root meansquared error (RMSE), normalized RMSE, Pearson correlation, concordance correlation, and statistical parametric mapping RESULTS: High to very high correlations were observed for anteroposterior GRF (ρ = 0.889), vertical GRF (ρ = 0.940), and sagittal hip (ρ = 0.805) and ankle (ρ = 0.876) moments. RMSEs were 1.2 ± 2.2 (%BW), 3.2 ± 5.7 (%BW), 0.7 ± 0.1.3 (%BWH), and 0.6 ± 1.0 (%BWH) SIGNIFICANCE: The proposed approach using the Azure Kinect provided higher accuracy compared to previous studies using the Kinect V2 potentially due to improved foot tracking by the Azure Kinect. Future studies should seek to optimize ground contact parameters and focus on regions of error between predicted and measured kinetics highlighted currently for further improvements in kinetic estimations. [ABSTRACT FROM AUTHOR]

Highlights • The Kinect™ provides valid and reliable hip and knee kinematics in stair negotiation. • The Kinect™ also provides valid and reliable hip and knee spatiotemporal data. • Ease of testing, cost and space requisites make the Kinect™ feasible for clinical use. • Further research is required to improve assessment for the ankle. Abstract Stair negotiation is one of the most challenging, yet frequently encountered, locomotor tasks in daily life. This study is the first attempt to investigate the capacity of the Kinect™ sensor to assess stair negotiation spatiotemporal and sagittal plane kinematic variables. The goal of this study was to examine the validity of the Kinect™ v2 sensor in assessing lower extremity kinematics and spatiotemporal parameters in healthy young individuals; and to demonstrate its potential as a low-cost stair gait analysis tool. Twelve healthy participants ascended and descended a 3-step custom-built staircase at their preferred speed, as spatiotemporal parameters and kinematics were extracted simultaneously using the Kinect™ and a three-dimensional motion analysis. Spatiotemporal measures included gait speed, swing phase time, and double stance time. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. Consistency (ICC 2,1) and absolute agreement (ICC 3,1) between the two systems were assessed using separate interclass correlations coefficients. In addition, ensemble curves and associated 90% confidence intervals (CI90) were generated for the hip, knee, and ankle kinematics to enable between system comparisons throughout the gait cycle. Results showed that the Kinect™ has the potential to be an effective clinical assessment device for sagittal plane hip and knee joint kinematics and for some spatiotemporal parameters during the stair gait negotiation. [ABSTRACT FROM AUTHOR]

Vision-based methods using RGB inputs for human pose estimation have grown in recent years but have undergone limited testing in clinical and biomechanics research areas like gait analysis. The purpose of the present study was to compare lower extremity kinematics during overground gait between a traditional marker-based approach and a commercial multi-view markerless system in a sample of subjects including young adults, older adults, and adults diagnosed with Parkinson's disease. A convenience sample of 35 adults between the age of 18–85 years were included in this study, yielding a total of 114 trials and 228 gait cycles that were compared between systems. A total of 30 time normalized waveforms, including three-dimensional joint centers, segment angles, and joint angles were compared between systems using root mean-squared error (RMSE), range of motion difference (ΔROM), Pearson correlation coefficients (r), and interclass correlation coefficients (ICC). RMSEs for joint center positions were less than 28 mm in all joints with correlations indicating good to excellent agreement. RMSEs for segment and joint angles were in range of previous results, with highest agreement between systems in the sagittal plane. ΔROM differences were within reference values that characterize clinical groups like Parkinson's disease, stroke, or knee osteoarthritis. Further improvements in pelvis tracking, markerless keypoint model definitions, and standardization of comparison study protocols are needed. Nevertheless, markerless solutions seem promising toward unrestricted motion analysis in biomechanics research and clinical settings. [ABSTRACT FROM AUTHOR]

Markerless motion capture methods are continuously in development to target limitations encountered in marker-, sensor-, or depth-based systems. Previous evaluation of the KinaTrax markerless system was limited by differences in model definitions, gait event methods, and a homogenous subject sample. The purpose of this work was to evaluate the accuracy of spatiotemporal parameters in the markerless system with an updated markerless model, coordinate- and velocity-based gait events, and subjects representing young adult, older adult, and Parkinson's disease groups. Fifty-seven subjects and 216 trials were included in this analysis. Interclass correlation coefficients showed excellent agreement between the markerless system and a marker-based reference system for all spatial parameters. Temporal variables were similar, except swing time which showed good agreement. Concordance correlation coefficients were similar with all but swing time showing moderate to almost perfect concordance. Bland-Altman bias and limits of agreement (LOA) were small and improved from previous evaluations. Parameters showed similar agreement across coordinate- and velocity-based gait methods with the latter showing generally smaller LOAs. Improvements in spatiotemporal parameters in the present evaluation was due to inclusion of keypoints at the calcanei in the markerless model. Consistency in the calcanei keypoints relative to heel marker placements may improve results further. Similar to previous work, LOAs are within boundaries to detect differences in clinical groups. Results support the use of the markerless system for estimation of spatiotemporal parameters across age and clinical groups, but caution should be taken when generalizing findings due to remaining error in kinematic gait event methods. [ABSTRACT FROM AUTHOR]

Gait analysis is used in research and clinical environments; yet several limitations exist in current methodologies. Markerless systems, utilizing high-speed video and artificial intelligence, eliminate most limitations encountered in marker-, depth-, or inertial sensor-based systems; however, further development is needed to improve their utility and accessibility in practice. Spatiotemporal parameters from 22 young adults were estimated during over-ground gait. Nine parameters were calculated using events determined from force plate information combined with foot segment tracking and from motion of the foot relative to the sacrum using marker-based and markerless tracking. Two-way mixed effects, single measurement, absolute agreement and relative consistency interclass correlation coefficients, Bland-Altman bias and limits of agreement, and Lin's concordance correlations were used to examine the validity of parameters from markerless tracking compared to parameters calculated from gait event methods using force plates and marker-based tracking. Gait speed, stride length, step length, cycle time, and step time from the markerless system all showed strong agreement with the force plate method. Other markerless-determined parameters were not as accurate. Differences in stride width are attributable to inconsistencies in foot segment definitions between models; while differences in stance time, swing time, and double limb support time were influenced by gait event methods. Mean differences in gait parameters were smaller than meaningful clinical differences in Parkinson's disease patients and within ranges of reference values for elderly subjects. Further studies are needed to determine the validity across other patient groups, but results support the continued development of markerless systems for over-ground gait analysis. [ABSTRACT FROM AUTHOR]