A 2D Markerless Gait Analysis Methodology: Validation on Healthy Subjects

A 2D markerless technique is proposed to perform lower limb sagittal plane kinematic analysis using a single video camera. A subject-specific, multisegmental model of the lower limb was calibrated with the subject in an upright standing position. Ankle socks and underwear garments were used to track the feet and pelvis segments, whereas shank and thigh segments were tracked by means of reference points identified on the model. The method was validated against a marker based clinical gait model. The accuracy of the spatiotemporal parameters estimation was found suitable for clinical use (errors between 1% and 3% of the corresponding true values). Comparison analysis of the kinematics patterns obtained with the two systems revealed high correlation for all the joints (0.82<R2<0.99). Differences between the joint kinematics estimates ranged from 3.9 deg to 6.1 deg for the hip, from 2.7 deg to 4.4 deg for the knee, and from 3.0 deg to 4.7 deg for the ankle. The proposed technique allows a quantitative assessment of the lower limb motion in the sagittal plane, simplifying the experimental setup and reducing the cost with respect to traditional marker based gait analysis protocols

Application of video frame interpolation to markerless, single-camera gait analysis

In clinic settings, factors such as time, cost, expertise, and technology feasibility limit the use of instrumented biomechanical analysis. Recent advances in commercial markerless motion capture systems can address patient ease-of-use factors, but are high cost and require specialised equipment, dedicated spaces, and technical expertise. As such, they present similar limitations to biomechanical analyses in clinic settings. Single-camera pose estimation techniques have generated cautious optimism for markerless gait analysis. However, parameters derived using low-cost and low-sample rate cameras commonly used in clinic settings are not yet accurate enough to detect change in complex movement systems. Video frame interpolation is a single-step process that artificially increases the sample rate of videos. This study applied video frame interpolation to videos of walking and demonstrates improved precision for step, stance, swing and double support times, as well as marginal improvements to the precision of ankle and knee joint angles, derived by single-camera pose estimation. Video frame interpolation potentially represents a delimiting factor for gait analysis in clinic settings, as limiting factors such as time, cost, technology feasibility and patient ease-of-use can be minimised

Markerless Human Motion Analysis

Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative information about human motion is fundamental to study how our Central Nervous System controls and organizes movements to functionally evaluate motor performance and deficits. In the last decades, the research in this field has made considerable progress. State-of-the-art technologies that provide useful and accurate quantitative measures rely on marker-based systems. Unfortunately, markers are intrusive and their number and location must be determined a priori. Also, marker-based systems require expensive laboratory settings with several infrared cameras. This could modify the naturalness of a subject\u2019s movements and induce discomfort. Last, but not less important, they are computationally expensive in time and space. Recent advances on markerless pose estimation based on computer vision and deep neural networks are opening the possibility of adopting efficient video-based methods for extracting movement information from RGB video data. In this contest, this thesis presents original contributions to the following objectives: (i) the implementation of a video-based markerless pipeline to quantitatively characterize human motion; (ii) the assessment of its accuracy if compared with a gold standard marker-based system; (iii) the application of the pipeline to different domains in order to verify its versatility, with a special focus on the characterization of the motion of preterm infants and on gait analysis. With the proposed approach we highlight that, starting only from RGB videos and leveraging computer vision and machine learning techniques, it is possible to extract reliable information characterizing human motion comparable to that obtained with gold standard marker-based systems

Using the Microsoft Kinect to assess human bimanual coordination

Optical marker-based systems are the gold-standard for capturing three-dimensional (3D) human kinematics. However, these systems have various drawbacks including time consuming marker placement, soft tissue movement artifact, and are prohibitively expensive and non-portable. The Microsoft Kinect is an inexpensive, portable, depth camera that can be used to capture 3D human movement kinematics. Numerous investigations have assessed the Kinect\u27s ability to capture postural control and gait, but to date, no study has evaluated it\u27s capabilities for measuring spatiotemporal coordination. In order to investigate human coordination and coordination stability with the Kinect, a well-studied bimanual coordination paradigm (Kelso, 1984, Kelso; Scholz, & Schöner, 1986) was adapted. ^ Nineteen participants performed ten trials of coordinated hand movements in either in-phase or anti-phase patterns of coordination to the beat of a metronome which was incrementally sped up and slowed down. Continuous relative phase (CRP) and the standard deviation of CRP were used to assess coordination and coordination stability, respectively.^ Data from the Kinect were compared to a Vicon motion capture system using a mixed-model, repeated measures analysis of variance and intraclass correlation coefficients (2,1) (ICC(2,1)).^ Kinect significantly underestimated CRP for the the anti-phase coordination pattern (p \u3c.0001) and overestimated the in-phase pattern (p\u3c.0001). However, a high ICC value (r=.097) was found between the systems. For the standard deviation of CRP, the Kinect exhibited significantly higher variability than the Vicon (p \u3c .0001) but was able to distinguish significant differences between patterns of coordination with anti-phase variability being higher than in-phase (p \u3c .0001). Additionally, the Kinect was unable to accurately capture the structure of coordination stability for the anti-phase pattern. Finally, agreement was found between systems using the ICC (r=.37).^ In conclusion, the Kinect was unable to accurately capture mean CRP. However, the high ICC between the two systems is promising and the Kinect was able to distinguish between the coordination stability of in-phase and anti-phase coordination. However, the structure of variability as movement speed increased was dissimilar to the Vicon, particularly for the anti-phase pattern. Some aspects of coordination are nicely captured by the Kinect while others are not. Detecting differences between bimanual coordination patterns and the stability of those patterns can be achieved using the Kinect. However, researchers interested in the structure of coordination stability should exercise caution since poor agreement was found between systems

Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera

Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google’s pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01–0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments

Markerless Analysis of Gait Patterns in the Parkinson's Disease

In the clinical praxis Gait Analysis constitutes one of the key tools for the diagnose and follow up of some pathologies. The conventional approach includes the approximation of the skeleton by the placement and detection of a set of markers, this procedure has some relevant drawbacks and can be better approached by a markerless strategy, where the dynamics of the body are estimated without the use of any artifact. The main goal of this thesis is to present some markerless approaches that allow the characterization of the human gait. For the analysis pathological gait, we focus on the Parkinson's Disease, a neurodegenerative disorder whose symptoms results in diculty to perform complex motor task among themwalking.Resumen. En la práctica clínica el análisis de marcha es una de las herramientas más importantes para el diagnostico y seguimiento de algunas patologías. Este análisis incluye la aproximación del esqueleto mediante marcadores colocados sobre el paciente. Debido a que este procedimiento tiene algunas desventajas, se han desarrollado aproximaciones sin marcadores para el análisis de marcha, estas intentan capturar la dinámica del movimiento del paciente prescindiendo de cualquier artefacto. El objetivo principal de esta tesis es presentar algunas aproximaciones sin marcadores al análisis para marcha patológica. La patología que analizamos es la enfermedad de parkinson, un desorden neurodegenerativo cuyos síntomas resultan en la creciente dificultad para realizar tareas motoras complejas entre ellas la marcha.Maestrí