Markerless View Independent Gait Analysis with Self-camera Calibration

We present a new method for viewpoint independent markerless gait analysis. The system uses a single camera, does not require camera calibration and works with a wide range of directions of walking. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and use of marker based technology. Tests on more than 200 video sequences with subjects walking freely along different walking directions have been performed. The obtained results show that markerless gait analysis can be achieved without any knowledge of internal or external camera parameters and that the obtained data that can be used for gait biometrics purposes. The performance of the proposed method is particularly encouraging for its appliance in surveillance scenarios

Markerless human pose estimation for biomedical applications: a survey

Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait & posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care

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

Human Perambulation as a Self Calibrating Biometric

This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios

Concurrent Validity of a Custom Method for Markerless 3D Full-Body Motion Tracking of Children and Young Adults Based on a Single RGB-D Camera

Low-cost, portable RGB-D cameras with integrated body tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications. In this study, we investigated the concurrent validity of our custom tracking method based on RGB-D images with respect to a gold-standard marker-based system. Additionally, we analyzed the validity of the publicly available Microsoft Azure Kinect Body Tracking (K4ABT). We recorded 23 typically developing children and healthy young adults (aged 5 to 29 years) performing five different movement tasks using a Microsoft Azure Kinect RGB-D camera and a marker-based multi-camera Vicon system simultaneously. Our method achieved a mean per joint position error over all joints of 11.7 mm compared to the Vicon system, and 98.4% of the estimated joint positions had an error of less than 50 mm. Pearson's correlation coefficients r ranged from strong ( r =0.64) to almost perfect ( 0.99). K4ABT demonstrated satisfactory accuracy most of the time but showed short periods of tracking failures in nearly two-thirds of all sequences limiting its use for clinical motion analysis. In conclusion, our tracking method highly agrees with the gold standard system. It paves the way towards a low-cost, easy-to-use, portable 3D motion analysis system for children and young adults

Characterization of Infants' General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study

Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentation in the home may be used to estimate quantitative metrics predictive of movement disorders. This study explored if infants' GM may be successfully evaluated in a familiar environment by processing the 3D trajectories of points of interest (PoI) obtained from recordings of a single commercial RGB-D sensor. The RGB videos were processed using an open-source markerless motion tracking method which allowed the estimation of the 2D trajectories of the selected PoI and a purposely developed method which allowed the reconstruction of their 3D trajectories making use of the data recorded with the depth sensor. Eight infants' GM were recorded in the home at 3, 4, and 5 months of age. Eight GM metrics proposed in the literature in addition to a novel metric were estimated from the PoI trajectories at each timepoint. A pediatric neurologist and physiatrist provided an overall clinical evaluation from infants' video. Subsequently, a comparison between metrics and clinical evaluation was performed. The results demonstrated that GM metrics may be meaningfully estimated and potentially used for early identification of movement disorders

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

Knee joint sagittal plane movement in cerebral palsy: a comparative study of 2-dimensional markerless video and 3-dimensional gait analysis

8Background and purpose — Gait analysis is indicated in children with cerebral palsy (CP) to identify and quantify gait deviations. One particularly difficult-to-treat deviation, crouch gait, can progress in adolescence and ultimately limit the ability to ambulate. An objective quantitative assessment is essential to early identify progressive gait impairments in children with CP. 3-dimensional gait analysis (3D GA) is considered the gold standard, although it is expensive, seldom available, and unnecessarily detailed for screening and follow-up. Simple video assessments are time-consuming when processed manually, but more convenient if used in conjunction with video processing algorithms; this has yet been validated in CP. We validate a 2-dimensional markerless (2D ML) assessment of knee joint flexion/extension angles of the gait cycle in children and young adults with CP. Patients and methods — 18 individuals, mean age 15 years (6.5–28), participated. 11 had bilateral, 3 unilateral, 3 dyskinetic, and 1 ataxic CP. In the Gross Motor Function Classification System, 6 were at level I, 11 at level II, and 1 at level III. We compared 2D ML, using a single video camera with computer processing, and 3D GA. Results — The 2D ML method overestimated the knee flexion/extension angle values by 3.3 to 7.0 degrees compared with 3D GA. The reliability within 2D ML and 3D GA was mostly good to excellent. Interpretation — Despite overestimating, 2D ML is a reliable and convenient tool to assess knee angles and, more importantly, to detect changes over time within a follow-up program in ambulatory children with CP.openopenPantzar-Castilla, Evelina; Cereatti, Andrea; Figari, Giulio; Valeri, Nicolò; Paolini, Gabriele; Della Croce, Ugo; Magnuson, Anders; Riad, JacquesPantzar-Castilla, Evelina; Cereatti, Andrea; Figari, Giulio; Valeri, Nicolò; Paolini, Gabriele; Della Croce, Ugo; Magnuson, Anders; Riad, Jacque

A spatiotemporal deep learning approach for automatic pathological Gait classification

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.info:eu-repo/semantics/publishedVersio

Validation of two-dimensional video-based inference of finger kinematics with pose estimation

Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant’s hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand. Several recent studies have validated lower-limb kinematics obtained with a marker-free technique, OpenPose. This investigation examines the accuracy of OpenPose, when applied to images from single RGB cameras, against a ‘gold standard’ marker-based optical motion capture system that is commonly used for hand kinematics estimation. Participants completed four single-handed activities with right and left hands, including hand abduction and adduction, radial walking, metacarpophalangeal (MCP) joint flexion, and thumb opposition. The accuracy of finger kinematics was assessed using the root mean square error. Mean total active flexion was compared using the Bland–Altman approach, and the coefficient of determination of linear regression. Results showed good agreement for abduction and adduction and thumb opposition activities. Lower agreement between the two methods was observed for radial walking (mean difference between the methods of 5.03°) and MCP flexion (mean difference of 6.82°) activities, due to occlusion. This investigation demonstrated that OpenPose, applied to videos captured with monocular cameras, can be used for markerless motion capture for finger tracking with an error below 11° and on the order of that which is accepted clinically