Markerless Motion Capture in the Crowd

This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

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

Comparison Marker-Based and Markerless Motion Capture Systems in Gait Biomechanics During Running

Background: Markerless (ML) motion capture systems have recently become available for biomechanics applications. Evidence has indicated the potential feasibility of using an ML system to analyze lower extremity kinematics. However, no research examined ML systems’ estimation of the lower extremity joint moments and powers. Objectives: This study primarily aimed to compare lower extremity joint moments and powers estimated by marker-based (MB) and ML motion capture systems during treadmill running. The secondary purpose was to investigate if movement’s speed would affect the ML’s performance. Methods: Sixteen volunteers ran on a treadmill for 120 s for each trial at the speed of 2.24, 2.91, and 3.58 m/s, respectively. The kinematic data were simultaneously recorded by 8 infrared cameras and 8 high-resolution video cameras. The force data were recorded via an instrumented treadmill. Results: Compared to the MB system, the ML system estimated greater increased hip and knee joint kinetics with faster speeds during the swing phase. Additionally, increased greater ankle joint moments with speed estimated by the ML system were observed at the early swing phase. In contrast, the greater ankle joint powers occurred at the initial stance phase. Conclusions: These observations indicated that inconsistent segment pose estimations (mainly the center of mass estimated by ML was farther away from the relevant distal joint center) might lead to systematic differences in joint moments and powers estimated by MB and ML systems. Despite the promising applications of the ML system in clinical settings, systematic ML overestimation requires extra attention

COMPARISON OF MARKERLESS AND MARKER-BASED MOTION CAPTURE FOR ESTIMATING EXTERNAL MECHANICAL WORK IN TENNIS: A PILOT STUDY

This pilot study assessed the accuracy of markerless motion capture to estimate the external mechanical work performed during tennis serves. One tennis player performed 9 serves whilst motion data were captured concurrently with a criterion marker-based and a custom markerless system (utilising HRNet and OpenPose). Centre of mass kinetic and potential energy were calculated and used to compute external mechanical work for all 3 approaches. Markerless methods yielded differences of 2-3% (HRNet) and 6-9% (OpenPose) for external work compared to the criterion measure, with the former regarded as a high level of agreement. Our markerless system, paired with HRNet, shows promise for accurately estimating external work during the tennis serve and has potential to provide players and coaches with a non-invasive tool for monitoring training ‘load’ in the field

ESTIMATION OF GROUND REACTION FORCES FROM MARKERLESS KINEMATICS AND COMPARISON AGAINST MEASURED FORCE PLATE DATA

This study investigated how accurately ground reaction forces (GRFs) can be estimated from body centre of mass (BCOM) motion derived using markerless motion capture. Fifteen participants performed a countermovement jump (CMJ) on, and a running trial across, force plates. Kinematics captured using markerless and marker-based systems were used to drive IK-constrained OpenSim models. The resulting BCOM displacements were double differentiated to inversely estimate GRFs and compared to force plate data. Markerless-derived estimates were similar to measured GRFs (RMSD = ~70-150 N) and vertical peak force, impulse and rate of force development were also accurately estimated (effect sizes \u3c 0.2), similarly to marker-based outputs. Our markerless workflow shows promise for the estimation of vertical GRF parameters out in the field, without markers or force plates

Markerless Kinematics of Pediatric Manual Wheelchair Mobility

Pediatric manual wheelchair users face substantial risk of orthopaedic injury to the upper extremities, particularly the shoulders, during transition to wheelchair use and during growth and development. Propulsion strategy can influence mobility efficiency, activity participation, and quality of life. The current forefront of wheelchair biomechanics research includes translating findings from adult to pediatric populations, improving the quality and efficiency of care under constrained clinical funding, and understanding injury mechanisms and risk factors. Typically, clinicians evaluate wheelchair mobility using marker-based motion capture and instrumentation systems that are precise and accurate but also time-consuming, inconvenient, and expensive for repeated assessments. There is a substantial need for technology that evaluates and improves wheelchair mobility outside of the laboratory to provide better outcomes for wheelchair users, enhancing clinical data. Advancement in this area gives physical therapists better tools and the supporting research necessary to improve treatment efficacy, mobility, and quality of life in pediatric wheelchair users. This dissertation reports on research studies that evaluate the effect of physiotherapeutic training on manual wheelchair mobility. In particular, these studies (1) develop and characterize a novel markerless motion capture-musculoskeletal model systems interface for kinematic assessment of manual wheelchair propulsion biomechanics, (2) conduct a longitudinal investigation of pediatric manual wheelchair users undergoing intensive community-based therapy to determine predictors of kinematic response, and (3) evaluate propulsion pattern-dependent training efficacy and musculoskeletal behavior using visual biofeedback.Results of the research studies show that taking a systems approach to the kinematic interface produces an effective and reliable system for kinematic assessment and training of manual wheelchair propulsion. The studies also show that the therapeutic outcomes and orthopaedic injury risk of pediatric manual wheelchair users are significantly related to the propulsion pattern employed. Further, these subjects can change their propulsion pattern in response to therapy even in the absence of wheelchair-based training, and have pattern-dependent differences in joint kinematics, musculotendon excursion, and training response. Further clinical research in this area is suggested, with a focus on refining physiotherapeutic training strategies for pediatric manual wheelchair users to develop safer and more effective propulsion patterns

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Clinical patient tracking in the presence of transient and permanent occlusions via geodesic feature

This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clinicians must remain close to the patient during training sessions, the patient is usually under permanent and transient occlusions due to the training equipment and the movements of the attending clinicians. These occlusions can significantly degrade the accuracy of existing human tracking methods. To improve the data association problem in these circumstances, we present a new global feature based on the geodesic distances of surface mesh points to a set of anchor points. Transient occlusions are handled via a multi-hypothesis tracking framework. To evaluate the method, we simulated different occlusion sizes on a data set captured from a human in varying movement patterns, and compared the proposed feature with other tracking methods. The results show that the proposed method achieves robustness to both surface deformations and transient occlusions

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