Movement research has typically been performed using three-dimensional (3D) marker-based motion capture, which is considered the “gold-standard” for biomechanical assessment. However, limitations ...exist due to the lack of portability, extensive preparation for data collection, marker placement training, error due to marker movement, and possible skin irritation due to marker adhesives. There is inherent error due to motion artifact stemming from skin movement and differences in marker placement between testers. Markerless motion capture systems are emerging as a new method of kinematic assessment. These methods require little preparation and there is no need to alter participant clothing. Markerless motion capture has also been validated for the lower extremity in healthy older adults during gait. However, it has not been validated for other populations or for the assessment of upper extremity (UE) motion. Therefore, the purpose of this study was to examine differences in calculated UE kinematics between marker-based and a markerless motion capture system. Participants attended two data collection sessions. Marker-based and markerless motion capture data was collected simultaneously while participants completed the Box and Blocks test (BBT). Kinematic and spatiotemporal data from both systems was exported using identical time series to ensure the same conditions for comparisons. Intraclass Correlation Coefficients (ICCs) were calculated to determine between session reliability for both systems on range of motion and peak joint angular data to ensure movement variability was not affecting measurement consistency. ICCs and Bland Altman statistics were also calculated between the systems. Root mean square deviation (RMSD) values were determined between demeaned UE joint angles for the two systems to examine movement pattern differences. The resulting between-session ICCs for each system showed that the markerless system shared similar reliability during this task as the marker-based system, further supporting the effect of variability on between-session reliability. Between-system ICCs resulted in good (0.7<ICC<0.9) to excellent (ICC>0.9) agreement. Bland Altman results confirmed the existence of measurement bias between the systems. RMSD values for all UE joint angles were found to be less than 6°. Overall, the results from this study support the use of markerless motion capture in clinical settings to examine upper extremity biomechanics in children.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
During the transitional time between Covid-19 and endemic, all Indonesian schools, and SMKN 47 in particular, had restricted educational opportunities. Due to challenges encountered by the instructor ...and 36 students majoring in accounting in grade 12, they were unable to carry out teaching and learning activities at school; art lesson formats with three-dimensional learning modules must be visualized in 3D. However, with hybrid teaching and learning utilizing presentations, displaying the learned 3D forms is less successful. In order to visualize three dimensions in fine arts classes using augmented reality technology, we require a new learning medium. The application of the Markerless Based Tracking approach in this work enables the presentation of a tracked 3D model in the surrounding environment in real-time by merging the actual and virtual worlds as if their boundaries did not exist. The augmented reality system scans flat surfaces utilizing points, as opposed to markers or other auxiliary media. Points used as pedestals or containers to elevate three-dimensional items. There are three sculptures and three traditional dwellings on show. Online examination outcomes average resulted a 78.2% that the AR Fine Arts application by instructors and students are therefore consistent and well accepted.
Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not ...clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data.
Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population.
A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking.
Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.
We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized ...decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.
This paper presents a system for performance-based character animation that enables any user to control the facial expressions of a digital avatar in realtime. The user is recorded in a natural ...environment using a non-intrusive, commercially available 3D sensor. The simplicity of this acquisition device comes at the cost of high noise levels in the acquired data. To effectively map low-quality 2D images and 3D depth maps to realistic facial expressions, we introduce a novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization. Formulated as a maximum a posteriori estimation in a reduced parameter space, our method implicitly exploits temporal coherence to stabilize the tracking. We demonstrate that compelling 3D facial dynamics can be reconstructed in realtime without the use of face markers, intrusive lighting, or complex scanning hardware. This makes our system easy to deploy and facilitates a range of new applications, e.g. in digital gameplay or social interactions.
We present a new algorithm for realtime face tracking on commodity RGB-D sensing devices. Our method requires no user-specific training or calibration, or any other form of manual assistance, thus ...enabling a range of new applications in performance-based facial animation and virtual interaction at the consumer level. The key novelty of our approach is an optimization algorithm that jointly solves for a detailed 3D expression model of the user and the corresponding dynamic tracking parameters. Realtime performance and robust computations are facilitated by a novel subspace parameterization of the dynamic facial expression space. We provide a detailed evaluation that shows that our approach significantly simplifies the performance capture workflow, while achieving accurate facial tracking for realtime applications.
Biomechanics: 40 Years On Hamill, Joseph; Knutzen, Kathleen M.; Derrick, Timothy R.
Kinesiology review (Champaign, Ill.),
08/2021, Volume:
10, Issue:
3
Journal Article
Peer reviewed
In the last 40 years, biomechanics has progressed significantly as a subdiscipline within kinesiology. The development of national and international societies dedicated to biomechanics and the ...increase in the number of scientific biomechanics journals has led to a growth in the biomechanics community. In the last few decades, the research focus in biomechanics has broadened substantially. With this diversity of focus, there have been many novel developments in new technologies used in biomechanics. Biomechanics has become an integral subdiscipline that has interfaced with several other areas in kinesiology and has contributed significantly to enhancing the knowledge base in all areas. Much of the development of biomechanics has resulted from improvements in the technology used in movement research. Although it may be overreaching to say that biomechanics can solve many human movement problems, the technology has allowed researchers to at least answer more comprehensive questions and answer them in greater depth.
There is a need within human movement sciences for a markerless motion capture system, which is easy to use and sufficiently accurate to evaluate motor performance. This study aims to develop a 3D ...markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), and these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. Quantitatively, of all the mean absolute errors calculated, approximately 47% were <20 mm, and 80% were <30 mm. However, 10% were >40 mm. The primary reason for mean absolute errors exceeding 40 mm was that OpenPose failed to track the participant's pose in 2D images owing to failures, such as recognition of an object as a human body segment or replacing one segment with another depending on the image of each frame. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30 mm or less.
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