scholarly journals Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6094
Author(s):  
Maria Skublewska-Paszkowska ◽  
Pawel Powroznik ◽  
Edyta Lukasik
Keyword(s):  
Three Dimensional ◽  
Movement Analysis ◽  
Human Movement ◽  
Further Training ◽  
Convolutional Networks ◽  
Movement Recognition ◽  
Fuzzy Input ◽  
Task Recognition ◽  
Graph Neural Networks ◽  
And Training

Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.

scholarly journals Filtering Biomechanical Signals in Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4580
Author(s):  
Francesco Crenna ◽  
Giovanni Battista Rossi ◽  
Marta Berardengo
Keyword(s):  
Measurement Uncertainty ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Movement Analysis ◽  
Analytical Signal ◽  
Human Movement ◽  
Reference Points ◽  
Biomechanical Analysis ◽  
Method Performance ◽  
Acceleration Signal

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.

scholarly journals Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiatian Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Three Dimensional ◽  
Human Action Recognition ◽  
Human Movement ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Movement Recognition

In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.

scholarly journals Lower Limb Kinematics in Individuals with Hip Osteoarthritis during Gait: A Focus on Adaptative Strategies and Interlimb Symmetry

2021 ◽  
Vol 8 (4) ◽  
pp. 47
Author(s):  
Micaela Porta ◽  
Massimiliano Pau ◽  
Bruno Leban ◽  
Michela Deidda ◽  
Marco Sorrentino ◽  
...  
Keyword(s):  
Lower Limb ◽  
Functional Limitations ◽  
Hip Osteoarthritis ◽  
Movement Analysis ◽  
Human Movement ◽  
Point Of View ◽  
Negative Effects ◽  
Gait Patterns ◽  
Limb Kinematics

Among the functional limitations associated with hip osteoarthritis (OA), the alteration of gait capabilities represents one of the most invalidating as it may seriously compromise the quality of life of the affected individual. The use of quantitative techniques for human movement analysis has been found valuable in providing accurate and objective measures of kinematics and kinetics of gait in individuals with hip OA, but few studies have reported in-depth analyses of lower limb joint kinematics during gait and, in particular, there is a scarcity of data on interlimb symmetry. Such aspects were investigated in the present study which tested 11 individuals with hip OA (mean age 68.3 years) and 11 healthy controls age- and sex-matched, using 3D computerized gait analysis to perform point-by-point comparisons of the joint angle trends of hip, knee, and ankle. Angle-angle diagrams (cyclograms) were also built to compute several parameters (i.e., cyclogram area and orientation and Trend Symmetry) from which to assess the degree of interlimb symmetry. The results show that individuals with hip OA exhibit peculiar gait patterns characterized by severe modifications of the physiologic trend at hip level even in the unaffected limb (especially during the stance phase), as well as minor (although significant) alterations at knee and ankle level. The symmetry analysis also revealed that the effect of the disease in terms of interlimb coordination is present at knee joint as well as hip, while the ankle joint appears relatively preserved from specific negative effects from this point of view. The availability of data on such kinematic adaptations may be useful in supporting the design of specific rehabilitative strategies during both preoperative and postoperative periods.

scholarly journals Using a 4K three-dimensional exoscope system (Vitom 3D) for mastoid surgery during the coronavirus disease 2019 pandemic

2021 ◽  
pp. 1-3
Author(s):  
M Ally ◽  
P Kullar ◽  
G Mochloulis ◽  
A Vijendren
Keyword(s):  
Residual Disease ◽  
Elective Surgery ◽  
Three Dimensional ◽  
Depth Of Field ◽  
High Definition ◽  
Microscopic Surgery ◽  
Lateral Skull Base ◽  
Mastoid Surgery ◽  
And Training

Abstract Objective Microscopic surgery is currently considered the ‘gold standard’ for middle-ear, mastoid and lateral skull base surgery. The coronavirus disease 2019 pandemic has made microscopic surgery more challenging to perform. This work aimed to demonstrate the feasibility of the Vitom 3D system, which integrates a high-definition (4K) view and three-dimensional technology for ear surgery, within the context of the pandemic. Method Combined approach tympanoplasty and ossiculoplasty were performed for cholesteatoma using the Vitom 3D system exclusively. Results Surgery was performed successfully. The patient made a good recovery, with no evidence of residual disease at follow up. The compact system has excellent depth of field, magnification and colour. It enables ergonomic work, improved work flow, and is ideal for teaching and training. Conclusion The Vitom 3D system is considered a revolutionary alternative to microscope-assisted surgery, particularly in light of coronavirus disease 2019. It allows delivery of safe otological surgery, which may aid in continuing elective surgery.

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110381
Author(s):  
Xue Bai ◽  
Ze Liu ◽  
Jie Zhang ◽  
Shengye Wang ◽  
Qing Hou ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Region Of Interest ◽  
Dice Similarity Coefficient ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Dose Distributions ◽  
Convolutional Networks ◽  
Dose Prediction ◽  
Fully Convolutional Networks ◽  
Percentage Difference

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk ( p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model ( p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

Static and dynamic postural control in long-term microgravity: evidence of a dual adaptation

2001 ◽  
Vol 90 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Guido Baroni ◽  
Alessandra Pedrocchi ◽  
Giancarlo Ferrigno ◽  
Jean Massion ◽  
Antonio Pedotti
Keyword(s):  
Postural Control ◽  
Time Course ◽  
Center Of Mass ◽  
Three Dimensional ◽  
Principal Component ◽  
Movement Analysis ◽  
Trunk Flexion ◽  
Dynamic Movement ◽  
Static Body

The adaptation of dynamic movement-posture coordination during forward trunk bending was investigated in long-term weightlessness. Three-dimensional movement analysis was carried out in two astronauts during a 4-mo microgravity exposure. The principal component analysis was applied to joint-angle kinematics for the assessment of angular synergies. The anteroposterior center of mass (CM) displacement accompanying trunk flexion was also quantified. The results reveal that subjects kept typically terrestrial strategies of movement-posture coordination. The temporary disruption of joint-angular synergies observed at subjects' first in-flight session was promptly recovered when repetitive sessions in flight were analyzed. The CM anteroposterior shift was consistently <3–4 cm, suggesting that subjects could dynamically control the CM position throughout the whole flight. This is in contrast to the observed profound microgravity-induced disruption of the quasi-static body orientation and initial CM positioning. Although this study was based on only two subjects, evidence is provided that static and dynamic postural control might be under two separate mechanisms, adapting with their specific time course to the constraints of microgravity.

scholarly journals Computational Modeling: Human Dynamic Model

2021 ◽  
Vol 15 ◽  
Author(s):  
Lijia Liu ◽  
Joseph L. Cooper ◽  
Dana H. Ballard
Keyword(s):  
Dynamic Model ◽  
Degrees Of Freedom ◽  
Dynamic Models ◽  
Movement Analysis ◽  
Human Movement ◽  
Uncontrolled Manifold ◽  
Human In The Loop ◽  
Joint Forces ◽  
Internal Joint ◽  
Movement Function

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.

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