scholarly journals Development, evaluation and application of a novel markerless motion analysis system to understand push-start technique in elite skeleton athletes

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259624
Author(s):  
Laurie Needham ◽  
Murray Evans ◽  
Darren P. Cosker ◽  
Steffi L. Colyer
Keyword(s):  
Computer Vision ◽  
Motion Capture ◽  
Force Production ◽  
Step Length ◽  
Learning System ◽  
Ground Contact ◽  
Mass Centre ◽  
Movement Data ◽  
Development Evaluation ◽  
Analysis System

This study describes the development, evaluation and application of a computer vision and deep learning system capable of capturing sprinting and skeleton push start step characteristics and mass centre velocities (sled and athlete). Movement data were captured concurrently by a marker-based motion capture system and a custom markerless system. High levels of agreement were found between systems, particularly for spatial based variables (step length error 0.001 ± 0.012 m) while errors for temporal variables (ground contact time and flight time) were on average within ± 1.5 frames of the criterion measures. Comparisons of sprinting and pushing revealed decreased mass centre velocities as a result of pushing the sled but step characteristics were comparable to sprinting when aligned as a function of step velocity. There were large asymmetries between the inside and outside leg during pushing (e.g. 0.22 m mean step length asymmetry) which were not present during sprinting (0.01 m step length asymmetry). The observed asymmetries suggested that force production capabilities during ground contact were compromised for the outside leg. The computer vision based methods tested in this research provide a viable alternative to marker-based motion capture systems. Furthermore, they can be deployed into challenging, real world environments to non-invasively capture data where traditional approaches are infeasible.

scholarly journals Basketball shooting technology based on acceleration sensor fusion motion capture technology

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Binbin Zhao ◽  
Shihong Liu
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Motion Capture ◽  
Visual Recognition ◽  
Gaussian Mixture ◽  
Human Vision ◽  
Support Vector ◽  
Background Removal ◽  
Graphics Processing ◽  
Basketball Shooting

AbstractComputer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

scholarly journals 3D kinematic analysis of patients’ gait before and after unilateral total hip replacement

2020 ◽  
Vol 22 (2) ◽  
Author(s):  
Kateřina Kolářová ◽  
Tomáš Vodička ◽  
Michal Bozděch ◽  
Martin Repko
Keyword(s):  
Range Of Motion ◽  
Total Hip Replacement ◽  
Kinematic Analysis ◽  
Hip Replacement ◽  
Sagittal Plane ◽  
Frontal Plane ◽  
Step Length ◽  
Total Hip ◽  
Before And After ◽  
Analysis System

Purpose: The purpose of the study was to describe changes in the kinematic parameters in the patients’ gait after total hip replacement. Methods: Research group of men in the end stage of osteoarthritis indicated to the THR (n = 10; age 54.1 ± 7.5 years; weight 92.2 ± 9.6 kg; height 179.7 ± 5.9 cm). All participants underwent a total of three measurements: before surgery, 3 and 6 months after the surgery. Using the 3D kinematic analysis system, the patients’ gait was recorded during each measurement session and kinematic analysis was carried out. The parameters that were monitored included the sagittal range of motion while walking in the ankle, the knee and the hip joints of the operated and the unoperated limb, and the range in the hip joint’s frontal plane, the rotation of pelvis in the frontal and transverse planes, as well as the speed of walking and the walking step length. Results: Significant increases were found in sagittal range of motion in the operated hip joint, sagittal range of motion in the ankle joint on the unoperated side and in the walking step length of the unoperated limb. Conclusions: During walking after a THR, the sagittal range of motion in the ankle of the unoperated limb increases. Also, the range of motion in the sagittal plane on the operated joint increases, which is related to the lengthening of the step of the unoperated lower limb.

scholarly journals Road Traffic Analysis Using Computer Vision

2021 ◽  
Vol 9 (8) ◽  
pp. 2002-2006
Author(s):  
Needhi U. Gaonkar
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Traffic Analysis ◽  
Transportation Systems ◽  
Visual Object ◽  
Highway Traffic ◽  
Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

The Intersections of Creativity, Technology, and the Mind

2020 ◽  
pp. 123-140
Author(s):  
Audri Phillips
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Motion Capture ◽  
Human Mind ◽  
Media Technology ◽  
The Senses ◽  
Creativity Studies ◽  
The Mind ◽  
Immersive Technologies

This chapter examines the relationships between technology, the human mind, and creativity. The chapter cannot possibly cover the whole spectrum of the aforementioned; nonetheless, it covers highlights that especially apply to new immersive technologies. The nature of creativity, creativity studies, the tools, languages, and technology used to promote creativity are discussed. The part that the mind and the senses—particularly vision—play in immersive media technology, as well as robotics, artificial intelligence (AI), computer vision, and motion capture are also discussed. The immersive transmedia project Robot Prayers is offered as a case study of the application of creativity and technology working hand in hand.

Predicting ground reaction and tibiotalar contact forces after total ankle arthroplasty during walking

2020 ◽  
Vol 234 (12) ◽  
pp. 1432-1444
Author(s):  
Yanwei Zhang ◽  
Zhenxian Chen ◽  
Yinghu Peng ◽  
Hongmou Zhao ◽  
Xiaojun Liang ◽  
...  
Keyword(s):  
Motion Capture ◽  
Multibody Dynamics ◽  
Contact Forces ◽  
Total Ankle Arthroplasty ◽  
Contact Model ◽  
Patient Specific ◽  
Ground Reaction Forces ◽  
Ground Contact ◽  
Ankle Arthroplasty ◽  
Reaction Forces

The motion capture and force plates data are essential inputs for musculoskeletal multibody dynamics models to predict in vivo tibiotalar contact forces. However, it could be almost impossible to obtain valid force plates data in old patients undergoing total ankle arthroplasty under some circumstances, such as smaller gait strides and inconsistent walking speeds during gait analysis. To remove the dependence of force plates, this study has established a patient-specific musculoskeletal multibody dynamics model with total ankle arthroplasty by combining a foot-ground contact model based on elastic contact elements. And the established model could predict ground reaction forces, ground reaction moments and tibiotalar contact forces simultaneously. Three patients’ motion capture and force plates data during their normal walking were used to establish the patient-specific musculoskeletal models and evaluate the predicted ground reaction forces and ground reaction moments. Reasonable accuracies were achieved for the predicted and measured ground reaction forces and ground reaction moments. The predicted tibiotalar contact forces for all patients using the foot-ground contact model had good consistency with those using force plates data. These findings suggested that the foot-ground contact model could take the place of the force plates data for predicting the tibiotalar contact forces in other total ankle arthroplasty patients, thus providing a simplified and valid platform for further study of the patient-specific prosthetic designs and clinical problems of total ankle arthroplasty in the absence of force plates data.

Obstacles Encountered by Learners, Instructors, Technical Support, and Librarians

2011 ◽  
pp. 306-319 ◽  
Author(s):  
Badrul H. Khan ◽  
Laura J. Cataldo ◽  
Ruth Bennet ◽  
Salvatore Paratore
Keyword(s):  
Learning Environment ◽  
Support Services ◽  
Learning System ◽  
Design Development ◽  
Flexible Learning ◽  
Learner Centered ◽  
Library Support ◽  
Development Evaluation ◽  
High Level ◽  
Goals And Objectives

To create a successful flexible learning system, one with a flexible learning environment where learning is actively fostered and supported, a systematic process of planning, design, development, evaluation, and implementation is needed. A flexible learning system should be meaningful not only to learners, but also to all stakeholder groups, including instructors and support services staff. For example, a flexible learning system is meaningful to learners when it is easily accessible, well designed, learner centered, affordable, and efficient and has a facilitated learning environment. When learners display a high level of participation and success in meeting a course’s goals and objectives, this can make learning meaningful to instructors. In turn, when learners enjoy all available technical and library support services provided in the course without any interruptions, it makes technical and library support services staff happy as they strive to provide easy-to-use, reliable services.

scholarly journals A Real-time Interactive Tai Chi Learning System Based on VR and Motion Capture Technology

2020 ◽  
Vol 174 ◽  
pp. 712-719
Author(s):  
Juan Liu ◽  
Yawen Zheng ◽  
Ke Wang ◽  
Yulong Bian ◽  
Wei Gai ◽  
...  
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Tai Chi ◽  
Learning System

scholarly journals IART: Inertial Assistant Referee and Trainer for Race Walking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 783 ◽  
Author(s):  
Teodorico Caporaso ◽  
Stanislao Grazioso
Keyword(s):  
Elite Athletes ◽  
Step Length ◽  
Ground Contact ◽  
Average Accuracy ◽  
Race Walking ◽  
Single Sensor ◽  
Ground Contact Time ◽  
Paved Road ◽  
Inertial Measurements

This paper presents IART, a novel inertial wearable system for automatic detection of infringements and analysis of sports performance in race walking. IART algorithms are developed from raw inertial measurements collected by a single sensor located at the bottom of the vertebral column (L5–S1). Two novel parameters are developed to estimate infringements: loss of ground contact time and loss of ground contact step classification; three classic parameters are indeed used to estimate performance: step length ratio, step cadence, and smoothness. From these parameters, five biomechanical indices customized for elite athletes are derived. The experimental protocol consists of four repetitions of a straight path of 300 m on a long-paved road, performed by nine elite athletes. Over a total of 1620 steps (54 sequences of 30 steps each), the average accuracy of correct detection of loss of ground contact events is equal to 99%, whereas the correct classification of the infringement is equal to 87% for each step sequence, with a 92% of acceptable classifications. A great emphasis is dedicated on the user-centered development of IART: an intuitive radar chart representation is indeed developed to provide practical usability and interpretation of IART indices from the athletes, coaches, and referees perspectives. The results of IART, in terms of accuracy of its indices and usability from end-users, are encouraging for its usage as tool to support athletes and coaches in training and referees in real competitions.

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