Marker-less motion capture for biomechanical analysis using the Kinect sensor BACHERLOR'S THESIS Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (original) (raw)
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Motion capture system has recently being brought to light and drawn much attention in many fields of research, especially in biomechanics. Marker-based motion capture systems have been used as the main tool in capturing motion for years. Marker-based motion capture systems are very pricey, lab-based and beyond reach of many researchers, hence it cannot be applied to ubiquitous applications. The game however has changed with the introduction of depth camera technology, a markerless yet affordable motion capture system. By means of this system, motion capture has been promoted as more portable application and does not require substantial time in setting up the system. Limitation in terms of nodal coverage of single depth camera has widely accepted but the performance of dual depth camera system is still doubtful since it is expected to improve the coverage issue but at the same time has bigger issues on data merging and accuracy. This work appraises the accuracy performance of dual depth camera motion capture system specifically for athletes' running biomechanics analysis. Kinect sensors were selected to capture motions of an athlete simultaneously in three-dimension, and fused the recorded data into an analysable data. Running was chosen as the biomechanics motion and interpreted in the form of angle-time, angleangle and continuous relative phase plot. The linear and angular kinematics were analysed and represented graphically. Quantitative interpretations of the result allowed the deep insight of the movement and joint coordination of the athlete. The result showed that the root-mean-square error of the Kinect sensor measurement to exact measurement data and rigid transformation were 0.0045 and 0.0077291 respectively. The velocity and acceleration of the subject were determined to be 3.3479 ms-1 and −4.1444 ms-2. The result showed that the dual Kinect camera motion capture system was feasible to perform athletes' biomechanics analysis.
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Modern biomechanical and clinical applications require the accurate capture of normal and pathological human movement without the artifacts associated with standard marker-based motion capture techniques such as soft tissue artifacts and the risk of artificial stimulus of taped-on or strapped-on markers. In this study, the need for new markerless human motion capture methods is discussed in view of biomechanical applications. Three different approaches for estimating human movement from multiple image sequences were explored. The first two approaches tracked a 3D articulated model in 3D representations constructed from the image sequences, while the third approach tracked a 3D articulated model in multiple 2D image planes. The three methods are systematically evaluated and results for real data are presented. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice.
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Archives of Physiotherapy and Global Researches, 2016
From a cibernetic approach, the body system can be defined like a net of structural and functional related subsystems with motor equifinality inside the concept of balance, energetic economy and comfort: therefore the ideal posture is the one that allows the maximum effectiveness of motor gesture, in absence of pain with the maximum energetic economy. The present study research is based on the necessity to individuate a real objective evaluation system of the postural parameters, inexpensive and of simple use compared to the evaluation instruments in use already scientifically validated. The instrument used in this study is the Microsoft Kinect®, gaming platform combined with the Xbox console. Created by Microsoft in the field of play, the Microsoft Kinect® for years has entertained millions of consumers through the Motion Capture System, the recording of movement through cameras and instant or deferred replay. The primary aim of this randomized controlled single-blind research is the demonstration that, despite being commonly defined objective the evaluation systems that utilize markers, is essential to look for an alternative evaluation method to minimize the systematic human error. The results demonstrate the real validity of Kinect®, and have verified the reliability of the data obtained from the assessment, showing the scientific reliability of this innovative objective evaluation method in rehabilitation-clinical field.