Duplex camera portable motion capture system for single athlete’s performance analysis

In recent years, introduction of depth camera in motion capture have caught a massive attention by both researchers and biomechanists. Therefore, high-performance sports certainly require motion capture technology to compete with other competitors. Thus, a study involving quantitative analysis is important to obtain a more accurate analysis. The purpose of this study is to develop duplex motion capture system for biomechanical analysis of sports. The visual sensor used in this research consists of two Kinect sensors Microsoft. The case studies were focused on a single athlete for sepak takraw pre-serve and bunga silat at any single time. MATLAB and Software Development Kit (SDK) by Microsoft were used as software developing platform. Accuracy between software developed in MATLAB platform and SDK were compared and the best platform in term of performance and accuracy were chosen. “Bunga silat” in Silat and sepak takraw pre-serve were chosen as the case studies to justify the feasibility of the system. This study also examined the capability of Kinect Xbox 360° sensor and software to be used in biomechanical analysis. Result obtained revealed significant achievement in terms of motion capture system in the biomechanical analysis on certain sports. For Sepak Takraw, the foot-to-foot distance and angle of knee before serve has been used as a parameter to determine the speed of the ball to be serviced. In this research the highest ball speed recorded is 69km/h with 1.0664m of foot-to-foot distance and 147.20° of right knee 153.52° for the left knee at the pre-serve phase. Whereas for Silat, it shows that the amateur motion is very much similar from the practitioners. The closer the mean and standard deviation value to unity the more accurate the movement referred to the benchmark stated. Therefore, to increase the accuracy of movement execution, the subject needs to repeat the movement execution until perfection which is set by the teacher. In summary, this research successfully described an athlete’s performance quantitatively using motion capture system

Performance of Dual Depth Camera Motion Capture System for Athletes’ Biomechanics Analysis

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

Forecasting modelling of cockles in Malaysia by using time series analysis

Cockle farmed in Malaysia are from Anadara genes and Arcidae family which known as blood cockle. Normally, it was found in the farmed around mangrove estuary areas in the muddy and sandy shores. This study aims to predict the production of cockle to ensure sure the cockle supplies are synchronised with the demand. Then, based on the demand, the prediction result could be used to make decision either to import or export the cockle. The data were taken from the Department of Fisheries Malaysia (DFM) and it has cyclic pattern data. There are two methods used in this study which are Holt-Linear method and Auto regressive moving average (ARMA). In determining the best fitted model between the two methods, the mean square error (MSE) values will be compared and the lowest value of MSE will assign as the best model. Result shows that ARMA(1,1) is the best model compared to Holt-Linear. Therefore, ARMA(1,1) model will be used to forecast the production of cockle in Malaysia

Analysis of Musculoskeletal Disorder Due To Working Postures via Dual Camera Motion Capture System

Ergonomic are known as the study of work. It helps the worker to fit with the environment of the workplace for example the tools, equipments and the work station. Poor ergonomic practice can affect the performance of the worker and the quality of the product besides can cause loss to the company. This study have three main purpose which is to establish the optimal set up of the dynamic RULA analysis in UTHM, to compare the performance of static RULA analysis with the current dynamic RULA analysis and to identify the effect of current working posture to the musculoskeletal disorder of the university. The ergonomic tools that are use in this study are Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) and Rapid upper limb assessment (RULA). Besides that, motion captures system and Kinect camera is use for 3D dynamic RULA analysis. Besides that, 2D static analysis and 3D dynamic analysis must run the experiment and record the video of the subject motion simultaneously to ensure the similarity in terms of result obtain. Thus, this research finds that the 3D dynamic analysis is more accurate compare with the 2D static analysis. This can be proved by comparing the length of the joint point of 2D static analysis and 3D dynamic analysis with the actual length. 3D dynamic method provided 3 axes while the other method only provided 2 axes. Besides that, 3D dynamic method are analyze by the program while 2D static method are analyze manually by the user that not entirely accurate. The result for comparing the performance of the 2D static analysis and 3D dynamic analysis shows that the respondent 1 and 2 have high risk to get neck pain based on the 3D dynamic analysis RULA score. CMDQ analysis shows that the body part of respondent 1 and 2 that are most probably affected by the MSD is leg.Â

Performance of Dual Depth Camera Motion Capture System for Athletes’ Biomechanics Analysis

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

Performance of Dual Depth Camera Motion Capture System for Athletes’ Biomechanics Analysis

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