Markerless motion capture system via kinematic analysis of angular lower limb

The introduction of markerless sensor technology in motion capture system offers a comparable alternative to the conventional systems by employing infrared-depth sensors and retaining the ability to acquire two (2D) and three-dimensional (3D) data on human movement. However, its accuracy is often questioned compared to the established technologies such as passive marker systems. Therefore, this study sets an alternative method to evaluate Kinect Xbox 360 markerless system accuracy based on two positioning coordinates of two pairs of sensors. Through this approach, the length of lower limb segments was measured in 2D and 3D on each motion frame while performing squat movement and compared with the actual segment length. Interestingly, all segment lengths in the 3D showed excellent accuracy with the actual length of the segment. The angle of knee joints was also evaluated to identify the types of squat movements. The same evaluation is also used for the accuracy of a passive marker system while capturing the turning kick motion. In addition, the velocity of the knee joint was also studied at each phase of movement to determine the speed and angular of the knee required to enable the subject's foot to reach the target. For validation purposes, simulations of all recorded motions were implemented to evaluate the squat and the phases of movement in a turning kick from a visual angle. Successfully, the study was able to compare the accuracy and precision of the system constructed using lower limb data relative to the passive marker system using actual lower limb data. The markerless gave a remarkable difference value between the highest and lowest percentage coefficients of variation with 3.90%, while the passive marker system gave 5.72%. It is suggested that the multi-camera markerless motion capture system used in this study be used only for applications that do not require a significant level of accuracy such as animations, gaming and recreational sports analyses

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