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

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