Lean Manufacturing System (LMS) implementations in Malaysia's automotive industry has not been extensive in its expected reach, as extensive inquiries revealed it being adopted as a "pick-and-choose" system for certain processes or only upon determined levels within the industry. Current implementation strategy does not permit the industry to gain total benefits from the system itself. Undeniably, a few significant factors are being given less significance in multiple stages of LMS' execution. Employee involvement and employee empowerment have been identified as part of these contributing factors in a successful implementation of LMS in an organization. However, important criterion with its contributing aspects of these factors is not given the necessary attention, translating into a lamer impact upon companies embarking on a LMS deployment. This paper examines the impact of these two factors in the implementation of a lean manufacturing system towards achieving the organizational performances in the automotive industry. A questionnaire-survey was administered to gauge the impact of these two factors in an implementation process of a lean manufacturing system and later analyzing the effect towards their organizational performances. Data from 204 automotive parts manufacturers were gathered and analyzed. The correlation between the influencing factors, 5 lean activities and 6 organizational performances were measured. The results gained suggest that the integration between employee involvement and employee empowerment will be a valuable critical organizational capability impacting organizational performances towards the successful implementation of LMS in the Malaysian automotive industry.

Image processing is mostly used for exploring image behaviour. There are several steps in image processing. Image acquisition, pre-processing, feature extraction, and classification are the processes used for the detection of human movement based on high-level feature extraction (HLFE), in which HLFE was used for feature extraction in this paper. This study proposed the use of background subtraction and frame difference. This research was conducted to analyse the difference of background subtraction and frame difference methods based on movement of human. Movement of human detected by using feature extraction were centroid image technique used. Furthermore, support vector machine (SVM) was used for classification.

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Human action analysis is an enthralling area of research in artificial intelligence, as it may be used to improve a range of applications, including sports coaching, rehabilitation, and monitoring. By forecasting the body's vital position of posture, human action analysis may be performed. Human body tracking and action recognition are the two primary components of video-based human action analysis. We present an efficient human tracking model for squat exercises using the open-source MediaPipe technology. The human posture detection model is used to detect and track the vital body joints within the human topology. A series of critical body joint motions are being observed and analysed for aberrant body movement patterns while conducting squat workouts. The model is validated using a squat dataset collected from ten healthy people of varying genders and physiques. The incoming data from the model is filtered using the double exponential smoothing method;the Mean Squared Error between the measured and smoothed angles is determined to classify the movement as normal or abnormal. Level smoothing and trend control have parameters of 0.8928 and 0.77256, respectively. Six out of ten subjects in the trial were precisely predicted by the model. The mean square error of the signals obtained under normal and abnormal squat settings is 56.3197 and 29.7857, respectively. Thus, by utilising a simple threshold method, the low-cost camera-based squat movement condition detection model was able to detect the abnormality of the workout movement.