Conventional Training Methods Research Articles

Continual Learning for Cuffless Blood Pressure Measurement using PPG and ECG Signals.

Although numerous studies have been conducted on cuffless blood pressure (BP) estimation using machine learning methods, most of the data-driven models are static, with model parameters fixed after training is complete. However, BP is dynamic and the performance would degrade for a static model when the to-be predicted BP distribution deviates from the training BP distribution. In this paper, we propose a continual learning (CL) framework in which deep learning models are developed to learn dynamically and continuously for arterial BP (ABP) estimation with photoplethysmography (PPG) and electrocardiogram (ECG) waveforms. The effectiveness of the CL model is validated on UCI Repository and MIMIC-III database with a total of 132 individual samples, and compared with conventional training method. It was found that the CL model improved the ABP estimation accuracy in terms of mean absolute error (MAE) by 17.47% on average compared with conventional training model. Furthermore, the improvement increased with the variability of ABP. These results demonstrate that CL model has potential to estimate dynamic ABP, which has been challenging with conventional training.

Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors.

In recent years, machine learning has been utilized in health informatics and sports science. There is a great demand and development potential for combining the Internet of Things (IoT) and artificial intelligence (AI) to be applied to football sports. The conventional teaching and training methods of football sports have limited collection and mining of real raw data using wearable devices, and lack human motion capture and gesture recognition based on sports science theories. In this study, a low-cost AI + IoT system framework is designed to recognize football motion and analyze motion intensity. To reduce the communication delay and the computational resource consumption caused by data operations, a multitask learning model is designed to achieve motion recognition and intensity estimation. The model can perform classification and regression tasks in parallel and output the results simultaneously. A feature extraction scheme is designed in the initial data processing, and feature data augmentation is performed to solve the small sample data problem. To evaluate the performance of the designed football motion recognition algorithm, this paper proposes a data extraction experimental scheme to complete the data collection of different motions. Model validation is performed using three publicly available datasets, and the features learning strategies are analyzed. Finally, experiments are conducted on the collected football motion datasets and the experimental results show that the designed multitask model can perform two tasks simultaneously and can achieve high computational efficiency. The multitasking single-layer long short-term memory (LSTM) network with 32 neural units can achieve the accuracy of 0.8372, F1 score of 0.8172, mean average precision (mAP) of 0.7627, and mean absolute error (MAE) of 0.6117, while the multitasking single-layer LSTM network with 64 neural units can achieve the accuracy of 0.8407, F1 score of 0.8132, mAP of 0.7728, and MAE of 0.5966.

SRoUDA: Meta Self-Training for Robust Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasingly more popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and fine-tuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy.

The Influence of Kinetic Life Training Methods and Concentration on the Dwi Hurigi Kick Skills of Taekwondo Athletes

So, this study aims to improve the skills of the double hurigi kick through the life kinetic training method and concentration on the double hurigi kick of Tae Kwon Do athletes. This research is a type of quantitative research with experimental methods and using a 2x2 factorial design. The aim of this study was to reveal the effect of life kinetic and concentration training methods on the two hurigi kick skills of Tae Kwon Do athletes. The population in this study were Tae Kwon Do athletes at the Tunas Harapan Academy in Bengkulu. The sample in this study were Tae Kwon Do Athletes at the Tunas Harapan Academy, totaling 36 athletes. The statistical analysis used in this study was the two-way ANOVA test to test the hypothesis that the Kinetic Life Training Method accompanied by the Concentration level has an influence on the Two Hurigi Kick Skills of Tae Kwon Do Athletes. The results of the research show that the Training MethodLifeKinetic which is accompanied by a level of concentration has an influence on the Kick Skills of the Two Hurigi Tae Kwon Do Athletes. The results of the study show that (1) there are differences in training methodslife kinetic high concentration level with low concentration level kinetic life training methods on increasing kick technique skillsTwo hurigi Kepahiang Tae Kwon Do athlete. (2) There are differences in training methodsconventionalhigh level of concentration with conventional methods of low concentration on increasing kick technique skillsTwo hurigi Kepahiang Tae Kwon Do athletes (3) There are differences in training methodslife kinetic high concentration level with conventional high concentration level training methods on improving kick technique skillsTwo hurigi Kepahiang Tae Kwon Do athlete. (4) There are differences in training methodslife kinetic Low concentration level with conventional low concentration level training methods on increasing kick technique skillsTwo hurigi Kepahiang Tae Kwon Do athlete. The conclusion in this study is that there is a significant difference in effect between the kinetic life training method and the conventional training method.

Effect of Perceptual Training with Sound-Guided and Kinesthetic Feedback on Human 3D Sound Localization Capabilities.

In this paper, we experimentally investigate how the 3D sound localization capabilities of the blind can improve through perceptual training. To this end, we develop a novel perceptual training method with sound-guided feedback and kinesthetic assistance to evaluate its effectiveness compared to conventional training methods. In perceptual training, we exclude visual perception by blindfolding the subjects to apply the proposed method to the visually impaired. Subjects used a specially designed pointing stick to generate a sound at the tip, indicating localization error and tip position. The proposed perceptual training aims to evaluate the training effect on 3D sound localization, including variations in azimuth, elevation, and distance. The six days of training based on six subjects resulted in the following outcomes: (1) In general, accuracy in full 3D sound localization can be improved based on training. (2) Training based on relative error feedback is more effective than absolute error feedback. (3) Subjects tend to underestimate distance when the sound source is near, less than 1000 mm, or larger than 15° to the left, and overestimate the elevation when the sound source is near or in the center, and within ±15° in azimuth estimations.

Escape rooms technology as a way of teaching mathematics to secondary school students.

The aim of this study is to determine whether Escape Rooms can be used as an active methodological approach for the purpose of teaching mathematics. The research adapted a quantitative approach via an experimental design. Two different study groups were established, the first of which comprised the control group who were taught using conventional training methods, and the second consisted of the experimental group, who were taught using a novel approach incorporating an Escape Room activity. The participants included 80 students who were all attending secondary school within the Kingdom of Saudi Arabia. The findings revealed how the activity involving the Escape Room led to a significant enhancement in the student's motivation, achievement and autonomy. The conclusion can be drawn that using an Escape Room in mathematics teaching can enhance learning achievement, anxiety, motivation and autonomy, with students' negative attitudes regarding mathematics learning considered as an important variable, particularly with respect to autonomy and motivation. Hence, Escape Rooms can potentially be more effective than conventional methods for teaching mathematics.

Mobile-Based Training and Certification Framework for Teachers’ Professional Development

This study highlights the challenges faced in providing professional development programs for teachers, including limited resources and conventional training methods. Mobile technology, with its flexibility and cost-effectiveness, is increasingly being used as a part of professional development programs, but existing frameworks do not adequately support the integration of technology. This study proposes a mobile-based training certification framework that includes support for mobile phones, a training curriculum and assessment, expert support, and CPD evaluation. The framework was piloted with 35 participants from Pakistan and Saudi Arabia, with positive results indicating that mobile-based training is effective in enhancing teachers’ learning outcomes. According to the teachers’ responses, 100% agreed that this was a groundbreaking endeavor and expressed excitement to use it. Additionally, 98% found mobile learning easier and more enjoyable than traditional methods. The study recommends the implementation of the mobile-based training certification framework to improve teachers’ professional development and bridge the gap between traditional training methods and the needs of modern-day educators. The study also highlights the potential of mobile-based training to improve student outcomes by developing the knowledge and skills of teachers.

Meta-Analysis of Circuit, Interval, and Conventional Training Methods Towars Motivation and Mental Toughness among Elementary School Hockey Athletes

In high-intensity and high-performance training, athletes sometimes face physical or mental aspects. The observed thing is that there is less or no research on the training method's effect on motivation. Studies linking exercise and its impact on the basis and mental toughness are lacking. This study aims to see the effects of particular training methods, namely circuit training methods, interval training, and conventional training, on motivation and mental toughness in primary school hockey athletes. Based on references from past studies presented in the Literature Highlights, it has been found that most studies that researchers have carried out are in the form of a combination of two research methods, namely the quasi-experimental type and the survey type. Sports Motivation Scale (SMS) or Sports Motivation Scale is widely used in studies related to sports motivation. In addition, another instrument found as a result of references from past studies used by researchers is the Mental Toughness Questionnaire (MTQ). In this pilot study, the respondents chosen by the researcher are students aged 11 to 12 years who are currently in Years 5 and 6 in the Malaysian education system. A total of 20 students involved in the game of hockey who are hockey athletes were selected based on the purposive sampling method. The findings of the pilot study found that the reliability and validity of the research instrument with a Cronbach alpha value >0.7 (excellent and acceptable). Thus, the findings of this pilot study show that this existing instrument can be used in the actual study later.

Real-time pneumonia prediction using pipelined spark and high-performance computing.

Pneumonia is a respiratory disease caused by bacteria; it affects many people, particularly in impoverished countries where pollution, unclean living standards, overpopulation, and insufficient medical infrastructures are prevalent. To guarantee curative therapy and boost survival chances, it is vital to detect pneumonia soon enough. Imaging using chest X-rays is the most common way of detecting pneumonia. However, analyzing chest X-rays is a complex process vulnerable to subjective variation. Moreover, the data available is growing exponentially, and it will take hours and days to train the model to predict pneumonia. Timely prediction is significant to guarantee a better cure and treatment. Existing work provided by different authors needs more precision, and the computation time for predicting pneumonia is also much longer. Therefore, there is a requirement for early forecasting. Using X-ray picture samples, the system must have a continuous and unsupervised learning system for early diagnosis. In this article, the training time of the model is accelerated using the distributed data-parallel approach and the computational power of high-performance computing devices. This research aims to diagnose pneumonia using X-ray pictures with more precision, greater speed, and fewer processing resources. Distributed deep learning techniques are gaining popularity owing to the rising need for computational resources for deep learning models with several parameters. In contrast to conventional training methods, data-parallel training enables several compute nodes to train massive deep-learning models to improve training efficiency concurrently. Deploying the model in Spark solves the scalability and acceleration. Spark's distributed processing capability reads data from multiple nodes, and the results demonstrate that training time can be drastically reduced by utilizing these techniques, which is a significant necessity when dealing with large datasets. The proposed model makes the prediction 1.5 times faster than the traditional CNN model used for pneumonia prediction. The model also achieved an accuracy of 98.72%. The speed-up varying from 1.2 to 1.5 was obtained in the synchronous and asynchronous parallel model. The speed-up is reduced in the parallel asynchronous model due to the presence of straggler nodes.

Research on Driving Obstacle Detection Technology in Foggy Weather Based on GCANet and Feature Fusion Training.

The issues of the degradation of the visual sensor's image quality in foggy weather and the loss of information after defogging have brought great challenges to obstacle detection during autonomous driving. Therefore, this paper proposes a method for detecting driving obstacles in foggy weather. The driving obstacle detection in foggy weather was realized by combining the GCANet defogging algorithm with the detection algorithm-based edge and convolution feature fusion training, with a full consideration of the reasonable matching between the defogging algorithm and the detection algorithm on the basis of the characteristics of obvious target edge features after GCANet defogging. Based on the YOLOv5 network, the obstacle detection model is trained using clear day images and corresponding edge feature images to realize the fusion of edge features and convolution features, and to detect driving obstacles in a foggy traffic environment. Compared with the conventional training method, the method improves the mAP by 12% and recall by 9%. In contrast to conventional detection methods, this method can better identify the image edge information after defogging, which significantly enhances detection accuracy while ensuring time efficiency. This is of great practical significance for improving the safe perception of driving obstacles under adverse weather conditions, ensuring the safety of autonomous driving.

Use of virtual reality technology in chainsaw operations, education and training

AbstractWood harvesting operations represent one of the industrial sectors with the highest risk of accidents at work. In semi-mechanized logging operations, the great majority of accidents generally occur using chainsaws during tree felling. Unfortunately, these situations frequently cause serious injuries and even the deaths of workers. In numerous cases, the accidents occurred to people who were badly trained and inexperienced, with a short employment history. One solution to reduce these harmful events is to support workers employed in this sector with training applications. This paper presents a description of a research and training programme for operating with chainsaws in tree felling operations using a Virtual Reality (VR) application called ForestVRoom. This innovative education method was compared with the traditional theoretical lesson based on a slide show. The results of the training were verified through a statistical analysis of questionnaires administered to a sample of 45 students in undergraduate and master’s degree programmes at the School of Agriculture and Forestry at the University of Florence. We have shown that the combination of traditional didactics and symmetrical collaborative VR leads to an increase in knowledge and especially in the ability to understand situations in a real forest site. VR is a useful teaching tool that can complement conventional training methods because of its potential to provide an immersive and attractive experience that facilitates learning and recognition of situations in forest sites.

The effects of eccentric arm crank training - A pilot study

Background
 Strength training plays an important role in a holistic training approach, not only for athletes but also for patients looking to (re)gain strength during rehabilitation. In the past few years, eccentric strength training has received increased attention as this type of training increases muscle strength more than conventional training methods (Douglas et al., 2017). Since eccentric strength training includes higher levels of mechanical tension and leads to greater exercise-induced muscle damage compared to concentric training, the training protocol should be implemented with caution and effects may appear after a longer recovery period (Douglas et al., 2017). Whereas lower body training has been studied extensively, only little data is available regarding eccentric strength training for the upper body, particularly in either wheelchair athletes or individuals with a spinal cord injury. We wanted to investigate if strength training on a specially designed eccentric arm crank ergometer (Krafttraining mittels exzentrischer Handkurbel, KREHA) can improve performance as well as the maximum strength of the upper body. In this pilot study, we investigated the feasibility of the training protocol in healthy individuals.
 Methods
 Thirteen healthy, physically active and strength training experienced participants (median [interquartile range] age 29 [5] years, nine women) participated in the study. The participants followed the eccentric arm crank training protocol besides their usual daily training routine. The performance tests and training at the KREHA were familiarized one week before the pretests (Pre) took place. The test battery consisted of a bench press (one-repetition maximum [1RM]), a Wingate test (peak and mean power) and a VO2max test (Wmax) on a regular arm crank ergometer, as well as grip strength and an upper arm circumference measurement. The training protocol consisted of 20 progressive training sessions on the KREHA. The training sessions were planned based on individual peak power using a double progression method for duration and intensity, and lasted between 8 to 14 minutes at an intensity of 20-50% of peak power. The sessions were spread over a maximum of 12 weeks, with no more than three sessions per week. Posttests consisted of the same test battery as the pretests and were scheduled 5-11 days (Post1) respectively four weeks (Post2) after the last training.
 Results
 The 1RM (39 [18] to 44 [18] kg), peak power (357 [143] to (360 [154] W) and mean power (263 [108] to 274 [135] W) improved over the training duration (Pre-Post1, p < 0.05). Likewise, the upper arm circumference increased from 28.4 (3.5) to 29.5 (4.0) cm (p < 0.05). 1RM, peak power and mean power remained higher four weeks after the last training (Pre vs. Post2, p < 0.05). Grip strength remained similar at all time points (p = 0.2).
 Conclusions
 Participants were able to improve their aerobic and anaerobic performance between 1-4% as well as the maximum strength of the upper body by 13% through eccentric arm-crank training. In two future studies, the training concept will be investigated in wheelchair athletes and patients with a spinal cord injury.

First Responder Virtual Reality Simulator to train and assess emergency personnel for mass casualty response.

As mass casualty incidents continue to escalate in the United States, we must improve frontline responder performance to increase the odds of victim survival. In this article, we describe the First Responder Virtual Reality Simulator, a high-fidelity, fully immersive, automated, programmable virtual reality (VR) simulation designed to train frontline responders to treat and triage victims of mass casualty incidents. First responder trainees don a wireless VR head-mounted display linked to a compatible desktop computer. Trainees see and hear autonomous, interactive victims who are programmed to simulate individuals with injuries consistent with an explosion in an underground space. Armed with a virtual medical kit, responders are tasked with triaging and treating the victims on the scene. The VR environment can be made more challenging by increasing the environmental chaos, adding patients, or increasing the acuity of patient injuries. The VR platform tracks and records their performance as they navigate the disaster scene. Output from the system provides feedback to participants on their performance. Eventually, we hope that the First Responder system will serve both as an effective replacement for expensive conventional training methods as well as a safe and efficient platform for research on current triage protocols.

Mechanism Models of the Conventional and Advanced Methods of Construction Safety Training. Is the Traditional Method of Safety Training Sufficient?

Cognitive failures at the information acquiring (safety training), comprehension, or application stages led to near-miss or accidents on-site. The previous studies rarely considered the cognitive processes of two different kinds of construction safety training. Cognitive processes are a series of chemical and electrical brain impulses that allow you to perceive your surroundings and acquire knowledge. Additionally, their attention was more inclined toward the worker's behavior during hazard identification on-site while on duty. A study is proposed to fill the knowledge gap by developing the mechanism models of the two safety training approaches. The mechanism models were developed based on cognitive psychology and Bloom's taxonomy and six steps of cognitive learning theory. A worker's safety training is vital in acquiring, storing, retrieving, and utilizing the appropriate information for hazard identification on-site. It is assumed that those trained by advanced techniques may quickly identify and avoid hazards on construction sites because of the fundamental nature of the training, and when they come across threats, they may promptly use their working memory and prevent them, especially for more complex projects. The main benefit of making such a model, from a cognitive point of view, is that it can help us learn more about the mental processes of two different types of construction safety training, and it can also help us come up with specific management suggestions to make up for the approaches' flaws. Future research will concentrate on the organizational aspects and other cognitive failures that could lead to accidents.

ALTERAÇÕES DA FORÇA MUSCULAR EXPLOSIVA NO TREINAMENTO COM SHUTTLECOCK

ABSTRACT Introduction: Shuttlecock is a popular sport in China. Its level of competition depends on the athlete's body shape, strength, technique, tactics, and mentality, among other factors that require the mastery of the shuttlecock with all body parts except the hands. The physical preparation for this sport requires the explosive strength of specific muscles for its total domination. Objective: To explore the changes in explosive muscle strength in shuttlecock players after short-term physical training. Methods: College students in two shuttlecock elective classes were selected as volunteers for the research. The experiment divided the two classes into experimental and control groups. The conventional training method was used in the control group. The experimental group used a combination of a physical and routine training protocol. The groups were tested for fitness before and after the experimental intervention. The data obtained were statistically analyzed and discussed. Results: The experimental group's specific muscle strength and explosive power after physical training were better than the control groups. The scores of the experimental and control groups were significantly different in terms of physical flexibility, the number of kicks, and motor skill assessment (P<0.05). There was no statistical significance in the number of push-ups between the two groups (P>0.05). Conclusion: Physical training significantly affects the physical quality of college students and their number of shots. Physical training is essential to improve shuttlecock practitioners’ muscular strength and explosive power. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance during Motor Training.

As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (~10% and ~18% improvement respectively), reaching ~89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.

Fast Near-Field Beam Training for Extremely Large-Scale Array

In this letter, we study efficient near-field beam training design for the extremely large-scale array (XL-array) communication systems. Compared with the conventional far-field beam training method that searches for the best beam direction only, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead based on the two-dimensional exhaustive search, we propose in this letter a new two-phase beam training method that decomposes the two-dimensional search into two sequential phases. Specifically, in the first phase, the candidate angles of the user are determined by a new method based on the conventional far-field codebook and angle-domain beam sweeping. Then, a customized polar-domain codebook is employed in the second phase to find the best effective distance of the user given the shortlisted candidate angles. Numerical results show that our proposed two-phase beam training method significantly reduces the training overhead of the exhaustive search and yet achieves comparable beamforming performance for data transmission.

A Conceptual Framework for BIM Process Flow to Mitigate the Causes of Fall-Related Accidents at the Design Stage

Safety training is essential in enhancing safe environments, attitudes, and employee safety behaviour. It has been recognized that the construction industry must tackle the inefficiencies of conventional safety training methods. Using innovative training practices for construction workers is one of the most effective ways to improve safety performance. There is a lack of a standard framework and of necessary information for building information modelling (BIM) process flow needed by designers and safety and health officers to aid construction workers’ safety and job hazard identification (JHI) at the design stage. This study aims to create a framework for BIM process flow to minimize the causes of fall-related accidents in the architecture, engineering, and construction (AEC) industry. This framework was developed based on an integrative review approach and other empirical findings. The connection of the different components makes up the framework. This research recommends practical enhancements, innovation, and adjustments to construction employees’ safety training and JHI. One of the suggested framework’s research advantages is that it evolved through expert validation and contains the necessary components to facilitate practical construction safety training and JHI. These components could serve as a starting point for developing guidelines for practical safety training and JHI.

INFLUÊNCIA DO TREINAMENTO DO CORE NA APTIDÃO FÍSICA DE ATLETAS UNIVERSITÁRIOS

ABSTRACT Introduction: Recent research shows the CORE is the central link in the kinetic chain of aerobic gymnastics. The athlete’s synchronized performances depend not only on strength but also on the balance and precision of the movements that start from this area. Although the importance of this muscle group is recognized, there is no consensus about the most effective protocol to increase performance in aerobic gymnastics athletes. Objective: Develop and verify the influence of a protocol for CORE training on the aerobic gymnasts’ balance. Methods: This paper selects high-level aerobic gymnastics athletes and randomly divides them into experimental and control groups. The experiment was conducted over eight weeks. The experimental group adopted systematic core strength training methods, and the control group continued with traditional conventional strength training methods. Volunteers were tested for physical fitness before and after the experiment. Results: Aerobic gymnastics athletes’ physical fitness and athletic abilities were significantly improved after training. Conclusion: The CORE training protocol in aerobic gymnastics athletes increased the execution quality in complex movements. Evidence Level II; Therapeutic Studies - Investigating the result.

The Impact of AR Environment in Enhancing Visualization of Petroleum Refinery Process

Petroleum refinery is an industrial process plant where crude oil is being transformed and refined into most useful products. In the industrial process, it involves a lot of complicated physical and chemical processes that are sequentially applied for various refinery processes. Some employees are facing a difficulty to understand the training module because the tools being used limits the ability to learn and understand the abstract complex process and face the challenges to visualize the content as it is viewed via a two-dimensional (2D) method. Therefore, this paper tries to focus the attention on the practical use of AR in the crude oil refinery process, describing the potential and the effectiveness of this technology to enhance their visualization skill. The study used quasi-experimental research design on the technical employees that were trained on the basic refinery process of crude oil. Two groups of samples consist of an experimental group who were exposed to the AR environment and a control group that was taught using conventional training methods. The result demonstrated there is a significant difference between the scores obtained by the technical employees in the experimental group, thus the application of AR was able to enhance technical employees’ visualization skills as a training approach.