GOMS Model Research Articles

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

A model for predicting physical health of college students based on semantic web and deep learning under cloud edge collaborative architecture is proposed to address the issue of most physical health prediction models being unable to fully describe the characteristics of sports performance changes and having large prediction errors. Firstly, the authors design a measurement data analysis system based on cloud edge collaboration architecture to improve data analysis efficiency. Then, they preprocess the data on the edge side, such as missing samples, and extract data features using an equal dimensional dynamic GOM model. Finally, they deploy the RBFNN-SSA model in the cloud center, input the characteristics of each indicator into the model for predictive analysis, and obtain the physical health status of college students. Based on the physical health test data of Hohai University from 2018 to 2021, an experimental analysis was conducted. The results showed that all three intervention measures had significant effects on maintaining and improving the physical health level of college students.

Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach.

Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user. In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model. All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method. The majority of the task execution times comprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation, the number of mental operators should be reduced.

GOMS 모델을 이용한 온라인 쇼핑 융합 프로세스 분석 및 최적화 연구 - 한중 주요 쇼핑 사이트를 중심으로 -

With the continuous development of e-commerce, various online shopping platforms have emerged as the times require. How to allow users to accurately complete operational tasks with the least amount of time and steps is a long-term consideration for interaction designers. This research begins with the theoretical analysis of the optimization of shopping websites by GOMS model.BR The purpose of this study is to find a design scheme to optimize the shopping website through the application analysis of the GOMS model on the shopping website, so as to improve the shopping efficiency and enhance the shopping experience. Therefore, this study takes the GOMS model as the theoretical basis, takes the main shopping websites in China and South Korea as the research object, models the online shopping operation process of different platforms, and predicts and quantifies the operation time of the obtained model. Design the operation process model of the shopping website, so as to carry out performance experiments and questionnaire interviews.BR The research results and content are as follows. First, the theoretical operation time obtained by the GOMS model is basically consistent with the actual operation time, which proves that the GOMS model can accurately predict the operation time of the shopping operation process. Second, through the analysis of the GOMS model, the optimal combination scheme of the shopping process was found, and it was verified through the questionnaire survey. Third, the time predicted by the GOMS model for all shopping operation process combinations is greater than the actual operation time. Fourth, by analyzing the GOMS model of 10 tasks, it is found that in the tasks with less actual operation time, the number of steps to point to the operation P is less, and the number of steps to press the key K is more. Therefore, in the development and design of the shopping webpage, More keystroke steps can be set to reduce unnecessary selection links.BR Based on the GOMS model, this study quantifies the running time of different online shopping platforms, which can optimize the availability of the shopping platform system and improve user satisfaction. At the same time, although this research cannot completely solve the complex problems of shopping websites, it provides ideas for the optimization of shopping websites. It is expected that the research on GOMS model-related topics can be extended to more fields such as mobile Internet shopping.

Application of Computer Technology in VR Digital Media

In order to make up for the defects of traditional digital media system design, continuously strengthen the user’s sense of experience, and further improve the digital media system design through diversified digital media forms, this paper proposes an application method of computer technology in VR digital media. Through the introduction and research of human-computer interaction interface, the usability of human-computer interaction interface evaluation and testing is analyzed, and combined with GOMS model to optimize and improve the model, a new optimized GOMS layered quantitative model is proposed. The experimental results show that when the system is running, the trend of video cache rate in WiFi and 4G environments is basically the same. The playback start rate is fast and then tends to be stable. During the playback process, the video frame rate is stable at about 60 FPS, which meets the needs of human eyes to watch without Caton. After the playback starts, the GPU occupancy rate remains about 20% and stable. Computer technology can realize the effective combination of multiscreen interaction and digital media in VR digital media, creating a better prospect interactive experience for the future development of digital media mobile terminal.

TINJAUAN MODEL GOMS APLIKASI MEGA DALAM INTERAKSI MANUSIA DAN KOMPUTER

Human Computer Interaction (Human Computer Interaction) is a discipline that teaches and studies communication or interaction between users and systems. In human computer interaction, there are several models, one of which is the GOMS model. The GOMS model itself is an original approach to an evaluation-based model in the field of computer user interfaces. in this study, we conducted a review of an application called Mega. Mega application is a cloud storage application and file hosting service offered by Mega company. Our review aims to see and find out what the GoMS model is and see what impact it has on applications that use it. From this research, we know that the GOMS model provides convenience and effectiveness in the application

Fatigue life prediction of wire rope based on grey particle filter method under small sample condition

The fatigue life prediction of wire ropes has two main characteristics: a large test sample size and uncertain factors. In this paper, based on the small number of wire rope fatigue life data, the grey particle filter method has been used to realize the fatigue life prediction of wire rope under different load conditions. First, the GOM(1,1) model is constructed and the reliability life data of wire rope is predicted under small sample size. Then, P-S-N curve of the dangerous part is determined by combining the equivalent alternating stress of the dangerous part of the wire rope during the fatigue test. Subsequently, the particle filter method is used to modify P-S-N curve. Finally, the fatigue life prediction model of wire rope is obtained based on fatigue damage accumulation, which realized the fatigue life prediction under different load conditions, and the results were compared with that from the test. The results show that the proposed method is effective and has high accuracy in wire rope fatigue life prediction under single, combined loading conditions and small sample size.

The use of mobile-assisted virtual reality in fear of darkness therapy

Fear of darkness is a common psychological problem that may extent to a specific phobia if it is not treated well. Several intervention techniques related to fear and phobia using actual exposure therapy have been studied for decades, however, there were some constraints emerged when the therapist provides a real environment to overcome the patient's reaction to his/her specific fear. Virtual reality (VR) technology is an innovative tool providing a more immersive, secure, personal, and controlled virtual environment. Therefore, we developed a novel framework for treating the fear of darkness named Mobile-assisted Virtual Reality (MAVR). The purpose of this study was to evaluate the use of MAVR to treat fear of darkness based on usability, time consumption and its ability to decrease fear. We used the GOMS model as an interaction guidance between human and computer which aimed to facilitate the process of re-learning in mindset change and individual’s behavioral toward situation of darkness and night. Therefore, a comprehensive evaluation was conducted to measure the efficacy of the MAVR. We developed the usability assessment checklist to assess the feasibility and acceptability of the MAVR, and fear of darkness thermometer to measure the degree of fear. The Wilcoxon Signed Rank Test showed that the fear of darkness was significantly decreased after participants received the MAVR therapy (z=-3.550, p-value<0.001). We found that the MAVR was very useful, easy to be used and acceptable for participants. In conclusion, this study highlights the efficacy of Mobile-assisted Virtual Reality in treating specific fear, and it seems that Virtual Reality technology has a promising benefit to be implemented for other fear or specific phobia and also used in other psychological treatment.

Updated Goals, Operators, Methods, and Selection Rules (GOMS) with Touch Screen Operations for Quantitative Analysis of User Interfaces

When developing applications, User Interfaces (UI) are considered as an important and integral component of any application. With badly designed UI, users are less likely to use the application, leading to low adoption rates and is not desirable in any application development setting. The process of developing good intuitive and stream-lined UI for users is complex, that requires many processes and experts from many fields to contribute. When evaluating potential UI designs, there are many attributes and features that could be examined either in a qualitative and/or quantitative standpoint. The Updated Goals, Operators, Methods, and Selection Rules (GOMS) model is an approach that has been used in the area. With the Keystroke Level (KLM) extension, it is possible to quantitatively estimate the time requirement or efficiency of UI for completing different tasks with minimal effort, and has been adopted in many GUI improvement projects. Due to the usefulness, extensions to the GOMS model had been proposed over the years including extensions to account for motion control interfaces. Though the GOMS model has been useful for quantitatively evaluating UI design, the main input device of modern smartphones and tablets are touch screens, which are different in nature when compared to traditional computer inputs. The differences leads to the point that GOMS model with KLM is ill-suited for touch screen based applications. This research paper addresses the issue by proposing extensions to the GOMS model to account for UI that are based on touch screen input devices.

Using GOMS and NASA-TLX to Evaluate Human–Computer Interaction Process in Interactive Segmentation

ABSTRACTHCI plays an important role in interactive medical image segmentation. The Goals, Operators, Methods, and Selection rules (GOMS) model and the National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire are different methods that are often used to evaluate the HCI process. In this article, we aim at improving the HCI process of interactive segmentation using both the GOMS model and the NASA-TLX questionnaire to: 1) identify the relations between these two methods and 2) propose HCI design suggestions based on the synthesis of the evaluation results using both methods. For this, we conducted an experiment where three physicians used two interactive segmentation approaches to segment different types of organs at risk for radiotherapy planning. Using the GOMS model, we identified 16 operators and 10 methods. Further analysis discovered strong relations between the use of GOMS operators and the results of the NASA-TLX questionnaire. Finally, HCI design issues were identified, and suggestions were proposed based on the evaluation results and the identified relations.

A Unit Touch Gesture Model of Performance Time Prediction for Mobile Devices

Objective: The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background: When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured ‘operators’ to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method: In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with ‘out of range’, ‘registration’, ‘continuation’ and ‘termination’ of gesture. Results: As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants’ execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion: In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application: The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.

Modifying Geometric-Optical Bidirectional Reflectance Model for Direct Inversion of Forest Canopy Leaf Area Index

Forest canopy leaf area index (LAI) inversion based on remote sensing data is an important method to obtain LAI. Currently, the most widely-used model to achieve forest canopy structure parameters is the Li-Strahler geometric-optical bidirectional reflectance model, by considering the effect of crown shape and mutual shadowing, which is referred to as the GOMS model. However, it is difficult to retrieve LAI through the GOMS model directly because LAI is not a fundamental parameter of the model. In this study, a gap probability model was used to obtain the relationship between the canopy structure parameter nR2 and LAI. Thus, LAI was introduced into the GOMS model as an independent variable by replacing nR2 The modified GOMS (MGOMS) model was validated by application to Dayekou in the Heihe River Basin of China. The LAI retrieved using the MGOMS model with optical multi-angle remote sensing data, high spatial resolution images and field-measured data was in good agreement with the field-measured LAI, with an R-square (R2) of 0.64, and an RMSE of 0.67. The results demonstrate that the MGOMS model obtained by replacing the canopy structure parameter nR2 of the GOMS model with LAI can be used to invert LAI directly and precisely.

Using Cognitive Workload Analysis to Predict and Mitigate Workload for Training Simulation

Constructive simulation-based training does not always go according to plan. Instructors observe scenarios and direct adaptive responses to unexpected events. Simulation operators typically use a graphical interface to monitor the scenario and generate specific interventions that implement the instructor's intent. These activities require the operators’ attention and cognitive resources, particularly for complex directions and scenarios with multiple entities to monitor. We are developing automated tools to reduce operator workload, thereby increasing the cost effectiveness of training. We use cognitive workload analysis to identify situations in which operators would be overloaded, and to identify the factors that cause the overloading. By identifying workload “hot spots,” we can target more effective automated support strategies.GOMS (Goals, Operators, Methods, Selection) is a theoretical framework for workload analysis that has been applied to many modeling and automation applications. We use GOMS to create a detailed characterization of a set of typical monitoring and intervention tasks for fixed-wing air combat training. This enabled us to synthesize predictive models for combinations of these tasks. The models indicate where operators are likely to have difficulty implementing instructor's intent, as well as where operators are unlikely to be able to complete their tasks correctly. We then used these predictive models to guide the development of tools for reducing operator workload, as well as experimental scenarios for evaluating the effectiveness of those tools.This paper discusses the approach to GOMS modeling, as well as the details of how we used the predictive GOMS models to identify cognitive workload limitations. Using illustrative examples representative of fixed-wing air combat training, we describe composition of workload models, construction of combined predictive models, and use of models for development and testing.

Development of a two-step touch method for website navigation on smartphones

The touch method for hyperlink selection in smartphones can often create usability problems because a hyperlink is universally smaller than a finger contact area as well as visually occluded by a finger while pressing. In this study, we developed a two-step touch method (called Press and Flick method) and comprehensively examined its effectiveness using the goals, operators, methods, and selection rules (GOMS) model and user testing. The two-step touch method consisted of finger press and flick motions; a target hyperlink was selected by a finger press motion, and a finger flick method was subsequently conducted for error correction if the initial interaction (press) failed. We compared the two-step touch method with the current touch method through the GOMS model and user testing. As a result, the two-step touch method was significantly superior to the current touch method in terms of error rate and subjective satisfaction score; however, its superiority in terms of number of interactions and touch time was vulnerably affected by error rate. The two-step touch method developed in this study can improve the usability and user experience of website navigation using smartphones.

Grey New Information GOM(1,1) Model Based on Opposite-Direction Accumulated Generating and its Application

As the monotonic decreasing data the traditional grey model is characteristic with low accuracy, using the opposite-direction accumulated generating operation, making full use of the new information, the formula of the parameters was induced and the grey new information GOM(1,1) model was established based on opposite-direction accumulated generating operation. It provides a new way for the grey model. The examples show the model is practical and reliable.

Extracting Flick Operator for Predicting Performance by GOMS Model in Small Touch Screen

Objective: The purpose of this study is to extract GOMS manual operator, except for an experiment with participants. Background: The GOMS model has advantage of rapid modeling which is suitable for the environment of technology development which has a short life cycle products with a fast pace. The GOMS model was originally designed for desktop environment so that it is not adequate for implementing into the latest HCI environment such as small touch screen device. Therefore, this research proposed GOMS manual operator extraction methodology which is excluded experimental method. And flick Gesture was selected to explain application of proposed methodology to extract new operator. Method: Divide into start to final step of hand gesture needed to extract as an operator through gesture task analysis. Then apply the original GOMS operator to each similar step of gesture and modify the operator for implementation stage based on existing Fitts' law research. Steps that are required to move are modified based on the Fitts' law developed in touch screen device. Finally, new operator can be derived from using these stages and a validation experiment, performed to verify the validity of new operator and methodology by comparing human performance. Results: The average movement times of the participants' performance and the operator which is extracted in case study are not different significantly. Also the average of movement times of each type of view study is not different significantly. Conclusion: In conclusion, the result of the proposed methodology for extracting new operator is similar to the result of the experiment with their participants. Furthermore the GOMS model included the operator by the proposed methodology in this research could be applied successfully to predict the user's performance. Application: Using this methodology could be applied to develop new finger gesture in the touch screen. Also this proposed methodology could be applied to evaluate the usability of certain system rapidly including the new finger gesture performance.

Grey New Information Unbiased GOM(1,1) Model Based on Opposite-Direction Accumulated Generating and its Application

Monotonically decreasing sequence data for the use of traditional modeling methods to establish the grey model accuracy is not high, grey GM (1,1) model, there is a deviation, the model does not meet the conditions for coordination, generated using the definition of reverse accumulation, full use of system information and unbiased grey GM (1,1) model parameters of the model derived formula, based on reverse incremental build new interest generated by an unbiased model grey GOM (1,1) model for the grey model provides a new method. Data processing examples show that the model's practicality and reliability.

Grey Unbiased GOM(1,1) Model Based on Inverse Accumulated Generating Operation and its Application

The accuracy of the traditional grey model is not high for the monotonic decreasing data and the model can not satisfy the compatibility conditions which make the error be not avoided. The paper adopts inverse accumulated generating operation and unbiased GM(1,1) model to induces the parameters in the model and establishes the unbiased grey unbiased GOM(1,1) model based on inverse accumulated generating operation. The model has high accuracy and has good value in the theory and practice. The examples show it is practical and reliable.

Collecting health-related data on the smart phone: mental models, cost of collection, and perceived benefit of feedback

We describe a mobile health application that collects data relevant to the treatment of insomnia and other sleep-related problems. The application is based on the principles from neuroergonomics, which emphasizes assessment of the brain's alertness system in everyday, naturalistic environments, and ubiquitous computing. Application benefits include the ability to incorporate both embedded data collection and retrospective manual data input--thus providing the user with a rewarding data access process. The retrospective data input feature was evaluated by comparing an older version of the retrospective editing interface with a newly developed one. The time course of user interactions was precisely measured by exporting time stamps of user interactions using the Google App Engine. We also developed models that closely fit the time course of user interactions using the Goals, Operators, Methods, and Selection rules (GOMS) modeling method. The user data and GOMS models demonstrated that the retrospective sleep tracking feature of the new interface was faster to use but that the retrospective habit tracking feature was slower. Survey results indicated that participants enjoyed using the newly developed interface more than the old interface for the assessment of both sleep and habits. These findings indicate that a mobile application should be designed not only to reduce the amount of time it takes a user to input data, but also to conform to the user's mental models of its behavior.

Communicative NGOMSL: Development of an Evaluation Method for a Text-Based Communication System

Goals, Operators, Methods, and Selection rules (GOMS) is an analytic method and a model evaluation method for the knowledge necessary for a user to perform tasks in a system, and Natural GOMS Language (NGOMSL), a version of GOMS, is an attempt to define a language so that the GOMS model can be precisely constructed in a structured way. This study proposes Communicative NGOMSL, which is an extension of NGOMSL intended to accommodate modeling of text-based communication among two or more individuals. Besides the properties of GOMS techniques in evaluation, Communicative NGOMSL additionally considers common ground and communication time: Common ground refers to the mutual knowledge, beliefs, and assumptions of participants in communication, and communication time refers to the time that an individual waits for another to respond. The analysis of a Communicative NGOMSL model provides some information for a common task goal in a text-based communication system: (a) the relationship between the grounding process and Communicative NGOMSL statements, (b) the ratio of communication time to execution time for each individual, and (c) the balance among individuals in terms of learning time and mental workload. A demonstration of Communicative NGOMSL is provided through a sample case—a virtual text-based chat tool for exchanging addresses between two individuals. Future work needed to address the limitations of this study and further develop Communicative NGOMSL is discussed.

Grey New Information GOM(1,1) Model and its Application Based on Opposite-Direction Accumulated Generating and Background Value Optimization

The accuracy of the traditional grey model is not high for the monotonic decreasing data. The paper uses the opposite-direction accumulated method and makes full use of new information combined with optimization of the background values. It induces the formula of the parameters in the model and establishes the grey new information GOM(1,1) model in which it is based on the opposite-direction accumulated operation and optimization of the background values. It is a new method of the grey model. The examples show it is with higher accuracy than that of non-new information model.