Task Completion Efficiency Research Articles

A Personalized Task Allocation Strategy in Mobile Crowdsensing for Minimizing Total Cost.

Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by sorting the cost of users to complete the tasks, where the cost is measured by the expense of time or distance. In this paper, we argue that the task allocation process is actually a dynamic combinational optimization problem because the previous allocated task will influence the initial state of the user to finish the next task, and the user’s preference will also influence the actual cost. To this end, we propose a personalized task allocation strategy for minimizing total cost, where the cost for a user to finish a task is measured by both the moving distance and the user’s preference for the task, then instead of statically allocating the tasks, the allocation problem is formulated as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP). Furthermore, we transform the multiple-TSP to the single-TSP by proving the equivalency, and two solutions are presented to solve the single-TSP. One is a greedy algorithm, which is proved to have a bound to the optimal solution. The other is a genetic algorithm, which spends more calculation time while achieving a lower total cost. Finally, we have conducted a number of simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The simulation results could match the results of theoretical analysis.

Reasoning With Scene Graphs for Robot Planning Under Partial Observability

Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this letter, we develop an algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scene analysis for robot planning</i> (SARP) that enables robots to reason with visual contextual information toward achieving long-term goals under uncertainty. SARP constructs scene graphs, a factored representation of objects and their relations, using images captured from different positions, and reasons with them to enable context-aware robot planning under partial observability. Experiments have been conducted using multiple 3D environments in simulation, and a dataset collected by a real robot. In comparison to standard robot planning and scene analysis methods, in a target search domain, SARP improves both efficiency and accuracy in task completion.

Online Sequential Task Assignment With Execution Uncertainties for Multiple Robot Manipulators

In order to let multiple robot manipulators cooperatively complete a sequence of tasks in a shared workspace under task execution uncertainty, this letter proposes a multi-robot task allocation framework for constantly assigning tasks to robots, while the interference among concurrent robot motions is account for. An online sequential task assignment method is presented, which decouples the time-extended problem into a sequence of synchronous and asynchronous instantaneous assignment sub-problems. This renders the approach capable of reacting to task execution uncertainties in real-time. A one-step-ahead simulation method is employed to reduce the idle time of robots and improve task completion efficiency. Each instantaneous assignment sub-problem is modeled as an optimal assignment problem with variable utility and solved by a branch-and-bound algorithm, with which multi-robot motion coordination is integrated. Experimental results conducted with three Franka-Emika Panda arms show that these can cooperatively complete all tasks without collision and little waiting time. Simulations with larger multi-robot systems show that the approach scales linearly with the number of robots.

PD58-11 BEYOND EFFICIENCY: SURFACE ELECTROMYOGRAPHY ENABLES FURTHER INSIGHTS INTO THE QUALITY OF SURGICAL MOVEMENT OF EXPERT UROLOGISTS

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment (PD58)1 Sep 2021PD58-11 BEYOND EFFICIENCY: SURFACE ELECTROMYOGRAPHY ENABLES FURTHER INSIGHTS INTO THE QUALITY OF SURGICAL MOVEMENT OF EXPERT UROLOGISTS Perry Xu, Daniel Haik, Pengbo Jiang, Andrew Brevik, Akhil Peta, Zhamshid Okhunov, Shlomi Tapiero, Emmanuel John, Rahul Soangra, Jaime Landman, and Ralph Clayman Perry XuPerry Xu More articles by this author , Daniel HaikDaniel Haik More articles by this author , Pengbo JiangPengbo Jiang More articles by this author , Andrew BrevikAndrew Brevik More articles by this author , Akhil PetaAkhil Peta More articles by this author , Zhamshid OkhunovZhamshid Okhunov More articles by this author , Shlomi TapieroShlomi Tapiero More articles by this author , Emmanuel JohnEmmanuel John More articles by this author , Rahul SoangraRahul Soangra More articles by this author , Jaime LandmanJaime Landman More articles by this author , and Ralph ClaymanRalph Clayman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002092.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The advent of laparoscopic and robotic surgery has required urologists to hone multiple skillsets that involve complex movements. Surface electromyography (EMG) accurately records aspects of complex movements. Our objective was to use EMG to capture the differences of surgical movement between expert urologists and trainees while performing laparoscopic and robotic surgical tasks. METHODS: Based on surgical experience, 26 participants were divided into three groups: novice trainees (no surgical training; n=10), intermediate trainees (resident; n=11), and experts (faculty; n=5). With 12 surface EMG electrodes affixed to the upper extremities, participants performed 2 tasks: laparoscopic peg transfer (LPT) and robotic suturing (RS) (Figure 1). Performance EMG data were computed based on muscular activation and workload. These variables were compared across groups using ANOVA statistical analysis; significance was defined as p<0.05 and differences were confirmed with posthoc Tukey analysis. RESULTS: Novices took longer to complete the LPT and RS tasks (Table 1). There was no significant difference across skill level for both tasks in muscle contraction range, root mean square (variability in linear motion), and average work per second. Experts had higher muscle contraction frequency compared to both novices and intermediate trainees in RS (p<0.01). Intermediate trainees had higher muscle contraction frequency compared to novices in LPT (p=0.03). Experts had lower cumulative workload in RS compared to novices (p=0.04). CONCLUSIONS: Contrary to open surgery where speed is often regarded as a primary indicator of skill, among a range of personnel there was no significant difference in efficiency in task completion. Instead, experts in robotic surgery were distinguished by a higher muscle contraction frequency indicating a more concentrated effort expended on fine muscle control and microadjustments. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1021-e1021 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Perry Xu More articles by this author Daniel Haik More articles by this author Pengbo Jiang More articles by this author Andrew Brevik More articles by this author Akhil Peta More articles by this author Zhamshid Okhunov More articles by this author Shlomi Tapiero More articles by this author Emmanuel John More articles by this author Rahul Soangra More articles by this author Jaime Landman More articles by this author Ralph Clayman More articles by this author Expand All Advertisement Loading ...

Research on Usability of the Toolbar of Teaching Interactive Smart Tablet

The work aims to find out the usability problems in the design of the toolbar of teaching interactive smart tablet, and provide an improvement basis and direction for improving the user experience of the teaching interactive smart tablet. Usability testing and user interview were used to research the toolbar of Seewo teaching interactive smart tablet. Quantitative analysis of task completion time, task success level and task completion efficiency were measured in usability testing. The thesis combines the results of user interviews with the results of usability testing to find out the specific factors that affect the usability of the toolbar of teaching interaction smart tablet. The icon design of the toolbar of teaching interactive smart tablet has a great impact on usability. The hiding of frequently used functions may make it difficult for users to find them. The larger screen makes it difficult to find icons outside the field of view. The usability of interactive smart tablet can be improved by improving icon recognition, optimizing common interactive processes, and setting important functions in the best visual field.

Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation

AbstractThe vision of Industry 4.0 is to materialize the notion of a lot-size of one through enhanced adaptability and resilience of manufacturing and logistics operations to dynamic changes or deviations on the shop floor. This article is motivated by the lack of formal methods for efficient transfer of knowledge across different yet interrelated tasks, with special reference to collaborative robotic operations such as material handling, machine tending, assembly, and inspection. We propose a meta reinforcement learning framework to enhance the adaptability of collaborative robots to new tasks through task modularization and efficient transfer of policies from previously learned task modules. Our experiments on the OpenAI Gym Robotics environments Reach, Push, and Pick-and-Place indicate an average 75% reduction in the number of iterations to achieve a 60% success rate as well as a 50%-80% improvement in task completion efficiency, compared to the deep deterministic policy gradient (DDPG) algorithm as a baseline. The significant improvements achieved in the jumpstart and asymptotic performance of the robot create new opportunities for investigating the current limitations of learning robots in industrial settings, associated with sample inefficiency and specialization on one task through modularization and transfer learning.

Bandwidth optimization of information application system under fine integral method of fuzzy fractional order ordinary differential equations

In order to solve the problems that the network bandwidth of previous information application systems can’t guarantee the quality of big data transmission, resulting in low transmission efficiency and slow data processing, etc., an improved information application system is proposed by optimizing the fine integration method of fuzzy fractional ordinary differential equations and combining with software-defined networking (SDN). Firstly, the fractional-order fuzzy differential equation fine integration method of Pade approximation is derived. Then, based on the error analysis theory, the optimization formula for the adaptive selection of weighted parameter N and expanded item number q is obtained. Combined with SDN, an improved information application system is designed, and the improved algorithm and system are detected by example simulation and performance test. The results show that the numerical accuracy and the computational efficiency of the improved algorithm are higher than that of the improved algorithm. The improved port data merge rate and task completion efficiency under different bandwidth are also significantly higher than the original system. It shows that the information application system proposed in this research can better solve the problems of insufficient bandwidth and low communication speed of traditional systems, and provide a new perspective for bandwidth optimization of big data.

Roaming Through the Castle Tunnels

Smartphone applications (a.k.a., apps) have become indispensable in our everyday life and work. In practice, accomplishing a task on smartphones may require the user to navigate among various apps. Unlike Web pages that are inherently interconnected through hyperlinks, apps are usually isolated building blocks, and the lack of direct links between apps has compromised the efficiency of task completion and user experience. In this article, we present the first in-depth empirical study of page-level access behaviors of smartphone users based on a comprehensive dataset collected through an extensive user study. We propose a model to distinguish informational pages and transitional pages , based on which we can extract page-level inter-app navigation. Surprisingly, the transitional pages account for quite substantial time cost and manual actions when navigating from the current informational page to the desirable informational page. We reveal that developing “ tunnels ” between “isolated” apps under specific usage scenarios has a huge potential to reduce the cost of navigation. Our analysis provides some practical implications on how to improve app-navigation experience from both the operating system’s perspective and the developer’s&lt;?brk?&gt; perspective.

The IMPACT of Agent Transparency on Human Performance

The primary purpose of this article is to determine the impact of a simulated agent's transparency on human performance and related variables, such as response time, workload, and trust calibration. The agent supports participants as they complete a base defense task by managing a team of heterogeneous unmanned vehicles and serves as a decision aid to the human. Three conditions of transparency are explored. In condition 1, the agent displays only the basic information (map of vehicle location and proposed routes). In condition 2, the agent displays the basic information and an explanation of its reasoning. In condition 3, the agent displays the basic information, reasoning, and uncertainties involved in the plans. Results show that participants exhibit better performance and trust calibration in the high-transparency conditions without perceiving a significant increase in workload. However, response time also increased, likely due to the additional processing time needed for conditions with more information. Overall, our findings indicate that the increased agent transparency can improve human-agent decision making and performance, but with a small cost of the efficiency (timeliness) of task completion.

UAV Virtualization for Enabling Heterogeneous and Persistent UAV-as-a-Service

In this paper, we propose an architecture for UAV virtualization with the primary aim of providing virtualized UAV services to multiple users by envisioning the concept of UAV-as-a-Service. In contrast to traditional UAVs, which are resource-constraint in nature and exhibit shorter flight times, our proposed UAV virtualization overcomes the limitations of short flight time of traditional UAVs, in turn allowing them to provide persistent and ubiquitous services. We achieve the virtualization of a UAV through multiple collaborating real-life UAVs performing various tasks in tandem. In this work, we focus on the placement and selection of UAVs to achieve virtualization. We use a social welfare-based approach to select suitable UAVs, from the available ones, and map the UAV to a virtual one. This work enables the provision of different UAV services to multiple end-users, without actual procurement of the UAVs by the end-users. We compare the results for optimal placement, normal maximum energy-based UAV selection, and Atkinson-based selection method. We also compare the virtual model and simple UAV-to-task model to physical UAV usage, task completion ratio, and residual energy of the system. Our proposed model outperforms the traditional model with a task completion efficiency of 94.26%. The residual energy of the system also increases with an increase in the number of tasks.

Многозадачность как одновременное выполнение и как переключение между заданиями: подходы к диагностике медиамногозадачности у детей и подростков

The transformation of the problem of multitasking as an object of experimental cognitive psychology into a General psychological problem of constant combination of several activities, provoked by information technologies and which has become a social criterion for a successful person, requires a comparison of different approaches to definition, modeling and diagnostics. The aim of this study is to compare the features of media multitasking as simultaneous task completion and switching between them in schoolchildren. 57 primary school students, 54 teenagers aged 11—13 and 46 teenagers aged 14—16 solved a series of tasks presented simultaneously in windows on a computer and sequentially in the form of SMS messages to a mobile phone. According to the results, multitasking as simultaneous task completion was observed in only 4.5% of children and adolescents and was not associated with multitasking as switching between tasks. Multitasking as delaying and returning to tasks was typical for every second child and increased with age, but it was also not associated with the speed and efficiency of task completion. By the age of 11—13, children are characterized by attempts to speed up and optimize activities in a multitasking environment through various, often ineffective strategies. Attempts to speed up and optimize tasks are more typical and, according to our data, are formed earlier among boys. The results are discussed in the context of the hypothesis that the development of multitasking in children and adolescents should be considered as the formation of strategies for solving problems in a multitasking environment.

Adaptive information sharing approach for crowd networks based on two stage optimization

Purpose This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them. Design/methodology/approach This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach. Findings The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained. Originality/value This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings.

BackgroundPopulation surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information.ObjectivesThis paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context.MethodsThe framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards.ResultsThe evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use.ConclusionsOverall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context.

Adding body load modifies the vibratory sensation of the foot sole and affects the postural control

BackgroundHeavy backpacks are often used by soldiers and firefighters. Weight carrying could reduce the speed and efficiency in task completion by altering the foot sole sensitivity and postural control.MethodsIn fifteen healthy subjects, we measured the changes in sensitivity to vibrations applied to the foot sole when standing upright or walking after load carrying (30% body weight). The participants were asked to judge different vibration amplitudes applied on the 2nd or 5th metatarsal head and the heel at two frequencies (25 and 150 Hz) to determine the vibration threshold and the global perceptual representation (Ѱ)of the vibration amplitude (Ф) given by the Stevens power function (Ѱ = k × Фn). Any increase in negative k value indicated a reduction in sensitivity to the lowest loads. Pedobarographic measurements, with computation of the center of pressure (COP) and its deviations, were performed during weight carrying.ResultsThe 25-Hz vibration threshold significantly increased after weight carrying when standing upright or walking. After standing with the added loads, the absolute negative k value increased for the 25 Hz frequency. After walking with the added loads, the k coefficient increased for the two vibration frequencies. Weight carrying significantly increased both the CoP surface and CoP lateral deviation.ConclusionsOur data show that weight carrying reduces the sensory pathways from the foot sole and accentuates the center of pressure deviations.

CPS-Based Smart Control Model for Shopfloor Material Handling

Automated guided vehicles (AGVs) have been widely used in manufacturing and supply chain management for material handling. The efficiency of the material handling process has been the bottleneck of the production manufacturing. By applying maturing technologies such as sensing, cloud computing, and wireless communication, the efficiency and the reliability of the material delivery could be enhanced. In this paper, a cyber-physical system-based smart control model for shopfloor material handling is designed. In contrast to the traditional vehicle control methods, AGVs and base stations at intersections can communicate and interact with each other and share the real-time information online. Then, the smart control model, which consists of car-following model, overtaking model, and collision warning and avoidance model, is designed and developed. The presented model is demonstrated by a set of simulations and an experiment, which proved that the overall task completion efficiency and the utilization of the road are improved.

Usability evaluation of menu interfaces for smartwatches

ABSTRACTCurrently, there are various smartwatch products on the market, and the number of users is expected to continue to increase. The functions of smartwatches have also been diversified, and the amount of information displayed on their screens is also increasing. However, as there are many restrictions on the rather small screen size, it is difficult to apply the methods used to provide information on the existing smart devices. Therefore, this study investigated how menu should be provided on the touch screen of smartwatches and derived a more effective menu structure. For our purposes, we conducted two experiments. In the first experiment, we provided 40 items in grid view and list view layout styles and tried to search for a given item by scrolling or paging. Efficiency was higher for the list view layout, in which many items were displayed on the screen and task completion time was shortened. However, the overall satisfaction was higher for the grid view layout, in which fewer items were displayed on the screen. In the second experiment, we derived an efficient menu structure when displaying hierarchical items that could be grouped into upper and lower categories. Likewise, many items on a single screen were excellent in terms of task completion time and efficiency. Providing depth of menu by categorization showed satisfactory results in task completion time, efficiency, and overall satisfaction.

TeMoto: Intuitive Multi-Range Telerobotic System with Natural Gestural and Verbal Instruction Interface

Teleoperated mobile robots, equipped with object manipulation capabilities, provide safe means for executing dangerous tasks in hazardous environments without putting humans at risk. However, mainly due to a communication delay, complex operator interfaces and insufficient Situational Awareness (SA), the task productivity of telerobots remains inferior to human workers. This paper addresses the shortcomings of telerobots by proposing a combined approach of (i) a scalable and intuitive operator interface with gestural and verbal input, (ii) improved Situational Awareness (SA) through sensor fusion according to documented best practices, (iii) integrated virtual fixtures for task simplification and minimizing the operator’s cognitive burden and (iv) integrated semiautonomous behaviors that further reduce cognitive burden and negate the impact of communication delays, execution latency and/or failures. The proposed teleoperation system, TeMoto, is implemented using ROS (Robot Operating System) to ensure hardware agnosticism, extensibility and community access. The operator’s command interface consists of a Leap Motion Controller for hand tracking, Griffin PowerMate USB as turn knob for scaling and a microphone for speech input. TeMoto is evaluated on multiple robots including two mobile manipulator platforms. In addition to standard, task-specific evaluation techniques (completion time, user studies, number of steps, etc.)—which are platform and task dependent and thus difficult to scale—this paper presents additional metrics for evaluating the user interface including task-independent criteria for measuring generalized (i) task completion efficiency and (ii) operator context switching.

A &amp;quot;Photographing and Making Money&amp;quot; Pricing Model Based on Game Theory

In recent years, the Internet has shown a booming situation. According to the survey, many emerging products have flooded into the market on a large scale. One of the self-service apps that make money by taking pictures has quietly emerged in many big cities. Firstly, taking Shenzhen City, Guangdong Province, China as the research object, the price of photographing and making money in different areas of Guangzhou was collected and analyzed. The relationship between location and price was obtained by multivariate linear fitting, and the latitude and longitude of the failed task after clustering was substituted. The three main reasons for the failure of the task in the software are: low pricing or remote location; members and tasks are far away, members have low credibility, and scheduled tasks start too late; the population of the region The flow rate is low and the market potential is insufficient. Secondly, in order to reduce the probability of task failure, a non-cooperative game model is established to improve the pricing law, which increases the task success rate by 11.3% compared with the original, indicating that the model can effectively improve the task success rate. Finally, in order to avoid the concentration of tasks, users are competing for choice, and also improve the efficiency of task completion. Consider combining these tasks together and publishing them. Therefore, the optimized “crowdsourcing” model is established again, and the implementation of the scheme is evaluated by data fitting. Effect. The result suggests that this model not only effectively ensures the authenticity of the survey's data, but also shortens the survey's period, which is conducive to increasing the respective interests between enterprises and users and promoting economic development.

Exposure to Digital and Hands-on Delivery Modes in Engineering Design Education and Their Impact on Task Completion Efficiency

Exposure to Digital and Hands-on Delivery Modes in Engineering Design Education and Their Impact on Task Completion Efficiency

Study on method of laparoscopic training based on eye gaze tracking techniques

In current domestic research on laparoscopic training, researchers usually consider instrument movement path in the hand-eye coordination relationship. However, they ignore the information contained in visual cues by which could guide and control instrument movements. Studies in other areas have shown that trainers can improve their perceptual-motor skills by gaze training. This paper was designed to examine the effectiveness of eye gaze tracking technology in laparoscopic training and to analyze gaze strategy of the subjects in different training methods. The Tobii X1 Light Eye Tracker was used to track the gaze position of subjects when they were performing the two-handed transferring task in box trainer, and to obtain parameters related to gaze strategy including the efficiency of task completion, as well as visual search, visual processing and observation transfer analysis based on Markov chain model. The results showed that the completion time during the last training in gaze training group was decreased by 101.5 s comparing to the first training. Compared with video training group, gaze strategy of gaze training group has a significant change, such as fixation and saccade duration rate was increased by 38%, fixation duration on target area was increased, and saccade amplitude increased by 0.58°, and the probability of the fixation point transferring to equipment decreased by 15%. The results demonstrated that eye gaze tracking technology can be used in laparoscopic training, and can improve the subjects' skills and shorten the learning curve by learning gaze strategies of experts.