Identifying the task variables that influence perceived object assembly complexity

There is a general lack of understanding as to what issues affect assembly task performance when using diagrammatic instructions because few of the task variables contributing to assembly complexity have been identified. Using a task analysis of a range of self-assembly products, seven task variables hypothesized to predict assembly complexity were identified and studied in the instruction comprehension phase of assembly. Experiment 1 took nine real world assembly instructions and described each in terms of the seven task variables. Seventy-two participants gave a subjective rating of assembly difficulty for each assembly, showing a clear relationship between the task variables and perceived assembly difficulty. As real world assemblies provide little control a second experiment used an orthogonal design to systematically vary the values of each of the assembly task variables in 16 abstract assemblies. Forty-two participants compared the 16 assembly instructions to a final assembly. There was a clear relationship between the task variables and the time taken to view the instructions. Further, it was found that it is possible to predict the complexity of assembly tasks based upon the levels of the task variables identified. The task variables identified are a significant step towards identifying the factors that influence assembly complexity, together with providing progress towards a tool for predicting assembly complexity.     

Identifying the task variables that predict object assembly difficulty

OBJECTIVE: We investigated the physical attributes of an object that influence the difficulty of its assembly. Identifying attributes that contribute to assembly difficulty will provide a method for predicting assembly complexity. BACKGROUND: Despite object assembly being a widespread task, there has been insufficient research into information processing and cognition during assembly. The lack of research means that the variables that affect the performance of procedural assembly tasks with illustration-only instructions are unknown. METHOD: In Experiment 1, seven physical characteristics (task variables) of assembly objects were systematically varied in a balanced fractional factorial and orthogonal design to create 16 abstract assemblies, which were assembled by 12 participants (6 men and 6 women aged 18-56). A second experiment (20 participants, 8 men and 12 women aged 18 to 52) involved scaled-down models of 8 real-world assemblies. RESULTS: A clear relationship between the task variables and assembly difficulty was found in both studies, and the regression model from the first experiment was able to predict the assembly difficulty timings in Experiment 2. CONCLUSION: The proposed task variables are associated with assembly difficulty, and the regression analysis has shown four of the task variables to be significant predictors of difficulty. APPLICATION: Applications of this research include the use of the regression model as a tool to evaluate the difficulty of assemblies or assembly steps defined by instructions. The task variables can also be used to produce guidelines to ensure that assemblies or assembly steps are manageable.     

Evaluation of pictorial assembly instructions for young children

OBJECTIVE: We examined the usability of common formats of pictorial toy assembly instructions for 6- and 9-year-old children. BACKGROUND: Interlocking building toys and models are increasingly prevalent and important for developing spatial abilities and fine motor skills among children. Little is known about how effectively the intended child users can interpret and carry out the instructions. METHOD: Twenty-four children used five sets of manufacturer-supplied pictorial toy assembly instructions. We evaluated the impact of toy instruction set, age, gender, and previous experience on usability problems, assembly speed and accuracy, instruction gaze time, and subjective ratings. RESULTS: The children had difficulty with all but the simplest instructions and assemblies. As predicted, older participants assembled more quickly, with fewer errors and fewer instruction looks. However, the 6-year-old girls assembled the fewest parts correctly, and the 9-year-old girls reported having the least fun. Instruction look time and frequency revealed differences in instruction complexity and were correlated with subjective ratings of fun. Thirty-two usability problems were observed, and 10 are described in detail. CONCLUSION: Product age recommendations may not reflect developmentally appropriate instructions. Small design changes should contribute to improved instruction usability among young children. For instance, designers should avoid complex graphic syntax, depict colors accurately, select clear angles of view, and support natural tendencies to assemble top to bottom. APPLICATION: This research provides pictorial assembly instruction guidelines to inform instruction designers and describes performance and look-time benchmarks for future usability studies.     

Impact of training standard complexity on inspection performance

Research in the area of visual inspection has shown that various factors influence inspection performance. Task factors have been identified as one of the primary classes of factors influencing the complexity of inspection tasks. If inspection task complexity is to be reduced it is essential to understand the influence of various task factors and prescribe interventions based on the impact of these factors. Moreover, historical work in this area has shown that the greater the difficulty of a vigilance task, the more engaged operators may become. Therefore, this research studies the influence of the following task factors: number of defect types, defect standard complexity, defect probability, and defect distribution on both the visual search and decision‐making components of a contact lens inspection task. This study was conducted using a computer simulation of a real world contact lens inspection task using 28 student subjects. Performance was measured on both the visual search and decision‐making components of the task. The results revealed a negative influence of defect standard complexity and a positive influence of defect probability on both the visual search and decision‐making components of the inspection task. © 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 109–132, 2006.     

Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review

This paper presents a literature review on the different aspects of task allocation and assignment problems in human–robot collaboration (HRC) tasks in industrial assembly environments. In future advanced industrial environments, robots and humans are expected to share the same workspace and collaborate to efficiently achieve shared goals. Difficulty- and complexity-aware HRC assembly is necessary for human-centric manufacturing, which is a goal of Industry 5.0. Therefore, the objective of this study is to clarify the definitions of difficulty and complexity used to encourage effective collaboration between humans and robots to leverage the adaptability of humans and the autonomy of robots. To achieve this goal, a systematic review of the following relevant databases for computer science was performed: IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and ASME Digital Collection. The results extracted from 74 peer-reviewed research articles published until July 2022 were summarized and categorized into four taxonomies for 145 difficulty and complexity definitions from the perspectives of (1) definition-use objectives, (2) evaluation objectives, (3) evaluation factors, and (4) evaluation variables. Next, existing definitions were primarily classified according to the following two criteria to identify potential future studies on the formulation of new definitions for human-centric manufacturing: (1) agent specificity and (2) common aspects in manual and robotic assemblies.     

Augmented Reality-Based Manual Assembly Support With Visual Features for Different Degrees of Difficulty

This research investigates different visual features for augmented reality (AR)–based assembly instructions. Since the beginning of AR research, one of its most popular application areas has been manual assembly assistance. A typical AR assembly application indicates the necessary manual assembly operations by generating visual representations of parts that are spatially registered with, and superimposed on, a video representation of the physical product to be assembled. Research in this area indicates the advantages of this type of assembly instruction presentation. This research investigates different types of visual features for different assembly operations. The hypothesis is that in order to gain an advantage from AR, the visual features used to explain a particular assembly operation must correspond to its relative difficulty level. The final goal is to associate different types of visual features to different levels of task complexity. A user study has been conducted in order to compare different visual features at different operation complexity levels. The results support the hypothesis.     

The relationship between post‐task and continuous‐vicarious ratings of difficulty

A study was conducted that used continuous‐vicarious ratings (CVRs) of difficulty to investigate how people make judgments of task difficulty. Twelve people performed five editing tasks using a text editor. After each task, the participants rated the overall difficulty they experienced in performing the task. They then viewed a videotape of their performance. While the participants were viewing the tape, they were asked to rate continuously the difficulty they felt they had experienced at that moment when performing the task. From this curve of difficulty over time, seven variables were obtained as possible candidates in predicting their overall task‐difficulty ratings. It was found that the contrast between the maximum or peak of the continuous rating and the mean difficulty level best accounted for the post‐task ratings of difficulty, followed by the time it took to perform a task. In other words, the more the peak difficulty stood out from the background average‐difficulty level combined with the more time spent performing a task, the higher the participants rated their overall task difficulty. A three‐variable (maximum difficulty, mean difficulty, and time) power‐function model was developed that best predicted the post‐task difficulty ratings. The terms in this model were statistically significant and accounted for 63% of the variability in the task‐difficulty ratings. These results imply that human factors practitioners may be more effective focusing on and improving what people judge to be the most difficult aspects of an interface. Indeed, the results suggest that concentrating on other areas to improve an interface may actually cause an increase in perceived difficulty simply because the problem causing the peak difficulty has become more salient.     

Training in virtual environments: transfer to real world tasks and equivalence to real task training

Virtual environments (VEs) are extensively used in training but there have been few rigorous scientific investigations of whether and how skills learned in a VE are transferred to the real world. This research aimed to measure and evaluate what is transferring from training a simple sensorimotor task in a VE to real world performance. In experiment 1, real world performances after virtual training, real training and no training were compared. Virtual and real training resulted in equivalent levels of post-training performance, both of which significantly exceeded task performance without training. Experiments 2 and 3 investigated whether virtual and real trained real world performances differed in their susceptibility to cognitive and motor interfering tasks (experiment 2) and in terms of spare attentional capacity to respond to stimuli and instructions which were not directly related to the task (experiment 3). The only significant difference found was that real task performance after training in a VE was less affected by concurrently performed interference tasks than was real task performance after training on the real task. This finding is discussed in terms of the cognitive load characteristics of virtual training. Virtual training therefore resulted in equivalent or even better real world performance than real training in this simple sensorimotor task, but this finding may not apply to other training tasks. Future research should be directed towards establishing a comprehensive knowledge of what is being transferred to real world performance in other tasks currently being trained in VEs and investigating the equivalence of virtual and real trained performances in these situations.     

Training in virtual environments: transfer to real world tasks and equivalence to real task training

Virtual environments (VEs) are extensively used in training but there have been few rigorous scientific investigations of whether and how skills learned in a VE are transferred to the real world. This research aimed to measure and evaluate what is transferring from training a simple sensorimotor task in a VE to real world performance. In experiment 1, real world performances after virtual training, real training and no training were compared. Virtual and real training resulted in equivalent levels of post-training performance, both of which significantly exceeded task performance without training. Experiments 2 and 3 investigated whether virtual and real trained real world performances differed in their susceptibility to cognitive and motor interfering tasks (experiment 2) and in terms of spare attentional capacity to respond to stimuli and instructions which were not directly related to the task (experiment 3). The only significant difference found was that real task performance after training in a VE was less affected by concurrently performed interference tasks than was real task performance after training on the real task. This finding is discussed in terms of the cognitive load characteristics of virtual training. Virtual training therefore resulted in equivalent or even better real world performance than real training in this simple sensorimotor task, but this finding may not apply to other training tasks. Future research should be directed towards establishing a comprehensive knowledge of what is being transferred to real world performance in other tasks currently being trained in VEs and investigating the equivalence of virtual and real trained performances in these situations.     

Selection of key visual cues in real and virtual environments for assembly tasks

Previous research identified that learning assembly tasks in Virtual Environments (VEs) is more difficult than in Real Environments (REs). This work's objective is to identify the key visual areas for both environments when performing an assembly task for ten consecutive cycles, when following visual instructions and when having visual distractors. Using an eye-tracker, we identified the key visual areas required for an assembly task in both environments. Results indicate that practice allowed participants to reduce their assembly time in both environments. They also indicate that two areas, Assembly Area and Blocks, concentrated a higher proportion of eye-fixations; 59.98% for REs and 81.48% for VEs, with a statistically significant observation difference between environments (t-test value = −14.23, with p-value <0.00001 and Cohen's d = 6.36). We conclude that participants considered the same key visual areas for both environments and that VE interaction has a significant role in observation behavior.     

Development of a tablet test for the precision assembly efficiency assessment

This study explored if an individual's aiming ability can be used to predict his or her overall performance in a precision assembly task. A tablet aiming test was developed. Ten participants in the Pilot group was asked to do both the tablet aiming test and the simulated assembly task to determine the number of trials that were required for participants to get familiar with each operation. Thirty participants in the Test group performed the both tasks at certain times based on the number of trials determined in the pilot test. Pearson correlation analysis shows that, among the seven performance measures extracted from the tablet aiming test, the Number of Miss (r = 0.39, p = 0.03), the Hit Rate (r = −0.38, p = 0.04), and the Miss Rate (r = 0.39, p = 0.03) each had a significant correlation with the time required to complete the simulated assembly task (p < 0.05). The findings provide insights into the potential development for predicting the efficiency of a precision assembly task.     

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.     

Ergonomics and quality in paced assembly lines

The aim of this industrial study was to measure relationships between workstation ergonomics and product quality. Quality on two paced manual assembly lines for disposable cameras was measured by the number of defects per week at each workstation. Two ergonomic variables, the time required for the task and the postural deficiencies, were together able to predict over 50% of the quality variance on each assembly line. This study shows the direct effect of ergonomics variables on quality results. © 2001 John Wiley & Sons, Inc.     

基于ARM Cortex-M4的构件化汇编框架的研究

规范易用的汇编框架与样例是微处理器深层次应用开发的基础,但汇编编程复杂,并且目前针对32位ARM Cortex-M4的汇编程序资料和样例程序非常匮乏,学习难度大,开发工作困难重重。在深入分析ARM Cortex-M4汇编指令系统和寻址方式的基础上,以NXP半导体公司发布的Kinetis K(KK)系列MCU为蓝本,根据软件工程思想和构件设计理论,提出层次架构的工程框架建模思想,并构建基于ARM Cortex-M4内核的构件化汇编框架,据此制作GPlO驱动构件。指示灯闪烁样例工程的设计及测试结果表明了汇编框架规范、实用、易用,能降低嵌入式汇编语言的学习难度。框架能为业界基于ARM Cortex-M4的汇编开发提供参考。     

A concept for an intelligent and fault-tolerant robot system

A concept for the intelligent control of subsystems of a flexible assembly cell is presented. Unknown or uncertain data about the real world may lead towards failure during an assembly task. Therefore, a fault tolerant system must be capable of reacting immediately to error situations. Thus, the major topic of this paper is the dynamic handling of unforeseen situations during realtime activities. This will be achieved by combining sensor guided actions with an advanced autonomous supervision system. Experimental results will be derived from the mobile two-arm robot system KAMRO of the Institute for Real-Time Computer Control Systems and Robotics, University of Karlsruhe, Federal Republic of Germany.     

The visual impact of augmented reality during an assembly task

Many studies have shown the benefits of augmented reality (AR) to improve manufacturing and control processes in industry. However, the presentation of AR content through optical see-through head-mounted displays may induce unnatural viewing conditions, which consequences on the user’s visual system have not been investigated yet. This study aimed at assessing whether using AR instructions to guide a manual task, i.e., conditions where the user is forced to repeatedly look at and accommodate in different planes to extract information from both real and virtual environments, could potentially impact the visual system of operators. A before/after design study was carried out, asking 26 participants to perform Lego assemblies for 30 min with either paper or AR instructions. The effects of using AR compared to paper instructions were evaluated both on binocular vision, with classical optometric measurements, and on visual fatigue, with the Virtual Reality Symptoms Questionnaire. No clinically significant differences in optometric measurements (far/near visual acuity, stereoacuity, phoria, convergence, fusional amplitude, accommodation amplitude, accommodative convergence) have been found between AR and paper instructions, and only negligible fatigue symptoms have been seen specifically for AR. Results from both objective and subjective measurements suggest that there is no impact of AR on the oculomotor system and that, in this specific case of use, AR can be safely used for production operators.     

The effect of testing location on usability testing performance, participant stress levels, and subjective testing experience

The effect of testing location on usability test elements such as stress levels and user experience is not clear. A comparison between traditional lab testing and synchronous remote testing was conducted. The present study investigated two groups of users in remote and traditional settings. Within each group participants completed two tasks, a simple task and a complex task. The dependent measures were task time taken, number of critical incidents reported, and user-reported anxiety score. Task times differed significantly between the physical location condition; this difference was not meaningful for real world application, and likely introduced by overhead regarding synchronous remote testing methods. Critical incident reporting counts did not differ in any condition. No significant differences were found in user reported stress levels. Subjective assessments of the study and interface also did not differ significantly. Study findings suggest a similar user testing experience exists for remote and traditional laboratory usability testing.     

Representation of assemblies for automatic tolerance chain generation

A scheme of representing assemblies and an algorithm for the tolerance chain generation are developed so that tolerance chains of assemblies can be generated automatically to accommodate tolerance analysis at the assembly level. In the hierarchical data structure representing an assembly the connectivity information is carried by the instances of components and subassemblies, and the mating relations between each pair of mating entities are described by mating links, mating paths, mating conditions, and mating features. Mating graphs are derived from the mating links before they are searched for the generation of tolerance chains. The scheme has been implemented in a prototype interactive package that allows the user to model assemblies directly without detailed object modeling. Several examples of various complexity have been tested with success.     

Strategies in performing a manual assembly task

A study of naïve participants' strategies in manual assembly is reported. Four groups of ten participants assembled hacksaws under varying conditions of the number of aids given to participants. The aids were provision of an assembly jig, instructions on use of the jig and components set up in an ergonomically-designed workplace. Assembly took place under four conditions: (a) with no jig or instructions (b) with a jig and with no instructions on its use (c) with a jig and with instructions and (d) with a jig, instructions and also an ergonomically designed workplace in which all parts were placed within the zone of convenient reach. Video recording was used to measure performance times and strategies in assembling the hacksaw. The 40 participants used a total of 32 models of liaison sequence and 152 patterns of assembly sequence. Participants used many different strategies in their early learning and generally settled down to a single pattern after the early trials. The common strategy of participants was to pick and assemble the longer and heavier components, followed by small and lighter components.Relevance to industryParticipants showed many different patterns of assembly, even for a simple product. The data indicate a need for the industrial engineer to determine the ergonomically best layout of components for assembly and demonstrate the best assembly sequence to the operator.     

Exploring Relationships Between Eye Tracking and Traditional Usability Testing Data

This study explored the relationships between eye tracking and traditional usability testing data in the context of analyzing the usability of Algebra Nation?, an online system for learning mathematics used by hundreds of thousands of students. Thirty-five undergraduate students (20 females) completed seven usability tasks in the Algebra Nation? online learning environment. The participants were asked to log in, select an instructor for the instructional video, post a question on the collaborative wall, search for an explanation of a mathematics concept on the wall, find information relating to Karma Points (an incentive for engagement and learning), and watch two instructional videos of varied content difficulty. Participants’ eye movements (fixations and saccades) were simultaneously recorded by an eye tracker. Usability testing software was used to capture all participants’ interactions with the system, task completion time, and task difficulty ratings. Upon finishing the usability tasks, participants completed the System Usability Scale. Important relationships were identified between the eye movement metrics and traditional usability testing metrics such as task difficulty rating and completion time. Eye tracking data were investigated quantitatively using aggregated fixation maps, and qualitative examination was performed on video replay of participants’ fixation behavior. Augmenting the traditional usability testing methods, eye movement analysis provided additional insights regarding revisions to the interface elements associated with these usability tasks.