Variation In Human Performance Research Articles

Everyday Performance of Ship Officer: Qualitative Analysis based on Functional Resonance Analysis Method

Uncertainty in a complex socio-technical system, such as ship, is given. Yet, surprisingly, most of the ship operations were done without any significant problem. In this case, the ship officer as the operator plays an important role in maintaining ship safety. Human performance is unpredictable and varies on the condition. However, variation in human performance is more likely to produce acceptable outcomes than adverse outcomes. Therefore, this study aimed to determine how human variability performance in specific officers onboard contributes to successful ship operation. Evaluation of officer variability performance for establishing safety in everyday ship operation has been done using Functional Resonance Analysis Method (FRAM) in this study. FRAM is Safety-II based tool that provides concepts and models for safety analysis that use terms called function to describe system activities. An essential feature of FRAM is the mean that is necessary to explain the activity of a system in which the functions are mutually dependent. System activities are modeled in terms of how the system works to ensure that it performs systematically. Key functions of officer activities onboard are generated through observation in training ship Fukae-maru owned by Kobe University. As a result, FRAM could define how officer variability performance contributes to system propagation and create a safe ship operation.

Reinforcement learning for facilitating human-robot-interaction in manufacturing

For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Several decades of development in the fields of robotics and automation have resulted in human–robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. Note to Practitioners —This article presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated, and used within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behavior to reduce the impact of human performance variation and improve human–machine interaction (HMI). This article also presents a simulation-based case study to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine-learning approach to identify the complex influence of several identified human factors on human performance. This knowledge can be used to adjust the robotic behavior to match the predicted performance of multiple different operators over different scenarios. This adaptability reduces performance disparity by reducing idle times and enabling leaner production through workpiece-in-progress reduction. Future work will focus on expanding the intelligent capabilities of the proposed system to deal with uncertainty and improve decision-making ability.

업무수행도 추정을 위한 직무스트레스 평가표의 비교 분석

A man is a component of a large system how complex it may be so that human performance which can influence on the system safety should be included in system safety assessment. Meanwhile, human performance can vary over time due to lots of factors. Among them, stress is an indirect factor that may cause human error which can result in industrial accidents. To assess stress level of human workers, not a few assessment tools have been developed. However, it can be questionable to utilize them for human performance anticipation because they were mainly developed with the viewpoint of mental health, though stress assessment can be also required in the safety aspect. Therefore, this study aimed to survey the possibility of their application with safety purpose. About 10 kinds of work stress tools were collected and analyzed with reference to assessment items, assessment and analysis methods, and follow-up measures. The results showed that most tools focused their weights on Demands, Supports, and Relationships, in sequence. However, they, except only one tool developed by the Japanese researchers, merely advised to set up counterplans in PDCA cycle or risk management activities. In consequence, application of stress assessment tools mainly developed for mental health seemed impractical for safety purpose with respect to human performance anticipation so that it was concluded that development of a new assessment tools aimed to human performance variation and accident prevention would be inevitable.

Using Multi-agent Systems to Pursue Autonomy with Automated Components

Humans have used tools to transform raw resources into valued outputs ever since society harnessed fire. The type of tool, amount of effort and form of energy required depends on the output or object being created. As tools evolved into machines, they enhanced operator productivity. Hence, industry continues to invest heavily in machines to assist people to do more with less physical control and/or interaction. This involves automating functions previously completed manually. Taylorism and the Hawthorn experiments all contributed to optimising industrial outputs and value engineers continue to promote a mecha- nized workforce in order to minimise business variations in human performance and their behaviour. Researchers have also pursued this goal using Computational Intelligence (CI) techniques. This process of transforming cognitive functionality into machine actionable form has encompassed many careers. Machine Intelligence (MI) is becoming more aspirational, with Artificial Intelligence (AI) enabling the achievement of numerous goals. More recently, Multi-Agent Systems (MASs) have been employed to provide a flexible framework for research and development. These frameworks facilitate the development of component interoperability, with coordination and cooperation techniques needed to solve real-world problems. However problems typically manifest in complex, dynamic and often hostile environments. Based on the effort to seek or facilitate human-like decision making within machines, it is clear that further research is required. This paper discusses one possible avenue. It involves future research, aimed at achieving a cognitive sub-system for use on-board platforms. The framework is introduced by describing the human-machine relationship, followed by the theoretic background into cognitive architectures and a conceptual mechanism that could be used to implement a virtual mind. One which could be used to improve automation, achieve greater independence and enable more autonomous behaviour within control systems.

Variation in human performance in the hypoxic mountain environment

Ascent to altitude is associated with a fall in barometric pressure, and with it a decline in the partial pressure of atmospheric (and thus alveolar) oxygen. As a result, a variety of adaptive physiological processes are engaged to mitigate the fall in tissue convective oxygen delivery which might otherwise occur. The magnitude and nature of such changes is also modified with time, a process known as acclimatization. However, other phenomena are at work; the ability to perform physical work at altitude falls in a manner which is not wholly related to changes in arterial oxygen content. Indeed, alterations in local skeletal muscle blood flow and metabolism may play an axial role. Thus, for those who are not native to high altitude, the ability to compete at altitude is likely to be impaired. The magnitude of such impairment in performance, however, differs greatly between individuals, and it seems that genetic variation underpins much of this difference. The identification of the relevant genetic elements is in its infancy in humans, but ongoing work is likely to help us gain an increasing understanding of how humans adapt to altitude and to develop mitigating interventions.

Human performance variation analysis: A process for human performance problem solving

Problem-solving ability is a much sought-after trait in executives, especially if it includes the ability to solve human performance problems. This paper proposes a systematic root cause analysis process that effectively and consistently uncovers the root causes of human performance problems and controls the causes in a way that prevents the problems from recurring. Applying action research the study brings into being a Human Performance Variation Analysis (HPVA) process, which consists of three phases: (1) performance variation assessment, (2) performance variation analysis, and (3) performance variation resolution. The HPVA provides much-needed capability in solving human performance problems in organisations.

Rhythmic Oscillations in Quantitative EEG Measured During a Continuous Performance Task

The objective of the present investigation was to determine if cyclic variations in human performance recorded during a 30 min continuous performance task would parallel cyclic variations in right-hemisphere beta-wave activity. A fast fourier transformation was performed on the quantitative electroencephalogram (qEEG) and the performance record of each participant (N = 62), producing an individual periodogram for each outcome measure. An average periodogram was then produced for both qEEG and performance by combining (averaging) the amplitudes associated with each periodicity in the 62 original periodograms. Periodicities ranging from 1.00 to 2.00 min and from 4.70 to 5.70 min with amplitudes greater than would be expected due to chance were retained (Smith et al. 2003). The results of the present investigation validate the existence of cyclic variations in human performance that have been identified previously (Smith et al. 2003) and extend those findings by implicating right-hemisphere mediated arousal in the process (Arruda et al. 1996, 1999, 2007). Significant cyclic variations in left-hemisphere beta-wave activity were not observed. Taken together, the findings of the present investigation support a model of sustained attention that predicts cyclic changes in human performance that are the result of cyclic changes in right-hemisphere arousal.

Human target detection performance using synthetic-aperture radar imagery in a quasi-operational context

This paper describes a study that measured the performance of human image analysts in the surveillance context, using images from synthetic-aperture radar, in terms of the analyst's receiver operating characteristic (ROC) and an ancillary analysis of variance (ANOVA). The experiment was designed to correspond as closely as possible to an operational one for analysts using similar imagery. In particular, the effects of target contrast and background clutter on human analysts' target detection performance were quantified. Our ROC and ANOVA studies show that the clutter and contrast metrics used account for most of the variation in human performance as measured by probability of detection.

Improving the design process for factories: Modeling human performance variation

The process of manufacturing system design frequently includes modeling, and usually, this means applying a technique such as discrete event simulation (DES). However, the computer tools currently available to apply this technique enable only a superficial representation of the people that operate within the systems. This is a serious limitation because the performance of people remains central to the competitiveness of many manufacturing enterprises. Therefore, this paper explores the use of probability density functions to represent the variation of worker activity times within DES models.

Methodological Issues in Studies of Rhythms in Human Performance

In this review various sources of measurement error are considered in the context of investigating rhythms in human performance. The reproducibility of performance in any exercise task is an important factor if rhythmic variations are to be detectable. Available physical performance tests range from simple efforts lasting only a couple of seconds to sustained endurance exercise. When measuring muscle strength, the options include static or dynamic actions, slow or fast movements, voluntary or electrically stimulated contractions and maximal force or maximal power output. For studies of circadian rhythms the researcher has to choose between using nychthemeral or controlled conditions, and the number of times a day to be used for observations, and to decide how to control for loss of sleep, diet and prior activity. The need to recover energy between tests has led research groups to employ diurnal rhythm models in preference to cosinor analysis of circadian designs. The use of male subjects has also been favoured due to difficulties of controlling for menstrual cycle phase. Nevertheless recent attention has been given to research models for investigating the effects of rhythmic variations in female steroid hormones and on interactions between circadian and circamensal rhythms. There are real challenges in exploring seasonal variations in human performance and in examining how the body adjusts after desynchronisation as occurs during nocturnal shift-work and travelling across multiple time zones. The methods adopted must accommodate flexibility between laboratory-based and field-studies depending on the context of the research questions being pursued.

MAXIMUM KNEE TORQUE AND ACCELERATION COMPLETELY DISCRIMINATE GENDER AT HIGH SPEEDS

Human performance variation by gender has been reported to be a function of neuromuscular activation patterns resulting in a male bias. This bias is most evident during maximum intensity work involving strength and speed and results in a neuromotoric response of males activating a greater percentage of their available motor neurons. PURPOSE To discriminate gender based on max torque and acceleration ROM measurements derived from an isokinetic knee extension test at two separate speeds. METHODS Twenty subjects (10 male, 10 female, age 22.3(1.9) yrs) volunteered for this study. Subjects performed three maximal concentric knee extension repetitions at speeds of 60 and 360 degrees per second (d/s) on a Biodex system 3 isokinetic dynamometer. The acceleration (ACC) ROM phase and the max torque produced during the load range phase were separated from each repetition at each speed. Discriminate analysis was used to predict gender from ACC at 360 d/s and torque at 60 d/s. RESULTS Males demonstrated significantly (p<0.01) greater torque at 60 d/s (166.8(18.1) vs. 101.9(11.6) ft/lbs) and less ACC ROM at 360 d/s (20.1(2.0) vs. 25.9(2.2) degs) when compared to females. Analysis showed that combined maximum torque and acceleration ROM correctly predicted 10 of 10 females (100 percent) and 10 of 10 males (100 percent). The overall correct prediction outcome for both genders was 20 of 20 subjects (100 percent). It is evident from the prediction outcome that maximum torque and acceleration ROM performance between genders accounts for any and all variance during discriminate analysis. CONCLUSION These results demonstrate that maximum torque and kinematics may be valuable when attempting to discriminate performance based on gender. Future research should attempt to isolate other intrinsic variables that may explain the neuromotoric discrepancy between genders.

Parametric analysis of the Blackwell-McCready data

An assessment of human visual performance for detecting low contrast targets is essential in many army target acquisition applications. We examine the comprehensive Blackwell-McCready (BM) contrast threshold data set that was acquired under carefully controlled laboratory conditions. We also examine the applicability of this methodology for all levels of apparent contrast. A hyperbolic numerical curve fit is made on the measured minimal target/background contrast threshold data as a function of target area and ambient background luminance for a wide range of presentation times. An extensive parametric analysis shows that the largest variations in human performance occur during the time intervals before dawn and after dusk. The ABC law generalizes Ricco's and Blackwell's laws and gives some particularly simple relationships between the relative target/background contrast, target area and ambient luminance, which are useful for military and commercial applications.

Circadian Variation in Human Performance Evaluated By the Walter Reed Performance

As part of a two clock-time (0830 versus 2030) evaluation of administration-time dependent effects of dexedrine (5 mg) and triazolam (0.25 mg) on human cognitive performance, placebo (control) studies were conducted on 12 diurnally active (0700-2300) male adults (23-38 yrs) using a double-blind, randomized crossover design. Testing was conducted hourly during a series of sleepless 13-hr spans commencing in the morning or evening, using the Walter Reed computer controlled and scored multi-task cognitive performance assessment battery. For the placebo condition, Single and Group Cosinor analyses documented circadian rhythms in performance for most tasks (reaction time, logical reasoning, serial add/subtract and spatial orientation) both for individuals and the group. Overall, performance was worse overnight, when sleepiness was greatest, and best between 1830 and 2030. It was most variable around 0600-0700. The day-night variation in performance over all cognitive tests amounted to 21% of the 24-hr mean.

Quantitative Assessment of Neuromuscular Deficit in ALS

Several criteria must be met in developing a test battery forr ALS. First, the test items should be designed to answer the specific questions being asked. If questions involve the amount and rate of deterioration, then the test items should generate interval data and show evidence that they reflect change in the disease itself. Second, the reliability of the test items should be carefully determined. Are the variances due to testing errors such as inconsistent positioning or changes in verbal instructions; or do they reflect human performance variation? Third, the test items must be sensitive to small changes, and one must be able to test the very strong as well as the severely debilitated patient. The TQNE was designed to measure degree of disease progression in ALS. The major portion of the protocol consists of isometric force measurement using an electronic strain gauge. Other quantitative segments include tests of pulmonary function, speech rates, and timed activities. Using z-score transformations, we have summarized these test items into five megascores: pulmonary function, bulbar function, timed hand activities, arm strength, and leg strength. Megascores have been used to characterize the natural history of the disease and to judge therapeutic efficacy. Quantitative methods to measure deficit in neuromuscular disease lag far behind other technologic advances in medical research. Future advances in the measurement of neuromuscular deficit depend on the commitment of researchers to invest the time and resources necessary to develop more sensitive and reliable testing techniques.