Complex Human-machine Systems Research Articles

Effects of assistance timing on metabolic cost, assistance power, and gait parameters for a hip-type exoskeleton.

There are many important factors in developing an exoskeleton for assisting human locomotion. For example, the weight should be sufficiently light, the assist torque should be high enough to assist joint motion, and the assistance timing should be just right. Understanding how these design parameters affect overall performance of a complex human-machine system is critical for the development of these types of systems. The present study introduces an assistance timing controller that regulates assistance timing such that peak joint velocity and peak assistance power are offset by a reference value for our hip-type exoskeleton. This is followed by measuring the manner in which various assistance timing references affect an important metric for performance, namely metabolic cost. The results indicate that net metabolic cost exhibits a concave up pattern with the most reduction of 21%, when compared to walking without the exoskeleton, at 0% assistance timing reference. The study also examines assistance timing's effect on gait parameters; increase in assistance timing reference increases step length, decreases cadence, and increases walk ratio (i.e. step length/cadence ratio) during treadmill walking.

Guest Editorial Special Issue on Systematic Approaches to Human–Machine Interface: Improving Resilience, Robustness, and Stability

The papers in this special section focus on systematic approaches to human-machine interface applications. The motivation for this special issue is the growing increase of remote mission management, unmanned aircraft systems, NextGen operations in the U.S. and its Single European Sky Air Traffic Management Research counterparts in Europe, and other similarly integrated systems of systems that include complex human–machine systems with high levels of autonomy and team dynamics that are difficult to understand and analyze. The issue explores key research areas that impact the properties of these systems, which rely on varied degrees of human and machine interactions. The special issue is a result of the continued interest in the formal verification of complex human–machine systems.

Human‐Centered Design of Wearable Neuroprostheses and Exoskeletons

Human-centered design of wearable robots involves the development of innovative science and technologies that minimize the mismatch between humans’ and machines’ capabilities, leading to their intuitive integration and confluent interaction. Here, we summarize our human-centered approach to the design of closed-loop brain-machine interfaces (BMI) to powered prostheses and exoskeletons that allow people to act beyond their impaired or diminished physical or sensory-motor capabilities. The goal is to develop multifunctional human-machine interfaces with integrated diagnostic, assistive and therapeutic functions. Moreover, these complex human-machine systems should be effective, reliable, safe and engaging and support the patient in performing intended actions with minimal effort and errors with adequate interaction time. To illustrate our approach, we review an example of a user-in-the-loop, patient-centered, non-invasive BMI system to a powered exoskeleton for persons with paraplegia. We conclude with a summary of challenges to the translation of these complex human-machine systems to the end-user.

Mental workload prediction based on attentional resource allocation and information processing.

Mental workload is an important component in complex human-machine systems. The limited applicability of empirical workload measures produces the need for workload modeling and prediction methods. In the present study, a mental workload prediction model is built on the basis of attentional resource allocation and information processing to ensure pilots' accuracy and speed in understanding large amounts of flight information on the cockpit display interface. Validation with an empirical study of an abnormal attitude recovery task showed that this model's prediction of mental workload highly correlated with experimental results. This mental workload prediction model provides a new tool for optimizing human factors interface design and reducing human errors.

Modelling workload in cognitive and concurrent tasks with time stress using an integrated cognitive architecture

Workload modelling is one of the important issues in human factors modelling and simulation. Focusing on the cognitive rather than the physical aspect of workload, the goal of the present study is to examine a new simulation method for workload modelling in performing complex cognitive activities and concurrent tasks under time stress. In a general-purpose mental architecture called QN-ACTR, overall expected utilisation (OEU) is used as an index of workload. This computational technique possesses construct validity and does not require human subjective rating. We examined and demonstrated QN-ACTR modelling in the simulations of a cognitive judgment single-task and a target-shooting and arithmetic computation dual-task, both with time stress. OEU results from simulation were related to subjective workload measures from human empirical results. The models produced performance results similar to the human data and OEU workload index results capturing the difference between various task demand conditions. This new modelling technique could support the test and evaluation of complex human-machine systems in a wide range of domains.

Программно-целевое планирование и управление созданием сложных технических и человекомашинных систем

Программно-целевое планирование и управление созданием сложных технических и человекомашинных систем

Task partitioning effects in semi-automated human–machine system performance

Twelve professional pilots performed a flight simulation consisting of three component sub-tasks: (i) tracking, (ii) monitoring and (iii) targeting, respectively. The targeting sub-task required (i) target identification, (ii) weapon selection and then (iii) weapon release. Pilots performed in a fully manual condition, a partial automation condition or a fully automated condition. Automated assistance was provided for the targeting sub-task only, while tracking and monitoring sub-tasks were always performed manually. During full automation, the computer located the target, identified it and released the appropriate weapon without any pilot input. During partial automation, the computer located and identified the target while the pilot retained final control over weapon release. Significantly higher levels of tracking error distinguished manual from both automated conditions and also between the two levels of automation. Monitoring response times were also sensitive to the degree of automation engaged, with the partial-automation condition exhibiting faster responses than full automation. Findings support a design principle in which pilots retain control over final weapons release directly on the basis of objective performance outcome. These collective results support the contention that effective and principled task-partitioning should represent a central strategy for the evolution of complex human–machine systems. Practitioner Summary: Advantages of partitioning tasks between human and automated control are contingent upon the overall context of performance and the actual way the partitioning is accomplished. Simple algorithms, for example, automate on every feasible occasion, are poor design heuristics and may even prove actively harmful to overall response capacity. Transitioning humans from active controllers to passive monitors can be a problematic design choice, especially when that individual is socially deemed to retain overall responsibility for ultimate system effects in the real world.

Human–automation interaction strategies and models for life science applications

AbstractThe objective of this research was to identify current and future approaches to the design of automated systems for life science processes, including humans in control loops, in applications such as high‐throughput compound screening and high‐performance analytical chemistry. The identified approaches were classified according to existing theories of human‐centered automation, which provided a basis for projecting human performance implications. We provide background on the life sciences domain and established theories of types and levels of automation (LOAs) in complex human–machine systems. We describe specific forms of robotic and automated technologies used in life science applications and the general design of high‐throughput screening (HTS) and analytical systems to accommodate particular process configurations. Example classifications of life science automation (LSA) schemes are presented by referring to a taxonomy of LOAs from the literature. We project the implications of these classified forms of automation on human performance on the basis of prior empirical research in other domains. A mathematical model for predicting the cost of LSA from an operator perspective is also defined to support hypotheses for future study. Finally, we identify the need for additional empirical research on human performance consequences of LSA and remedial measures, including enhanced supervisory control interface design. © 2009 Wiley Periodicals, Inc.

Biomedical and Health Informatics in Translational Medicine

To discuss translational medicine advances challenging biomedical and health informatics. Reviewing material presented at the Heidelberg 35th Anniversary Workshop, summarizing results from the 1st AMIA Summit on Translational Bioinformatics and discussing the opportunities, difficulties, and ethical dilemmas confronting researchers, practitioners, and healthcare managers in transitional bioinformatics. The first results in translational medicine are appearing in the biomedical literature. All rely on bioinformatics methods for analysis. Translational medicine introduces new problems of interpretation and application to healthcare. Applying results to complex human-machine systems raises ethical issues, which are augmented in healthcare informatics. Bridging biological, medical, and informatics knowledge requires new epistemological approaches.

On system validity, quasi-experiments, and safety: a critique of NUREG/CR-6393

Based on the methods of Cook and Campbell (1979), a quasi-experimental validation model for complex human–machine systems has been recommended for the final evaluation of nuclear power plant control rooms (O'Hara et al., 1997). This model-based approach to validating systems and assuring safety is critiqued in terms of experimental theory and engineering practice. It is concluded that the model may be inappropriate for industry as a general model of engineering validation.

Human Information Flow and Communication Pattern in NASA Mission Control System

This study is a preliminary effort to explore human information flow and communication pattern in an operation-critical and time-constrained collaboration environment such as NASA Mission Control System. Understanding information flow and communication pattern is a key step for improving the reliability and accuracy of a complex Human-Machine system. It also can be applied to design a better communication system for future NASA Mission Control Center operations. The information flow and communication pattern analysis based on NASA mission control training simulations indicates that there is no significant utilization difference among communication channels, however, the communication channel's burst rate is significantly affected by phase, mission and channel type.

Using Cognitive Work Analysis to Develop a Capability for the Evaluation of Future Systems

Evaluating complex human-machine systems is a multidimensional problem that requires the analyst to specify the characteristics of the system to be evaluated and to specify the method of evaluation to be used in determining its effectiveness. Whereas current approaches using task analysis techniques are used widely for evolutionary systems, other approaches, such as those from within the Cognitive Work Analysis (CWA) framework, may be better suited to revolutionary systems. This paper presents ongoing work directed at developing a human-in-the-loop simulation capability for revolutionary, or novel, systems that aims to evaluate the impact of a subsystem's performance (e.g. radar detection range) on the overall system's purpose (e.g. the safe transportation of troops). Two aspects of the development of this capability are presented: 1) the use of Work Domain Analysis and Control Task Analysis to represent systems for the purpose of providing measures of performance for human-in-the-loop experiments performed in support of system evaluation and 2) criteria for judging the utility of a CWA approach for system evaluation compared to other more traditional techniques.

Soft computing framework for intelligent human-machine system design, simulation and optimization

This paper proposes a novel soft-computing framework for human–machine system design and simulation based on the hybrid intelligent system techniques. The complex human–machine system is described by human and machine parameters within a comprehensive model. Based on this model, procedures and algorithms for human–machine system design, economical/ergonomic evaluation, and optimization are discussed in an integrated CAD and soft-computing framework. With a combination of individual neural and fuzzy techniques, the neuro-fuzzy hybrid soft-computing scheme implements a fuzzy if-then rules block for human–machine system design, evaluation and optimization by a trainable neural fuzzy network architecture. For training and test purposes, assembly tasks are simulated and carried out on a self-built multi-adjustable laboratory workstation with a flexible motion measurement and analysis system. The trained neural fuzzy network system is able to predict the operator's postures and joint angles of motion associated with a range of workstation configurations. It can also be used for design/layout and adjustment of human assembly workstations. The developed system provides a unified, intelligent computational framework for human–machine system design and simulation. Case studies for workstation system design and simulation are provided to illustrate and validate the developed system.

Outline of an Organisational Adaptation Support Information System (OASIS)

In this position paper, a vision of an inter-disciplinary and multi-cultural group of European researchers and practitioners is outlined, showing how the social, cultural and technological imperatives in the deployment of complex human-machine systems can be integrated. This vision will be realised in the form of a wide regionally networked organisational adaptation support information and communication system called OASIS. The OASIS network will assist in knowledge and technology transfer between the European Union and the European post-socialist countries. The strategic research foundations of OASIS, the project itself and the basic architecture of OASIS are briefly presented, emphasising its human - system interfaces, working processes and protocols.

Frank H. George Research Award Winning Paper

The cybernetic approach differs significantly from the conventional reductionist methods of natural and biological sciences. Norbert Wiener established the theory of cybernetics as a science of control and communication process in living beings (human and animals) and machines. Dutta Majumder in his Norbert Wiener Award winning paper extended the approach to include integrated complex human machine systems and functions with general systems theory as a unitary science laying the mathematical foundation for unifying observing systems, observed systems and the act of observing as indicated in von Foerster’s concept of second‐order cybernetics. Both from the point of view of ontology and that of epistemology the cybernetic approach now enables computer technology to incorporate artificial intelligence (AI) and expert system (ES) for knowledge based instrumentation for diagnostics and therapy planning. Presents the results of a project for development of a knowledge based framework for combining different modalities of medical image processing such as CT, MR(T1), MR(T2), SPECT, PET, USG etc. whichever is relevant for particular pathological investigation for diagnostics and therapeutic planning. Experiments were conducted with (a) Alzheimer’s patient data and (b) detection and grading of malignancy with oncological data for the cancer screening system.

Developing Effective Human Factors Design Guidelines: A Case Study

In the development of many complex human-machine systems, human factors information in the form of design guidelines has the potential to be a rich source of input to the design process. However, past research has firmly established that human factors reference information is viewed by system designers as having little value and is generally ignored. This has resulted in considerable uncertainty and concern regarding the nature and utility of human factors handbooks and guideline materials. In this article, we present a conceptual framework for developing design guidelines and a case study of the development process and evaluation of a set of 75 human factors design guidelines for Advanced Traveler Information Systems (ATIS). The design guidelines developed through this process were perceived by the ATIS design community as being clear, relevant, and easy to use. They were also seen as representing an improvement over existing human factors reference materials. The guideline development approach summarize...

Identifying mental models of complex human–machine systems

The notion of a `mental model' is widespread but ill defined. Its meaning can be clarified by noting both the cognitive processes of the worker and the context of the task. A mental model is a mapping of the properties of the task to its representation in the mind of the worker. Traditionally, such models have been identified by the use of protocol analysis, but a method has been developed which finds the mapping through its effects on the coupling between human and machine. This method uses Conant's theory of system decomposition based on high-order Shannon information theory. An example of its use is given. Relevance to industry Operators make use of mental models of complex systems in process control and manufacturing. A method to identify such models is relevant to productivity, and to fault detection and management, since it gives insight into the way in which operators become coupled, by training and habit, to the dynamics of the processes they control.

The Midas Human Performance Model

A unique software tool for conducting human factors analyses of complex human-machine systems has been developed at NASA Ames Research Center. Called the Man-Machine Integration Design and Analysis System (MIDAS), this simulation system contains models of human performance that can be used to evaluate candidate procedures, controls, and displays prior to more expensive and time consuming hardware simulators and human subject experiments. While this tool has been successfully applied to research issues in several domains, particularly in aeronautics, a desire to expand its functionality and its ease of use has led to the construction of a new object-oriented system. This new version of MIDAS contains a substantially modified human performance model, one that is aimed at being more consistent with empirical data on human behavior and more natural for designers to apply to the analyses of complex new designs. This paper offers a summary of this new human performance model, together with justifications for some of its main components, and indicates plans for its subsequent verification and validation.

When Soft Controls get Slippery: User Interfaces and Human Error

Many types of products and systems that have traditionally featured physical control devices are now being designed with soft controls - input formats appearing on computer-based display devices and operated by a variety of input devices. A review of complex human-machine systems found that soft controls are particularly prone to some types of errors and may affect overall system performance and safety. This paper discusses the application of design approaches for reducing the likelihood of these errors and for enhancing usability, user satisfaction, and system performance and safety.

Attention allocation within the abstraction hierarchy

Previous research has shown that Rasmussen's abstraction hierarchy, which consists of both physical and functional system models, provides a useful basis for interface design for complex human–machine systems. However, very few studies have quantitatively analysed how people allocate their attention across levels of abstraction. This experiment investigated the relationship between attention allocation strategies and performance on a thermal-hydraulic process simulation. Subjects controlled the process during both normal and fault situations for about an hour per weekday for approximately one month. All subjects used a multi-level interface consisting of four separate windows, each representing a level of the abstraction hierarchy. Subjects who made more frequent use of functional levels of information exhibited more accurate system control under normal conditions, and more accurate diagnosis performance under fault trials. Moreover, subjects who made efficient use of functional information exhibited faster fault compensation times. In contrast, subjects who made infrequent or inefficient use of functional information exhibited poorer performance on both normal and fault trials. These results provide some initial, specific evidence of the advantages of an abstraction hierarchy interface over more traditional interfaces that emphasize physical rather than functional information.