HUMAN FACTORS ENGINEERING AND SYSTEMS DESIGN
2.2 Person –Machine Systems
44 HUMAN FACTORS FUNCTION Generally, all systems have the following com-
ponents: (1) elements (personnel, equipment, proce- dures); (2) conversion processes (processes that result in changes in system states); (3) inputs or resources (personnel abilities, technical data); (4) outputs (e.g., number of units produced); (5) an environment (phys- ical and social and organizational); (6) purpose and functions (the starting point in system development);
(7) attributes (e.g., reliability); (8) components and pro- grams; (9) management, agents, and decision makers;
and (10) structure. These components must be consid- ered in the design and evaluation of every system. For example, the nature of the system inputs has a sig- nificant impact on the ability of a system to produce the desired outputs. Inputs that are complex, ambigu- ous, or unanticipated may lead to errors or time delays in information processing, which in turn may lead to inaccurate or inappropriate responses. If there is con- flicting or confusing information on a patient’s chart, a physician might have difficulty diagnosing the illness and prescribing the appropriate course of treatment.
There are various ways in which systems are clas- sified. Systems can be distinguished according to degree of automation, functions and tasks, feedback mechanisms, system class, hierarchical levels, and com- binations of system elements (Meister, 1991). A basic distinction between open- and closed-loop systems is usually made on the basis of the nature of a system’s feedback mechanisms. Closed-loop systems perform a process that requires continuous control and feedback for error correction. Feedback mechanisms exist that provide continuous information regarding the difference between the actual and the desired states of the system.
In contrast, open-loop systems do not use feedback
for continuous control; when activated, no further control is executed. However, feedback can be used to improve future operations of the system (Sanders and McCormick, 1993). The distinction between open- and closed-loop systems is important, as they require different design strategies.
We are also able to describe different classes of systems. For example, we can distinguish at a very general level among educational systems, production systems, maintenance systems and health care systems, transportation systems, communication systems, and military systems. Within each of these systems we can also identify subsystems, such as the social system or the technical system. Complex systems generally contain a number of subsystems. Finally, we are able to distinguish systems according to components or elements. For example, we can distinguish among machine systems, human systems (biological systems), and human–machine systems and more recently human–robot systems and collaborative team or group systems.
Equipment/
technology Task
Person
Age, education Gender Health status Self efficacy Communication skills SES status Ethnicity Knowledge & skills Health literacy Technical experience Beliefs Readiness Cognitive, motor,
perceptual, physical Capabilities
Demands Sensory, perceptual,
cognitive, physical
Policy Organization
Social Physical
Complexity Familiarity Degree of collaboration Duration Timing
Work schedule and location Size
Portability
Maintenance requirements Security
Hardware interface Software interface Instructional support Stage of deployment Degree of system intelligence
Figure 4 Human factors model of person–task–equipment system.
model which integrates social and environmental com- ponents and is more representative of today’s socio- technical systems is presented in Figure 4.
With the emergence of computer and automation technologies, the nature of person–machine systems has changed dramatically. For example, display technology has changed, and information can be presented in a wide variety of formats using multimedia approaches. Con- trol functions have also changed, and humans can even speak commands. In addition, as noted earlier, with the advent of the Internet a vast amount of information on a wide variety of topics is available at an unprecedented rate and communication is taking on new forms with the advent of applications email and instant messag- ing. Perhaps more important, machines have become more intelligent and capable of performing tasks for- merly restricted to humans. Prior to the development of intelligent machines, the model of the human–machine interface was formed around a control relationship in which the machine was under human control. In current human–machine systems (which involve some form of advanced technology), the machine is intelligent and capable of extending the capabilities of the human.
Computer/automation systems can now perform routine, elementary tasks and complex computations, suggest ways to perform tasks, or engage in reasoning or deci- sion making. In these instances, the human–machine interface can no longer be conceptualized in terms of a control relationship where the human controls the machine. A more accurate representation is a partner- ship where the human and the machine are engaged in two-way cognitive interaction. Also, in today’s work- place human–computer interaction tasks often involve networks among groups of individuals.
For example, in aircraft piloting, the introduction of the flight management system (FMS) has dramatically changed the tasks of the pilot. The FMS is capable of providing the pilot with advice on navigation, weather patterns, airport traffic patterns, and other topics and is also capable of detecting and diagnosing abnormalities.
The job of the pilot has become that of a process manager, and in essence the workspace of the pilot has become a desk; there is limited manual control of the flight system (Sheridan, 2002). Further the Next Generation Air Transportation System (NextGen) project is transforming the air transportation system in the United States through the incorporation of modern technologies. This will also have vast implications for pilots and air traffic controllers who will be assuming vast changes in job demands, roles, and responsibilities (http://www.jpdo.gov; Proctor and Vu, 2010). Rapidly advancing technologies such as image-guided navigation systems are being designed to support minimally inva- sive surgical procedures. Initially these systems, which represent a partial automation system for some aspects of a surgeon’s task, were largely used in neurosurgery;
however, they are increasingly being used in other surgi- cal fields such as orthopedics. As discussed by Manzey and colleagues (2009), these tools are helpful for sur- geons and have resulted in performance improvements.
However, there are several human factors issues such as mental workload and training that need to be considered prior to their implementation. Other types of systems such as automotive systems are also incorporating new computer, communication, and control technologies that change the way that operators interact with these systems and raise new design concerns. With respect to automobiles, a number of issues related to driver
46 HUMAN FACTORS FUNCTION safety are emerging: For example, are maps and route
information systems a decision aid or a distraction?
Similar issues are emerging in other domains. For example, flexible manufacturing systems represent some combination of automatic, computer-based, and human control. In these systems the operators largely assume the role of a supervisory controller and must plan and manage the manufacturing operation. Issues regarding function allocation are critical within these systems, as is the provision of adequate cognitive and technical support to the humans. Computers now offer the potential of assisting humans in the performance of cognitive activities, such as decision making, and a question arises as to what level of machine power should be deployed to assist human performance so that the overall performance of the system is maximized.
This question has added complexity, as in most complex systems the problem is not restricted to one operator but to two or more operators who cooperate and have access to different databases. Today’s automated systems are becoming even more complex with more decision elements, multiple controller set points, more rules, and more distributed objective functions and goals. Further, different parts of the system, both human and machine, may attempt to pursue different goals, and these goals may be in conflict. This is commonly referred to as the mixed-initiative problem, in which mixed human initia- tives combine with mixed automation initiatives. Most systems of this type are supervised by teams of people in which the operator is part of a decision-making team of people who together with the automated system control the process (Sheridan, 2002). The mixed-initiative problem presents a particular challenge for system designers and human factors engineers.
Obviously, there are many different types of human–
machine systems, and they vary greatly in size, struc- ture, complexity, and so on. Although the emphasis in this chapter is on work systems where computerization
is an integral system component, we should not restrict our conceptualization of systems to large, complex tech- nological systems in production or process environ- ments. We also need to consider other types of systems, such as a person using an appliance within a living environment, a physician interacting with a heart mon- itor in an intensive care unit, or an older person driving an automobile within a highway environment or using a telemedicine device within a home setting. In all cases, the overall performance of the system will be improved with the application of human factors engineering to system design.
New challenges for system design also arise from the evolution of virtual environments (VEs). Designers of these systems need to consider characteristics unique to VE systems, such as the design of navigational tech- niques, object selection and manipulation mechanisms, and the integration of visual, auditory, and haptic sys- tem outputs. Designers of these types of systems must enhance presence, immersion, and system comfort while minimizing consequences such as motion sickness. VE user interfaces are fundamentally different from tradi- tional user interfaces with unique input–output devices, perspectives, and physiological interactions. As noted virtual human modeling is commonly used in the design of many systems to prevent changes late in the design process and enhance design efficiency.
Thus, in today’s world, person–machine systems, which increasingly involve machine intelligence, can take many forms, depending on the technology involved and the function allocation between human and machine.
Figure 5 presents the extremes of various degrees of automation and the complexity of various task scenarios.
The lower left represents a system in which the human is left to perform completely predictable and, in most cases, “leftover tasks.” In contrast, the upper right rep- resents ideally intelligent automation where automated systems are deployed to maximal efficiency—a state
Task entropy (complexity, unpredictability)
Dignified human work
Ideally intelligent automation
Clockwork Drudgery,
slavery
Degree of automation
Direction of progress Human-supervised automation
Figure 5 Progress of human-supervised automation (Sheridan, 2002).
not attainable in the foreseeable future. The lower right also represents an effective use of full automation, and the upper left represents the most effective deploy- ment of humans—working on undefined and unpre- dictable problems. As discussed by Sheridan (2002), few real situations occur at these extremes; most human- automated systems represent some trade-off of these options, which gradually progress toward the upper right—ideally, intelligent automation. Clearly, specifi- cation of the human–machine relationship is an impor- tant design decision. The relationship must be such that the abilities of both the human and machine components are maximized, as is cooperation among these compo- nents. Too often, technology is viewed as a panacea and implemented without sufficient attention to human and organizational issues.
The impact of the changing nature of person–
machine systems on the system design process and current approaches to system design is discussed in a later section. However, before this topic is addressed, concepts of system and human reliability are introduced because these concepts are important to a discussion of system design and evaluation.