Capabilities Of Human Operators Research Articles

A User Study for the Evaluation of Adaptive Interaction Systems for Inclusive Industrial Workplaces

In recent years, production systems have become highly sophisticated and complex. As a result, while on the one hand, the least-skilled labor has been partially displaced by machines, high-skilled labor is more required to supervise and control advanced automation systems. In many cases, the complexity of machines implies an increased complexity of human–machine interfaces (HMIs), which are the main point of contact between the operator and the machine. To enable effective use of HMIs and to enable their usage by workers with different knowledge and capabilities, novel design approaches have been proposed. In particular, in this article, we consider the approach developed in the framework of the European research project INCLUSIVE, which aimed at designing industrial HMIs that adapt to the skills and capabilities of human operators. As a case study, we consider an adaptive interaction system for the woodworking industry and present an extensive evaluation carried out in real production environment with shopfloor workers. The effectiveness of the INCLUSIVE approach has been assessed with subjective and objective measurements and compared to that of interaction systems customarily used in industry. Results have shown that users appreciated the INCLUSIVE system and largely preferred it over the customary system. Moreover, with regard to objective performance-related measurements, they performed better when using the INCLUSIVE system since they received tailored guidance during the considered working tasks. Note to Practitioners— This article was motivated by the fact that advanced automation systems are often highly complex for human operators. To address this problem, we focus on the importance of designing the automation system around users and discuss an approach for adaptive automation, called INCLUSIVE. Its main feature is that it adapts the human–machine interface (HMI) according to operator’s skills, capabilities, and current mental fatigue. In this article, we provide an extensive evaluation of the INCLUSIVE system, considering a company producing woodworking machines as a use case. Assessment was carried out in the company shopfloor, considering real workers, and the INCLUSIVE system was compared with the customary HMI running on the company machines. The results of our study suggest that adapting the interaction to operator’s needs allows better working performance while letting workers more satisfied with the use of the system.

The effect of multi‐tasking training on performance, situation awareness, and workload in simulated air traffic control

AbstractIncreasingly higher demands are being made on the capacity‐limited cognitive capabilities of human operators as they strive to maintain situation awareness (i.e., understanding “what is going on”) and performance in complex tasks. In the current study we asked whether: (a) training administered via a mobile phone‐based app could improve multitasking and (b) improved multitasking in the app would generalize to improved performance and situation awareness in a simulated air traffic control task (ATC). Participants completed the ATC task before and after multiple sessions of app‐based multitasking training or control training. Multitasking on the app improved across training sessions. However, this did not lead to improved performance or situation awareness, or workload reduction, relative to control training on the ATC task. These outcomes indicate that app‐based multitasking training based on repetition of a single training task will not necessarily yield generalizable benefits to human performance in other complex dynamic tasks.

Application of Cognitive Computing for Smart Crowd Management

A crowd can be defined as a large gathering of people at a particular place showing different types of attitudes and behaviors. Monitoring and tracking these wide varieties of people is tedious in a real environment. The crowded scenarios have a high tendency to change into an abnormal condition due to sudden external pressures such as gunshots/fire or internal stress such as overcrowding, where things get often uncontrollable and the consequences are disastrous. Moreover, the use of a large number of monitoring cameras and the limited capability of human operators to analyze the video contents result in an urge to developing smart crowd monitoring systems with humanlike capabilities. This article discusses the human cognition capability and its application for smart crowd management. Cognitive computing facilitates complex task automation, real-time decision-making, predictive analytics, and processing of voluminous structured and unstructured data.

Development of sense of self-location based on somatosensory feedback from finger tips for extra robotic thumb control

Recently, wearable extra robotic limbs that aim to enhance the functionality and capability of human operators as extra arms or fingers have become an active research subject among robotics researchers. Improving the operability of the extra robotic limbs is required for the human operators, an approach for which is to induce robotic embodiment. In this paper, we focus on the update of sense of self-location which is the key aspect of embodiment and contributes to the body representation update, and we elucidate dominant factors which induce the embodiment of an extra robotic thumb (ERT). The experiments are conducted to compare the performance of the reaching task of the ERT under three separately given conditions, of which two are somatosensory feedbacks: (1) tactile and position feedback from the fingertips and (2) tactile and position feedback from the human face, and one is a non-somatosensory feedback: (3) auditory feedback. As a result, we confirmed that the somatosensory feedback from finger tips has a large contribution for the update of sense of self-location.

Automatic Detection of Events and Tracking of Moving Objects in Video Sequences

Digital video is being used widely in a variety of applications such as surveillance and security. Big amount of video in surveillance and security requires systems capable to process video automatically to detect events and track moving objects to alleviate the load on humans and enable preventive actions when events are detected [4]. our paper focuses to develop an intelligent visual surveillance system to replace the traditional passive video surveillance that is proving ineffective as the number of cameras exceeds the capability of human operators to monitor them, and it is able to track objects within a maximum solid angle speed which is measured at about 0.3 to 0.2 radian per second, further it also depends on the complexity of the system and the processor speed as well.

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

Exploring Behavior Analysis in Video Surveillance Applications

Video surveillance is recently one of the most active research topics in computer vision. It has a wide spectrum of promising public safety and security applications. As the number of cameras exceeds the capability of human operators to monitor them, the traditional passive video surveillance is proving ineffective. Hence, converting to intelligent visual surveillance is inevitable. Intelligent visual surveillance aims to detect, recognize and track certain objects from image sequences automatically, and more generally to understand and describe object behaviors. Many researchers have contributed to the field of automated video surveillance through detection, classification, and tracking algorithms. Despite recent progress in computer vision and other related areas, there are still major technical challenges to be overcome before reliable automated video surveillance can be realized. Recently, the problem of analyzing behavior in videos has been the focus of several researchers’ efforts. It aims to analyze and interpret individual behaviors and interactions between different objects found in the scene. Hence, obtaining a description of what is happening in a monitored area, and then taking appropriate action based on that interpretation. In this paper, we give a survey of behavior analysis work in video surveillance and compare the performance of the state-of-the-art algorithms on different datasets. Moreover, useful datasets are analyzed in order to provide help for initiating research projects.

Simulation-Based Tactics Generation for Warship Combat Using the Genetic Algorithm

In most existing warships combat simulation system, the tactics of a warship is manipulated by human operators. For this reason, the simulation results are restricted due to the capabilities of human operators. To deal with this, we have employed the genetic algorithm for supporting the evolutionary simulation environment. In which, the tactical decision by human operators is replaced by the human model with a rule-based chromosome for representing tactics so that the population of simulations are created and hundreds of simulation runs are continued on the basis of the genetic algorithm without any human intervention until finding emergent tactics which shows the best performance throughout the simulation. Several simulation tests demonstrate the techniques.

Comparison of electromyography and force as interfaces for prosthetic control

The ease with which persons with upper-limb amputations can control their powered prostheses is largely determined by the efficacy of the user command interface. One needs to understand the abilities of the human operator regarding the different available options. Electromyography (EMG) is widely used to control powered upper-limb prostheses. It is an indirect estimator of muscle force and may be expected to limit the control capabilities of the prosthesis user. This study compared EMG control with force control, an interface that is used in everyday interactions with the environment. We used both methods to perform a position-tracking task. Direct-position control of the wrist provided an upper bound for human-operator capabilities. The results demonstrated that an EMG control interface is as effective as force control for the position-tracking task. We also examined the effects of gain and tracking frequency on EMG control to explore the limits of this control interface. We found that information transmission rates for myoelectric control were best at higher tracking frequencies than at the frequencies previously reported for position control. The results may be useful for the design of prostheses and prosthetic controllers.

Effect of a Pneumatically Driven Haptic Interface on the Perceptional Capabilities of Human Operators

This paper describes experimental studies conducted using a pneumatically driven haptic interface (PHI) system. The PHI is a unilateral exoskeletal device that tracks the motion of the shoulder and elbow. The study was carried out to evaluate the impact of an exoskeletal haptic interface on human perceptional capabilities. A population of twenty subjects participated in a set of experiments that were tailored to assess force sensation, shape perception, and effect of force feedback in task performance. Using Weber fractions, we contrasted the outcome of our force sensation experiments against results reported by psychophysical researchers. The results indicated that the perception of weight (or force magnitude) through the haptic interface was significantly affected for relatively low reference force levels (4.44 N, Weber frac tion 5 0.5). The effect progressively diminished as the force level was increased, and almost matched the natural human capabilities for a reference force level of 18 N (Weber fraction 5 0.06). The haptic shape identification experiments showed that the subjects were able to identify various shapes using the PHI system (1 5 0.3 m reference length, with Weber fraction 5 0.38). This identification, however, was adversely affected by the lack of tactile sensation in the haptic device. The outcome of the force-feedback experiments demonstrated mixed results, an observation that was consistent with experimental studies of other researchers. While force feedback did not affect the time needed to complete the task, the subjects’ performance was significantly improved when the experiments involved controlling the thickness of a curve drawn on a pressure-sensitive tablet.

Real-time direct expert control using progressive reasoning

To investigate the capability of knowledge-based real-time control a number of experiments have been performed. The knowledge-based system should combine the advantages of numerical control algorithms implemented in process computer systems with the capabilities of human operators to adjust or tune these algorithms or to take over the operations of the algorithms by manual control. The implemented system combines algorithmic calculations with symbolic reasoning. Starting with a simple, empty shell expert system (Expert-2), written in Forth, a more sophisticated expert shell (Expert-3) has been developed. This shell extended the backward-chaining inference engine of Expert-2 with a number of speed optimizing facilities, such as agenda control and forward chaining. To realize real-time direct expert control this expert system was embedded inside a real-time environment (RETEX) based on real-time Forth. The progressive reasoning concept requiring knowledge divided into different knowledge layers (rule bases), was used to capture varying inference times within the framework of a constant sampling period. In order to control arbitrary processes with unknown parameters, control knowledge has been elicited and divided into five rule bases. The control strategy in the first three layers is based on classification of symbolic states of the process into the phase plane, and in the fourth on ‘Model Reference Expert Control’. The fifth knowledge layer is the supervisor of the underlying layers. The five rule bases contain 170 production rules. Inferencing is performed by a combination of forward and backward chaining. This application of a direct expert control strategy was realized on a 68000-microprocessor system (ATARI 1040ST). It proved to be a robust strategy with a minimum sampling interval of 55 ms for the lowest layer and 430 ms using all the rule bases.

Development of the Letter-Shape Keyboard: A New Approach to the Design of Data Entry Devices

Development of a four key typewriter for Hebrew characters is described. It capitalizes on the visual imagery capabilities of human operators. Parallel and sequential key strokes are employed as graphic elements to reproduce the shape of printed letters. Preliminary experiments show that letter-shape code is acquired with little training and is highly resistant to forgetting. Single hand blind typing at a relatively fast rate is achieved after few hours of training. No difference is revealed between right and left hand operation. Only small and non reliable decrements in data entry rates were observed when system was operated during a simulated flight mission in a F-4 simulator.