IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 24

[32]. Therefore, contextual information in the workplace
can be detected with the help of sensors, wearable devices, indoor and outdoor positioning systems, and object recognition capabilities (using fixed or wearable cameras), all
of which can collect historical data for further analysis.
Furthermore, information about equipment used by the
worker can be collected via smart objects and IoT devices.
IoT accessories are capable of identifying other devices by
sending information among themselves based on standard
and interoperable communication protocols, creating a
dynamic global network infrastructure with self-configuring capabilities. Additionally, natural communication
between the user and such systems can be provided via
immersive technologies, combining visual information
with natural interaction, using AR, gesture recognition,
haptic devices, and dialogue-driven voice control. Figure 2
illustrates this structure.
Many technologies can be used to gather contextual
information. But to create adaptive immersive systems, it is
necessary to combine these methods with computational
intelligence techniques to create intelligent applications
that are responsive to users' needs and behavior. Intelligent
immersive decision-support systems can be used not only
to assist decision making, providing recommendations for
situations based on real-time contextual information, but
also as hands-on training platforms for active training.
A decision-support system can be defined as an adaptive system that aids in solving nonstructured problems
using models and data that usually are collected from end
users in an interactive and iterative process, generally
including a knowledge component [33]. These systems usually have three basic characteristics [14]:
1) They support the worker in the best way possible in all
types of situations, particularly in unexpected eventualities.
2) They provide a dynamic perception of the situation that
is adaptive to an employee's actions in real time.
3) They are based on knowledge assets that are intuitively
and cost-effectively generated from existing company
data and constantly upgraded.
Thus, the generation of relevant solutions supposes a joint
process of building context by means of the user and the system working as a team. Human-machine teaming focuses
on the explicit allocation of cognitive functions and responsibilities between a human and an artificial system to
achieve specific goals [34]. From this perspective, human

and machine intelligence-while inherently different and not
interchangeable-can be seen as complementary [35]-[37].
Therefore, tasks are not executed based only on human
action or machine performance; rather, the aim is for them
to be shared and implemented by both parties working in
sync [38], [39].
Hence, it is important to consider other aspects of
human-machine teaming, such as human-machine interaction, communication, and share of the cognitive load.
Combining immersive technology with a decision-support
system could help with these issues, as it would provide a
natural interaction between the user and the system that
could lead to better human-machine interaction and communication. Here, immersive technologies could help in
the creation of what can be called a virtual assistant
focused on domain-specific content.
Finally, as interaction occurs with real-life equipment
used on the job, an intelligent immersive decision-support
system presents engaging opportunities for active training,
reinforcing sensorimotor skills, memory, and higher-order
thinking skills used in problem-solving scenarios. Workers
could benefit from curated content presented in an immersive way, such as 3-D visualizations of specialized equipment, immersive representations of factory floors and
warehouses, or step-by-step instructions with graphical
demonstrations overlaid on the physical equipment. Some
research studies suggest that industry-oriented AR applications, in particular, have the potential to support users
on the job and enhance learning, improving performance
and lowering operational costs [11], [40]. Learning with
technology (rather than from technology) is what distinguishes technology as a cognitive tool [41]. Moreover,
learning within technology creates a mechanism of interaction between content and experience [42], [43]. Immersive learning can provide highly interactive first-person
sensory scenarios, creating unique real-life learning experiences in the work field.
Case Studies
In previous works [44], [45], we introduced examples of
intelligent immersive systems for advanced decision-making support, which combine fuzzy logic with immersive
technologies to provide field service technicians with
assistance when they face new challenges. These solutions
aim to improve customer service by promoting faster

Input
- Sensors (Fixed, Wearable)
- Positioning Systems (Indoors, Outdoors)
- Object Recognition Capabilities
- Natural Interaction (Gesture and Speech Recognition)
- IoT Data (Equipment and Machinery)
Figure 2. An intelligent immersive decision-support system.
24

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 20 18

Output
Intelligent
Decision-Support
Systems

Immersive Technologies
- AR
- VR
- Haptic Feedback
- Auditory Feedback

Table of Contents for the Digital Edition of IEEE Systems, Man, and Cybernetics Magazine - January 2018