IEEE Systems, Man and Cybernetics Magazine - July 2019 - 55

Table 1. The potential biomarkers to measure trust.
No.

Biomarker

Description

1

Eye movement

Eye movement relays huge amounts of information reflecting the mental state of a person [5]. It has been
used as a detector of hazard perception, memory load, and decision-making [3], [6], [7]. New experiments
related to human-autonomous system relationships and development of trust are still missing.

2

Muscle activation and body
movement

The body is affected by the mental state. It was found that body kinematics could be changed with
cognitive overload [8]. Muscle activations measured through electromyography along with body movement profiles (joint angles and velocities) could be used to detect the onset of trust, since humans tend to
become more relaxed as they develop trust in a situation and the other participating members. This could
be detected in less-activated neck and shoulder muscles or in reduced movement in lower limbs.

3

Skin conductance
and peripheral
temperature

When people feel fear, which may be related to situations of distrust, their skin conductance and peripheral
temperature change. Both have been used as detectors of phasic arousal related to fear [9], [10].
Development of trust will eliminate the fear-related arousal, thereby causing a change in peripheral
temperature and skin conductance.

4

Brain activity

A brain activity study of trust and distrust found that they could be assumed to be two distinct constructs
that activate different parts of the brain [11]. Trust is connected to regions associated with reward, prediction, and uncertainty, whereas distrust is connected to regions associated with fear and intense emotions.

5

Heart rate
variability

The difference between trust and distrust can also be differentiated through change
in heart rate and heart rate variability.

close proximity to humans, perhaps even operating on
them soon. I have developed an iTrust Lab comprising
several robots, including models capable of tele-operation, haptic-enabled tele-operation, and autonomous
operation. In addition, various devices such as electroencephalographs, electromyographs, near-infrared spectrometers, eye trackers, and galvanic sensors are used to
collect various biosignals from subjects on whom tests
are being carried out.
Autonomous Systems
The Domain of Autonomous Systems and the
Difference With Automated Systems
The concept of autonomous systems is a very broad
umbrella that covers autonomous vehicles, many different
forms of autonomous robots, and even software-only systems, to some extent. Many people do not realize that we
have been using some form of autonomy in our everyday
lives for a long time. For example, long gone are the days
when a phone call would require a human operator to
make the connection. For the last 10-15 years, we have
been using an automated telephone system every day. We
also use GPS devices that are part of a highly automated
system. Even our mail is sorted using automated technologies for quicker processing. Our social media platforms
contain automated story-picker algorithms to populate
our social feeds.
Recent additions to these automated systems are the
driverless cars and predictive algorithms that run in
always-on mode. These automated systems have been setting the scene for autonomous systems, as the main difference between the two types of systems is the degree of
necessity for human intervention. In general, autonomous

systems will have more intelligence than automated systems, thereby requiring less human intervention. These
interventions may include involvement in start-up or shutdown procedures, control overrides, or regular maintenance of the system.
The domain of autonomous systems is very wide and
far reaching. Every system that currently requires human
input will potentially convert to an automated (and, subsequently, autonomous) system in the future. This advancement will require the use of knowledge and technology
developed in many other domains (Figure 2). For example,
a mechanical system that becomes automated will require
contributions from the domains of electromechanical engineering, physics, math, and computer science. A software
program that becomes an autonomous program will draw
contributions and inspiration from biological systems,
nature, math, and statistics.

Figure 2. Autonomous systems are sitting at an

intersection of technologies developed in other
domains. (Source: Pixabay.)

Ju ly 2019

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

55



IEEE Systems, Man and Cybernetics Magazine - July 2019

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