5 research outputs found
Proceedings of 3. International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4’2020)
Çevrimiçi ( XIV, 67 pages
Quantification of human operator skill in a driving simulator for applications in human adaptive mechatronics
Nowadays, the Human Machine System (HMS) is considered to be a proven technology, and now plays an important role in various human activities. However,
this system requires that only a human has an in-depth understanding of the machine
operation, and is thus a one-way relationship. Therefore, researchers have recently
developed Human Adaptive Mechatronics (HAM) to overcome this problem and
balance the roles of the human and machine in any HMS. HAM is different compared
to ordinary HMS in terms of its ability to adapt to changes in its surroundings and the
changing skill level of humans. Nonetheless, the main problem with HAM is in
quantifying the human skill level in machine manipulation as part of human
recognition. Therefore, this thesis deals with a proposed formula to quantify and
classify the skill of the human operator in driving a car as an example application
between humans and machines. The formula is evaluated using the logical conditions
and the definition of skill in HAM in terms of time and error. The skill indices are
classified into five levels: Very Highly Skilled, Highly Skilled, Medium Skilled, Low
Skilled and Very Low Skilled.
Driving was selected because it is considered to be a complex mechanical task that
involves skill, a human and a machine. However, as the safety of the human subjects
when performing the required tasks in various situations must be considered, a driving
simulator was used. The simulator was designed using Microsoft Visual Studio,
controlled using a USB steering wheel and pedals, as was able to record the human
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path and include the desired effects on the road. Thus, two experiments involving the
driving simulator were performed; 20 human subjects with a varying numbers of
years experience in driving and gaming were used in the experiments. In the first
experiment, the subjects were asked to drive in Expected and Guided Conditions
(EGC). Five guided tracks were used to show the variety of driving skill: straight,
circular, elliptical, square and triangular. The results of this experiment indicate that
the tracking error is inversely proportional to the elapsed time. In second experiment,
the subjects experienced Sudden Transitory Conditions (STC). Two types of
unexpected situations in driving were used: tyre puncture and slippery surface. This
experiment demonstrated that the tracking error is not directly proportional to the
elapsed time. Both experiments also included the correlation between experience and
skill. For the first time, a new skill index formula is proposed based on the logical
conditions and the definition of skill in HAM
Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system
An interest in developing the intelligent machine system that works in conjunction with
human has been growing rapidly in recent years. A number of studies were conducted to
shed light on how to design an interactive, adaptive and assistive machine system to
serve a wide range of purposes including commonly seen ones like training,
manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics
(HAM) was proposed to resolve these issues. According to past research, the focus is
predominantly on evaluation of human skill rather than human performance and that is
the reason why intensive training and selection of suitable human subjects for those
experiments were required. As a result, the pattern and state of control motion are of
critical concern for these works.
In this research, a focus on human skill is shifted to human performance instead due to
its proneness to negligence and lack of reflection on actual work quality. Human
performance or Human Performance Index (HPI) is defined to consist of speed and
accuracy characteristics according to a well-renowned speed-accuracy trade-off or
Fitts’ Law. Speed and accuracy characteristics are collectively referred to as speed and
accuracy criteria with corresponding contributors referred to as speed and accuracy
variables respectively. This research aims at proving a validity of the HPI concept for
the systems with different architecture or the one with and without hardware elements.
A direct use of system output logged from the operating field is considered the main
method of HPI computation, which is referred to as a non-model approach in this thesis.
To ensure the validity of these results, they are compared against a model-based
approach based on System Identification theory. Its name is due to being involved with
a derivation of mathematical equation for human operator and extraction of
performance variables. Certain steps are required to match the processing outlined in
that of non-model approach. Some human operators with complicated output patterns
are inaccurately derived and explained by the ARX models
Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system
An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts' Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo