Moreover, body movements are incorporated as part
of the observation behavior of a fossil exhibition,
which previously consisted of a conventional written
explanation. The system enables learners to enhance
their sense of immersion in a paleontological
environment and learn about the fossil itself and its
paleontology.
In this paper, we summarize the prototype of
"BELONG" as the first step toward developing the
immersive learning support system for the fossil
exhibition at the museum. In addition, we describe the
results of our experimental evaluation of the learning
support and immersion abilities of the system with the
aim of clarifying whether it can provide learners with
a realistic paleontological observation experience.
2 LEARNING SUPPORT SYSTEM
2.1 Belong
We aim to realize the immersive learning support
system "BELONG" that simulates a paleontological
environment and transitions that are impossible to
experience in reality for efficient learning at the
museum. Figure 1 illustrates the concept of
"BELONG." This system accepts body movements as
input for observational behavior. The movements of
the whole body and the system operation are linked;
therefore, it is possible to enhance the sense of
immersion in the paleontological environment. The
sense of immersion improves if the system can be
operated in conjunction with complicated body
movements as compared with a case in which the
system is operated with simple body movements. The
recognition of complicated body movements should
not involve attaching expensive sensors or devices to
learners when implementing it in a museum. In this
system, we utilize Microsoft’s Kinect v2 sensor, a
range-image sensor originally developed as a home
videogame device. Because BELONG comprises
only a Kinect v2 sensor, projector, and control PC, it
allows us to provide a low-cost immersive learning
experience within a small space. The advantage of
this arrangement is that it is possible to easily change
the learning contents. Moreover, we recognize the
body movements of learners by gesture recognition
using the Kinect v2 sensor. The gesture recognition
system, which can also interpret complicated body
movements, registers the body movement the creator
wishes to recognize and judges whether it is
recognized by verifying the similarity with the body
movement.
Figure 1: Concept of BELONG.
2.2 Configuration of the System
We developed an immersive learning support system
"BELONG" that simulates a paleontological
environment and transitions that are impossible to
experience in reality.
As a first step towards the
realization of this system, we are developing a system
to simulate paleoecology, especially learning about
dinosaurs, based on experiences that simulate a
paleontological excavation.
Our assumption was that
learners' interest would increase by virtually
excavating fossils included in the current exhibition.
However, because excavation motions are complex
body movements, gesture recognition was used.
(Tokuoka, M., 2017) When the excavation proceeds
successfully, videos showing the characteristics of the
dinosaur are displayed.
Linking the body and the
video in this way increases the sense of immersion.
These body movements are recognized by a Kinect v2
sensor, the properties of which are described below.
Microsoft’s Kinect v2 sensor is a range-image
sensor originally developed as a home videogame
device. Although it is inexpensive, the sensor can
record sophisticated measurements regarding the
user’s location. Additionally, this sensor can
recognize humans and the human skeleton using the
library in Kinect’s software development kit for
Windows. Kinect can measure the location of human
body parts such as hands and legs, and it can identify
the user’s pose or status with this function and the
location information. Moreover, Kinect Studio and
Visual Gesture Builder are used to recognize
complicated body movements captured by the Kinect
sensor. These enable complicated body movements to
be recognized using the discriminator (Tokuoka, M.,
2017). By using these, it is possible to create a
discriminator that registers the body movements we
want to recognize and can accurately recognize body
movements using machine learning. As a complicated