Effects on Separated Learning of Acquiring Physical Movement Skills Classified
by Level of Difficulty
LEAVE AUTHORS ANONYMOUS, LEAVE AUTHORS ANONYMOUS, LEAVE AUTHORS ANONYMOUS, LEAVE AUTHORS ANONYMOUS, LEAVE AUTHORS ANONYMOUS, LEAVE AUTHORSANONYMOUS, LEAVE AUTHORS ANONYMOUS, and LEAVE AUTHORS ANONYMOUS
It is common to learn skills for sports while moving the body. Based on this premise, a number of supportive methods have been
proposed to assist learners by moving their bodies while receiving information presentation, such as visual, auditory, and tactile
information presentation. A previous research proposed a learning method called “separated learning”, which divides the learning
process into two phases: one is to concentrate on receiving information presentation without moving the body, and the other is to
move the body. We validated the effects of this separated learning for learning percussion skills using the finger. However, we did
not examine whether the effects of this method can be applied to learning whole-body movements such as dancing and whether theeffects of separated learning are affected by the difficulty level of learning the dance steps involving the whole-body movements. In
this paper, we developed a system to give information presentation on learning dance steps. We evaluated whether the system canbe applied to learning the dance steps and whether the difficulty level of learning them has an impact on the effects of separated
learning. The experimental results showed that there was no significant difference between the effects of learning the dance step
without moving the body and the ones of learning the dance step while moving it.
CCS Concepts: • Human-centered computing → Interaction techniques.
Additional Key Words and Phrases: training, learning method, dance, physical movement skill, information presentation
ACM Reference Format:
Leave Authors Anonymous, Leave Authors Anonymous, Leave Authors Anonymous, Leave Authors Anonymous, Leave Authors
Anonymous, Leave Authors Anonymous, Leave Authors Anonymous, and Leave Authors Anonymous. 2018. Effects on Separated
Learning of Acquiring Physical Movement Skills Classified by Level of Difficulty. In Woodstock ’18: ACM Symposium on Neural Gaze
Detection, June 03–05, 2018, Woodstock, NY . ACM, New York, NY, USA, 17 pages. https://doi.org/XXXXXXX.XXXXXXX

1INTRODUCTION

Many previous studies claimed that giving information presentation such as visual, tactile, or auditory information
presentation in real-time helps people to learn physical movement skills, such as skipping, throwing a ball [31], andplaying music [26]. In conventional learning, researchers created the support systems under the premise that learning
physical movement skills while moving a body is effective. However, the movement tasks that are used for theseresearches often adopted simple movement tasks such as skipping and tapping. Under circumstances where people
address more complicated tasks, they would not obtain the expected benefit from the information presentation. Also,
Todorov et al. [58] developed a support system for learning difficult multijoint movement by minimizing information
presentation. Still, it was not sufficient for transfer to the complicated real-world tasks. We should take account in amethod giving information presentation should be adapted to various complicated tasks.
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1

Conference acronym ’XX, June 03–05, 2018, Woodstock, NY

AnonymousFig. 1. Lack of working memory.

Fig. 2. Concept of a separated learning.

Therefore, we assumed that moving the body and receiving information presentation simultaneously when learning
the skills lead to exhaustion in the working memory of our brains. Working memory is a function for handling otherprocessing tasks in parallel, while temporarily storing the information required for various changing purposes. Complicated body movements consume a lot of working memory, leaving no room for embracing information presentation
Figure 1). Indeed, there are papers on information presentation and working memory [39]. Buszard et al. [7] reported
that when they provided children with five explicit instructions that were specific to the technique of shooting abasketball, higher working memory capacity children displayed consistent improvements, whereas the lower working
memory capacity children had the opposite effect.
To tackle the above problem, the previous research proposed a separated learning [28] that separates a learning phase.
In this method, information presentation is not given while the learners are performing complicated movement tasks.
The learners first receive the presentation of performance information (they are given and internalize information),and then they actually practice it so that it is a two-part learning process (Figure 2). The separated learning supports
learners to master some skills.
The previous research [28] applied this separated learning to support learners to master percussion skills. The
percussion skills involve fingertip-only movements with complex rhythms. We found that learners who practised
the percussion skills after internalizing the information about the skills mastered the skills about 5 minutes quicker
than learners who were given information presentation. From the above result, the learners need to pay attention tovarious aspects when performing complicated movement tasks and have difficulty doing them simultaneously, whichlead to a decrease in the effect of information presentation while performing complicated movement tasks. Therefore,
the separated learning can be clearly more suitable for helping learners master complicated skills than conventional
simultaneous learning.
However, the separated learning does not necessarily work best at all task levels of learning movements, so we needto investigate at which difficulty threshold the separated learning should be applied for the best results (Figure 3). For
example, in the case of simple movement tasks such as skipping, we guess that the working memory can cope with bothmoving the body and receiving information presentation simultaneously. Therefore, the conventional simultaneous
learning which provides information presentation in real-time is suited to these tasks. On the other hand, when tacklingcomplicated tasks such as dance steps involving whole-body movements, the learners have a lack of working memory2

Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY

Fig. 3. Difficulty threshold the separated learning should be applied.

which deals with both moving the body and receiving information presentation simultaneously, so the separated
learning should be applied.
Therefore, in this paper, which primarily focuses on mastering dance steps, we investigate the effects of separated
learning on learning dance steps as classified by levels of difficulty. We aim at clarifying the movements and difficultylevels that affect the consumption of the working memory. This study will lead to clarifying what the most consuming
factor is for the working memory, which helps us to understand which learning procedures the learners who want tomaster the skills should practice and enable teachers to support learners to practice according to individual competence
and condition.
A user study prepared four dance steps corresponding to four levels of difficulty. Participants were required to learn
these four dance steps with both the separated learning and the conventional learning that gives real-time information
presentation. Participants received auditory information presentation via a musical piece, visual information presentation
via a reference video displayed on the laptop (where a dancer shows the correct steps), and tactile information
presentation via multiple vibration motors.
2

RELATED WORKSIn this study, we try to apply separated learning to the learning of dance movements. Since separated learning contains
the process that learners conduct motor imagery, we first review the studies on motor imagery. Next, we describe
various methods of supporting learning skills using information presentation. Finally, we describe some studies on
dance learning support methods.
2.1

Motor imagery

Motor imagery is a mental process that they simulate their movements in their mind without actual movement forskill acquisition. In the research literature on motor imagery, many terms, such as mental practice [15], motor imagery
training [41] and motor imagery practice [13] are used.
Researchers have reported that athletes widely use motor imagery. Murphy [43] reported that 90% of athletes at the
U.S. Olympic Training Center claimed to use motor imagery on a regular basis. Ungerleider et al. [62] reported that 85%
of over 600 prospective Olympic athletes employed imagery techniques while training for competition.
There are also many kinds of research suggested that motor imagery can improve learning and performance ofvarious sports skills. The effect of motor imagery is shown in individual self-paced activities, such as golf putting [52]and rhythmic gymnastics jumping [2]. The effect of it is also shown in multiple skills that are not self-paced such as
rugby tackling [40], tennis service return [53], and table tennis counter-attack forehands [36].
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Anonymous

Our study focuses on working memory during movements while receiving information presentation.
2.2

Information presentation

Researchers have proposed many support methods for learning skills using information presentation via various
perception.
2.2.1 Force information presentation. A system for learning piano performance with electromagnets [37], a system forlearning calligraphy with a robotic arm [24], a system for learning piano performance with electrical stimulation andexoskeleton robot hand that gives a information presentation to skin and inherent senses [25], etc., various support
systems that utilize force perception have been proposed. In this study, we first use the visual, auditory and tactile
information presentation utilized in the previous research [28].
2.2.2 Tactile information presentation. Many support systems for learning skills using a vibration motor device areproposed [49]. Huang et al. [27] proposed a support system for learning a piano performance called “PianoTouch”,
which consists of vibration motors attached to the fingers of a glove and presents the fingering information by vibration.
O’Neil et al. [48] developed a rehabilitation support system for transitioning from sitting to standing using vibration
information presentation linked to users’ body balance. Nakamura et al. [44] developed a device that uses vibrations
to indicate the timing of dance movements. Camarillo-Abad [8] built a system for communication between a leading
dancer and the following dancer via vibration information presentation. In this study, we present tactile information
presentation of the timing of raising the knee and kicking the toes forward in a dance step by vibrating a vibration
motor like the method that Nakamura et al. [44] used.
2.2.3 Auditory information presentation. A number of support systems using auditory information presentation havebeen proposed[3, 11, 17, 23, 32, 57]. Okugawa et al. [47] proposed a system that supports a constant pace of pedallinga bicycle using auditory information presentation. Grosshauser et al. [20] attached a pressure sensor to the feet andan angle measurement sensor to the knees of users and sonified the acquired data to support learning dance skills.
Yamaguchi et al. [65] proposed a system to support dance education by generating sound in real-time in response
to dance. Landry et al. [35] worked on a sonification project to sonify emotions as well as motions and described its
application to dance training. In this study, we did not build a system to acquire and sonify the data. We will use amusical piece that contains timing of movement such as rhythm and melody as auditory information presentation.
2.2.4 Visual information presentation. Visual information presentation is one of the most widely used information
presentation methods in support systems [4, 58]. Nakano et al. [46] proposed “MiruSinger”, which displays the pitch of
the current song and the correct pitch. Doi et al. [14] built a system to support learning Japanese traditional instrument“koto” by projecting images on a koto to show musical techniques. Wu et al. [64] developed a VR ski training systemthat presents the motion patterns of professional ski players in various ways in a VR space, allowing them to learn ski
skills efficiently. In this study, as in the case of auditory information presentation, we do not construct a system tovisualize the acquired data from dance steps, but display a reference video in which the dancer performs the dance
steps on a PC.
2.3

Supportive method for learning dance skills

In this study, we build a support system for learning dance steps to present information. A number of support systems
for learning dance skills have been proposed [51].
4Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY

2.3.1 Automatic scoring and judgment. There are several studies to support the learning of dance skills by providing
users with information presentation using some numerical values that get from the systems. Kyan et al. [34] proposed asystem that captured and evaluates ballet dance movements in real-time in the CAVE virtual environment and visualized
the scores. Chan et al. [10] proposed a system that displayed a virtual character as a instructor using a projector,
captured and analyzed the movements of the learner, and outputted scores each body part. In this study, we asked theevaluators with more than 5 years of street-dance experiences to look at the learners’ dance and judge whether thelearners have mastered the dance steps or not.
2.3.2 Augmented reference video. Some methods to support the learning dance steps by changing the reference dance
video or adding information to it are proposed. Fujimoto et al. [19] proposed a method to support learning dance steps
by mapping learners’ own video to that of a reference dancer. The system generates a sample video of learners’ own
dancing based on a reference video to support learning dance steps. Some systems [38, 42] mirrors to support learning
dance steps. Anderson et al. [1] proposed YouMove, which allows users to check their own movements from various
directions in 3D space and provides a variety of information such as scores, movement effects, and text instructions. In
this study, our proposed system repeatedly play and display a dance video of about 12 seconds as a reference, withoutchanging the reference video or adding information to it.
2.3.3 Mobile Robot. Some studies with mobile robots are proposed [33]. Nakamura et al. [45] proposed a system
in which a screen with reference video is projected on it moves while facing the learner, which allows the user tocheck the physical distance such as back and forth while practising. Tsuchida et al. [61] proposed a method to support
formation practice in group dances by moving a self-propelled screen on which dancers are projected instead of thegroup members. In this study, the dance steps as a movement task did not include moving to other positions and madelearners dance in the same position. Moreover, we did not use a mobile robot to present information.
2.3.4 Virtual reality (VR). Although this study does not utilize VR-based information presentation, we could applied aseparated learning to VR systems. Several VR-based support systems have been proposed [16, 50, 66]. Tsampounaris
et al. [59] developed a system that allows users to change into different avatars, visualize traces of the movementsof various body parts, and interact with virtual objects. Senecal et al. [55] developed a system for presenting virtual
partners in salsa dance where practicing with a partner is important. This study found that the motions of inexperienced
participants converged with those of skilled participants by practicing with the system. Kasahara et al. [29] found thatshowing a slightly futuristic video from motion information of users made them feel their body lighter.
All of the above support systems for learning skills are limited to giving information presentation in real-time whilemoving the body or giving information presentation after practice. It is unclear whether concentrating on receiving
information presentation without moving the body is more effective than practising the dance step while moving thebody. Our study is challenging in this respect.
3
3.1

SYSTEM DESIGN
TargetThis study addresses first-time learners of dance steps as the users of our proposed system. The system focuses onlearning elementary dance steps. To avoid giving information presentation, including overly complex information, ourproposed system mainly supports learning skills related to lower body movements.
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AnonymousFig. 4. Location and timing of vibration information presentation.

3.2

Designing information presentation

Camarillo-Abad [9] attempt to indicate the timing of dance step movements using a combination of vibrations froma wearable vibrating device. As well, Nakamura et al. [44] have developed a system to support the learning of butoh
dance, including the direction and timing of movements, by placing multiple vibration motors on Velcro straps which
can be wrapped around learners’ wrists or other joints. In this study, we develop a system that demonstrates the timingof dance step movements through vibration. Specifically, vibration information presentation devices are attached to
the learner’s legs at four locations: on the knees to indicate the timing when the knee is raised, and on the insteps ofthe feet to indicate the timing when the toes kick forward. The vibration information presentation device mounting
positions are shown in Figure 4. Also, Hall et al. [22] recommend using videos to help learners recall motor imagery;our proposed system presents a reference video in which a dancer performs the dance steps as visual information
presentation. This makes it possible to apply separated learning, including a Learn phase in which learners concentrate
on receiving information, to dance steps at various levels as well.
4

IMPLEMENTATION

We implemented a system for information presentation that can be applied to separated learning in learning dance
steps. Our proposed system consists of vibration information presentation devices with four vibration motors for tactile
information presentation (Figure 5), a main module for controlling the vibration information presentation devices, a PC
for video and audio output, and a timing editing application for vibration information presentation (Figure 6).
A disk-shaped vibration motor (FM34F) was used for the vibration motor, and a MacBook Pro (13-inch, 2019, Four
Thunderbolt 3 ports) was used for the PC. The main module is controlled via ZigBee by the PC. The PC sends instruction
information to the main module in synchronization with the rhythm of the musical piece in the video, the vibration
motor connected to the main module vibrates, and the LED flashes in conjunction with the vibration. A strap andrubber band are used to fix the vibration information presentation device and the main module to the learner’s body.
The PVC wires connecting the main module to the vibration information presentation device are wired so that they do
not interfere with the learner’s dance steps.
An application was implemented using openFrameworks v0.11.0. We conducted the operation check on macOS
Catalina. The application allows users to control the video and edit the timing of the vibration information presentation.
The editing interface for the timing of vibration information presentation is shown in Figure 6. The vertical axis indicates6Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY

Fig. 5. Vibration information presentation device.

Fig. 6. An interface for editing timing of vibration.

Fig. 7. (Top-left) A reference video for learning the dance step. (Top-right)Images for each dance step per count. (Bottom) Dancenotation using footprints (referring to Browning’s notation [6])each position receiving the vibration, and the horizontal axis indicates the timeline. The horizontal axis interval is thebeat unit, which is defined as quarter notes within a measure. When the indicator, which moves as the video plays,
comes within the range indicated by the white square, the PC sends the vibration indication information to the main
module. By dragging a mouse pointer across the rectangular frames on the screen, the user can designate the timingof the vibration to be adjusted. The reference dance video displayed on the PC is shown at the left of Figure 7. The
reference dancer’s dance steps are shot from the front. When learning steps, it is effective for learners to practice with amirror [12]. In order to simulate the effect of a mirror, the displayed videos are flipped left to right. For example, when
the learners raise their right leg, the reference dancer in the video raises his left leg. The musical piece included in thevideo uses mBR0 with an 80 BPM tempo, from AIST Dance Database [60].
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AnonymousFig. 8. Floor plan.

Table 1. The number of foot-step combinations. For example, in the RL(right-left) case, the dancer switches the stepping from the rightfoot to left foot. In the RB(right-both) case, the dancer switches the stepping from the right foot to both feet. In the RS(right-stop)
case, The dancer steps to their right foot and stops moving for 0.5 counts.

Step
1
2
3
4

5

RL
12
12
8
9

LR
12
12
10
9

RR
4
2
4
2

LL
3
0
0
2

RB
0
0
0
1

LB
0
2
2
1

RS
0
0
2
1

LS
0
0
0
1

BS
0
2
2
2

EXPERIMENT

We conducted an experiment in order to clarify the effects of separated learning on learning whole-body dance steps
classified by levels of difficulty. The participants were 12 university students in their 20s (9 men and 3 women). Theirdance experience was at a beginner’s level, having participated in group dance performances at school events or taken
part in physical education classes in junior and senior high school. The experiment was conducted in a university dancestudio with a 43m2 wood floor. The floor plan of the experiment is shown in Fig 8.
5.1

Dance steps

Participants wore the vibration information presentation device shown in Figure 5 and learned four dance steps (seeFigure 5 right). The four dance steps were designed by the first author, who has more than 10 years of street-danceexperience, to make each dance step progressively more difficult to learn. Specifically, the dance steps were designedwith reference to the combinations of left and right steps (Table 1).
We assumed that the learners would have more difficulty learning dance steps with more variations in foot-stepcombinations. We also asked a dancer with over five years of street-dance experience to observe the four dance steps
and confirm that they were graded by increasing difficulty. We filmed the first author dancing these four steps. The
video was cut to approximately 12 seconds and 16 counts at 80 BPM.
8

Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY

Fig. 10. Learn phase and test phase.

Fig. 9. Learning cycle.

5.2

Learning method

In the experiment, while receiving auditory information by sound, visual information presentation by video, and tactile
information presentation by vibration, participants learn the dance steps through the following two methods:
Conventional learning Learners learn the dance steps by dancing them while receiving information.
Separated learning Learners first receive information presentation, and then dance the steps.
The learning cycle of the experiment consists of two phases, the learn phase and the test phase. In the learn
phase, participants learn a dance step for one minute. After one minute of learning, the participants proceed to the test
phase to check whether they have mastered the dance. If they are not able to perform the dance step correctly in theconfirmation test, they take a 30-second break and then return to the learn phase. The participants repeat these twophases until they are able to perform the dance step correctly (see Figure 9). Many studies [21, 54, 63] have claimed thatthe combination of relaxation and motor imagery is effective. Therefore, in order to allow the participants to relax, we
provided a 30-second break after the test phase. The details of the learning and testing phases are set out below.
5.2.1 Learn phase. Figure 10 shows participants learning the dance steps in each of the phases and with each of thelearning methods. The participants received three types of information presentation: auditory information presentation
by an audio output from the PC’s built-in speaker, visual information presentation by a reference video in which
the dancer performs dance steps on the PC’s display, and tactile information presentation by vibration information
presentation devices that vibrate in sync with the music. During the learn phase, the video of the dance step is repeatedly
played. The participants, who cannot stop the video, constantly receive information presentation based on the dance
step.
In the learn phase of conventional learning, participants receive information presentation while performing thedance steps. Our system is not designed for interactive vibration information presentation to the participant according
to their dance steps. Rather, it gives vibration information presentation at regular intervals in sync with the rhythm of
the music.
In the learn phase of separated learning, participants do not perform the dance steps. To focus on internalizing thecorrect dance step information mentally, they receive information presentation while sitting. We assumed that the9Conference acronym ’XX, June 03–05, 2018, Woodstock, NY

Anonymous

stimuli caused by friction between skin and clothes while performing the dance step would reduce the sensitivity of
their perception toward the information presentation. The participants were allowed to perform small movements
such as foot taps while sitting on a chair, so as not to interfere with the information presentation. In this way, in theseparated learning process, the participants first receive information presentation without performing the dance steps;
then, in the following test phase, they perform the dance steps to check their mastery thereof.
5.2.2 Test phase. In the test phase, the evaluators check the dance step that the participants learned in the learn phase.
In the test phase, as shown in Figure 10, the participants listen to the musical piece and perform the dance steps while
watching a reference video in which the dancer perform the dance steps. If the participant can perform the dance
step correctly for 32 consecutive counts, evaluators judged the participant had mastered the dance step, and the test
phase ends. If the participant fails to perform the dance step within one minute from the start of the test phase, theparticipant is allowed to retry to perform. In order to judge whether the participant performs the dance steps correctly
and continuously, evaluator A, who has more than 5 years of street-dance experience, and evaluator B, who has morethan 10 years of street-dance experience, respectively judged. Finally, whether the dance steps were performed correctly
was judged by the agreement of the two evaluators. We adopt this subjective judgement procedure by two evaluators
because it is difficult to construct an automatic judgement system that can accurately judge whether or not a participant
performs dance steps correctly, such as moving the foot laterally or kicking the toes forward.
5.3

Learning cost

In our experiments, we regarded the learn phase and the test phase as one set of learning costs, and we defined it as theminimum unit of learning costs. For example, if the participant conducts the learn phase three times, and they performs
the dance step correctly in the third test phase, the learning cost is three sets.
5.4

Experimental conditions

All participants learn the four dance steps (Step1∼Step4) in the same order. This is because the four dance steps were
designed to be progressively more difficult to learn. Also, the more difficult dance step is designed on the basis of the
movements of the easier dance step. We assumed that if the participant once learned the more difficult dance step, it
would take less time to learn the easier dance steps. All participants learned the dance steps twice, once in conventional
learning and once in separated learning. For example, a participant applied conventional learning to Step 1 and 2
and separated learning to Step 3 and 4. We conducted these six combinations of conventional learning and separated
learning in two sessions for 12 participants.
5.5

Experimental procedure

Participants first enter the studio, wear the vibration information presentation device, and check that the device does
not interfere with their movements. The experimenter explains the participants have to master the four dance steps
and our proposed system. To understand the information presentation from the proposed system, the participants
watch the video that consists simple foot-steps and kicking the toes forward while receiving visual, audio, and tactile
information presentation from the system. This video do not have much influence on mastering the four dance steps.
As a warm up task, the participants perform foot-steps and kicking in rhythm with the video while watching it. They
confirm that they receive vibration information presentation on the knee when the participants raise their knee and on
the instep of the foot when the participants kick out forward. While the video is playing, the experimenter explains10Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY

Table 2. Learning cost for each participant (white filled cells indicate conventional learning, gray filled cells indicate separated
learning).

Participant
A
B
C
D
E
F
G
H
I
J
K
L
Mean

Step1
3
3
4
3
1
1
2
1
2
5
3
2
2.5

Step2
3
1
3
7
3
1
2
1
1
5
2
2
2.6

Step3
2
1
1
2
5
1
3
2
1
1
1
3
1.9

Step4
4
2
6
5
5
3
5
5
3
8
3
5
4.5

Fig. 11. Average learning costs of each learning method.

the counts while clapping their hands. The experimenter tells the participants that if they perform dance steps for
32 consecutive counts without failure, they have cleared one dance step task. Once the participants are familiar withthe system and its tests, they can finish the warm-up task. Next, the participants were explained that there are twolearning methods (conventional learning and separated learning) and two phases (learn phase and test phase). Also,
they were told to judge whether they perform the dance steps correctly by the agreement of two evaluators in the test
phase. Moreover, the experimenter explained that the evaluators cannot give any advice to the participants during
the experiment in order to unify the experimental conditions. After all the explanations are completed, if there are noquestions, the participants signed an agreement document. The experimenter also asked the participants to respondto which of the two types of learning methods they felt was easier to learn: learning the dance steps while moving(conventional learning) or sitting on a chair to focus on receiving the information presentation (separated learning).
6

RESULTS

6.1

Effects of separated learning for learning the dance steps

To begin with, in order to examine the effects of separated learning to learn the dance steps involving whole-bodymovements, we analyze the learning costs of each learning method. The learning cost of each learning method is shownin Table 2 and the average learning cost of each learning method is shown in Figure 11. The gray shaded cells in Table 2
show the results in separated learning. The vertical axis of Figure 11 shows the mean value of the learning cost and theerror bars show the standard errors.
The learning cost for conventional learning is 𝑀 = 3.0, 𝑆𝐷 = 1.8, and that for separated learning is 𝑀 = 2.8, 𝑆𝐷 = 1.7.
We conducted a significance test using T-test according to the two learning methods, and there were no significant
differences (𝑝 > .05). This result means that there is no difference in the time it took to learn dance steps between
practising without moving under the condition of receiving information presentation and practising while moving the
body under the condition of receiving information presentation from the system.
11

Conference acronym ’XX, June 03–05, 2018, Woodstock, NY

AnonymousFig. 12. Combinations of learning methods for normalization that takes into account participants’ learning ability.

6.2

Difficulty threshold for separated learning

We analyze the learning cost of each Step in order to examine whether there are any changes on the effects of separated
learning on learning dance steps classified by level of difficulty. The average learning costs between the learning
methods in each Step cannot be compared as is, as they vary based on each participant’s learning ability. Therefore, we
compare the average learning costs by normalizing them by using the sets of learning costs for other Steps. Specifically,normalization is conducted based on the following formula(1).
′𝑆 step𝑘= 𝑆 step𝑘 /(𝑆 step𝑖 + 𝑆 step𝑗 )(1)

The step number to be normalized is 𝑘, the step numbers to be used for normalization are 𝑖 and 𝑗 respectively, and we′
calculate 𝑆 step𝑘after normalization. However, if all the Steps except the one to be compared were used for normalization,

the difference in learning methods would mean that learning costs could not be compared. For example, suppose we
focus on participant A and participant C and want to normalize their Step1 cost. In that case, participant A uses a
separated learning method in Step2, while participant C uses a conventional learning method. Therefore, if we simply
use Step2 to Step4 costs to normalize Step1 cost, we will ignore the effect of the difference in methods. Thus, they weregrouped under the same combinations of learning methods and normalized respectively. Examples of normalization
calculations and combinations of learning methods are shown in Figure 12.
The average learning costs after applying normalization based on the participants’ learning ability are shownin Figure 13. The vertical axis shows the average learning costs, and the horizontal axis shows the combination ofparticipants. For example, in the case of GH-CD, the bar graphs respectively show the average of the normalized
learning costs for participants G and H in conventional learning and the normalized learning costs for participants C
and D in separated learning. Average is the average cost of learning costs for each learning method. The error bars
indicate the average error. We assessed the difference between these normalized learning costs by using analysis ofvariance (ANOVA). There are two types of factors here: learning methods and Steps. There was a significant difference in12Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY

Fig. 13. Average learning costs after applying normalization based on the participants’ learning ability.

the Steps condition (𝐹 (3, 88) = 23.69, 𝑝 < .05), but there was no significant difference in the learning method condition.
We also assessed the difference in the Steps by using a Bonferroni test. Step 4 was significantly greater than all other
Steps (𝑝 < .05). The difficulty level of learning Step 4 was higher than that of the other Steps. However, there wasno significant difference between the effects of separated learning on learning dance steps as classified by levels ofdifficulty.
In each Step, the learning costs of separated learning tend to be lower in Step 2 than those of conventional learning.
In the interview, one of the participants commented “In the second half, I learned how to remember myself, so I proceededsmoothly”. This comment was translated by Google translate. In separated learning, learners imagine their own motionsin the learn phase. However, compared to the index finger tapping movements which were adopted in a previous
study [28], dance steps require whole-body movements, which makes it difficult for the participants to imagine theirown movements if they have little dance experience. Bertram et al. [5] showed that video information presentation
may hinder beginners’ learning, so there is room for improvement with visual information presentation. Also, before
proceeding to separated learning, participants should begin with a preparatory phase, such as mental rotation [56] for
improving motor imagery skills, to help them visualize the whole-body movements mentally. This experiment was
unable to confirm that the effects of separated learning changed according to the difficulty levels of learning.
7

DISCUSSION AND FUTURE WORK

During the interview after the experiment, two participants mentioned the gap between their imagined movement andtheir actual one. The imagined dance steps involving whole-body movements are more likely to be different from theactual movements than in the case of the imagined tapping with the fingertips. The system should give information
presentation to perceive, grasp, and visualize three-dimensional whole-body movements to fill the gap between theimagined movements and the actual ones. For example, the system can adapt a method to have a learner perceive13Conference acronym ’XX, June 03–05, 2018, Woodstock, NY

Anonymous

three-dimensional whole-body movements by connecting multiple controllable wires [18, 30] to the feet, and a method
to give information presentation to the skin sensation and intrinsic sensation by using electrical stimuli and exoskeleton
robot hands [25]. Moreover, the visual and auditory information presentation in the experiment consisted simply ofplaying back the reference video with the musical piece. For example, to support motor imaging from whole-bodymovements, the system could display a video in which the dancer performed dance steps shot from various directions
to enable the learner to see the whole-body movement in 3D space [1]. Text information could also be added to thevideo [1]. The system could also support the imaged motion by giving audio advice such as “Raise the right knee” oraudio output at different pitches depending on the movement of the right and left feet [35]. In this way, increasing theamount of information presentation could make separated learning more effective.
There is also room to redesign the dance steps to create a more sensitive test protocol. In the previous study [28],the average time for mastering skill in separated learning was about 10 minutes. When considering the sets in thisexperiment, it takes about two minutes per set (one minute each for the learn phase and the test phase). Therefore, weshould adopt tasks requiring an average of five or more sets for learning. However, in the experiment, an average of 4.5
sets was the maximum, and eight out of 12 participants learned the dance steps in one set. If learners master the skills
in about one set, they are less likely to receive the beneficial effects of separated learning which are useful for masteringwhole-body movement skills. Therefore, the dance steps should be designed so that learning them will take longer.
8

SUMMARY

In this study, we developed a system to give information presentation for learning dance steps involving the whole-bodymovements. We examined whether separated learning is effective in learning dance steps. The experimental results
showed that there was no significant difference in the effect of learning dance steps between receiving information
presentation while moving the body and concentrating on receiving the information presentation without moving
the body during performing dance steps. This shows that even though the participants felt it was more effective tolearn dance steps while moving, learning them while moving was not necessarily more effective than learning without
moving. Based on the findings from the experiments, we present a proposal for improving the proposed system toapply separated learning. We also examined whether the effects of separated learning is affected by the difficulty levelof learning the dance steps or not. There was no significant difference between conventional learning and separated
learning. However, for some people, separated learning may be effective depending on the difficulty level of learning. In
the future, we will investigate the structure of the system and the phase for applying the separated learning, and aim at
finding the threshold of effects to apply the separated learning according the difficulty levels of learning.
REFERENCES[1] Fraser Anderson, Tovi Grossman, Justin Matejka, and George Fitzmaurice. 2013. YouMove: Enhancing Movement Training with an Augmented
Reality Mirror. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, UIST ’13. 311–320.
[2] Claudia Battaglia, Emanuele D’Artibale, Fiorilli Giovanni, Marina Piazza, Despina Tsopani, Arrigo Giombini, Giuseppe Calcagno, and Alessandra diCagno. 2014. Use of Video Observation and Motor Imagery on Jumping Performance in National Rhythmic Gymnastics Athletes. Jounral of Humanmovement science 38 (12 2014), 225–34.
[3] Ludovic Baudry, David Leroy, Ràgis Thouvarecq, and Didier Chollet. 2006. Auditory Concurrent Feedback Benefits on the Circle Performed in
Gymnastics. Journal of sports sciences 24, 2 (2006), 149–156.
[4] Anne Benjaminse, Wytze Postma, Ina Janssen, and Egbert Otten. 2017. Video Feedback and 2-dimensional Landing Kinematics in Elite Female
Handball Players. Journal of Athletic Training 52, 11 (2017), 993–1001.
[5] Christopher P. Bertram, Ronald G. Marteniuk, and Mark A. Guadagnoli. 2007. On the Use and Misuse of Video Analysis. International Journal ofSports Science & Coaching 2, 1_suppl (2007), 37–46.
[6] Barbara Browning. 1995. Samba: Resistance in motion. Indiana University Press.
14Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY[7] Tim Buszard, Damian Farrow, Simone J. J. M. Verswijveren, Machar Reid, Jacqueline Williams, Remco Polman, Fiona Chun Man Ling, and Rich S. W.
Masters. 2017. Working Memory Capacity Limits Motor Learning When Implementing Multiple Instructions. Journal of Frontiers in Psychology 8(2017), 1350.
[8] Hector Camarillo-Abad, Alfredo Sánchez, Oleg Starostenko, and Maria Sandoval Esquivel. 2019. A Basic Tactile Language to Support Leader-Follower
Dancing. Journal of Intelligent & Fuzzy Systems 36 (2019), 5011–5022.
[9] Héctor M. Camarillo-Abad, María Gabriela Sandoval, and J. Alfredo Sánchez. 2018. GuiDance: Wearable Technology Applied to Guided Dance. In
Proceedings of the 7th Mexican Conference on Human-Computer Interaction, MexIHC ’18. Article 4, 8 pages.
[10] Jacky Chan, Howard Leung, Jeff Tang, and Taku Komura. 2011. A Virtual Reality Dance Training System Using Motion Capture Technology. Journalof IEEE Transactions on Learning Technologies 4, 2 (2011), 187–195.
[11] D Chollet, M Madani, and JP Micallef. 1992. Effects of two types of biomechanical bio-feedback on crawl performance. Biomechanics and Medicine in
Swimming, Swimming Science VI 48 (1992), 53.
[12] Karen Dearborn and Rachael Ross. 2006. Dance Learning and the Mirror: Comparison Study of Dance Phrase Learning with and without Mirrors.
Journal of Dance Education 6 (2006), 109–115.
[13] Franck Di Rienzo, Ursula Debarnot, Sébastien Daligault, Elodie Saruco, Claude Delpuech, Julien Doyon, Christian Collet, and Aymeric Guillot. 2016.
Online and Offline Performance Gains Following Motor Imagery Practice: A Comprehensive Review of Behavioral and Neuroimaging Studies.
Journal of Frontiers in Human Neuroscience 10 (2016), 315.
[14] Mayuka Doi and Homei Miyashita. 2017. Koto Learning Support Method Considering Articulations. In Proceedings of the 14th International Conference
on Advances in Computer Entertainment Technology, ACE ’07. 368–383.
[15] James Driskell, Carolyn Copper, and Aidan Moran. 1994. Does Mental Practice Enhance Performance? Journal of Applied Psychology 79 (08 1994),
481–492.
[16] Daniel Eaves, Gavin Breslin, Paul Schaik, Emma Robinson, and Iain Spears. 2011. The Short-Term Effects of Real-Time Virtual Reality Feedback on
Motor Learning in Dance. Journal of Presence 20 (02 2011), 62–77.
[17] Alfred Effenberg, Ursula Fehse, Gerd Schmitz, Björn Krüger, and Heinz Mechling. 2016. Movement Sonification: Effects on Motor Learning beyond
Rhythmic Adjustments. Journal of Frontiers in Neuroscience 10 (05 2016).
[18] Cathy Fang, Yang Zhang, Matthew Dworman, and Chris Harrison. 2020. Wireality: Enabling Complex Tangible Geometries in Virtual Reality with
Worn Multi-String Haptics. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. 1–10.
[19] Minoru Fujimoto, Tsutomu Terada, and Masahiko Tsukamoto. 2012. A Dance Training System that Maps Self-Images onto an Instruction Video. In
Proceedings of the 5th International Conference on Advances in Computer-Human Interactions, ACHI ’12. 309–314.
[20] Tobias Großhauser, Bettina Bläsing, Corinna Spieth, and Thomas Hermann. 2012. Wearable Sensor-Based Real-Time Sonification of Motion andFoot Pressure in Dance Teaching and Training. Journal of the Audio Engineering Society 60, 7/8 (2012), 580–589.
[21] Geraldine H Van Gym, Howard A Wenger, and Catherine A Gaul. 1990. Imagery as a Method of Enhancing Transfer From Trailing to Performance.
Journal of Sport & Exercise Psychology 12, 4 (1990), 366–375.
[22] Evelyn G. Hall and Elizabeth S. Erffmeyer. 1983. The Effect of Visuo-Motor Behavior Rehearsal with Videotaped Modeling on Free Throw Accuracyof Intercollegiate Female Basketball Players. Journal of Sport Psychology 5, 3 (1983), 343–346.
[23] Shoichi Hasegawa, Seiichiro Ishijima, Fumihiro Kato, Hironori Mitake, and Makoto Sato. 2012. Realtime Sonification of the Center of Gravity for
Skiing. In Proceedings of the 3rd Augmented Human International Conference, AH ’12. Article 11, 4 pages.
[24] Kazuyuki Henmi and Tsuneo Yoshikawa. 1998. Virtual Lesson and Its Application to Virtual Calligraphy System. In Proceedings of the 1998 IEEE
International Conference on Robotics and Automation, ICRA ’98, Vol. 2. 1275–1280.
[25] Masato Hirano, Yudai Kimoto, and Shinichi Furuya. 2019. Specialized Somatosensory-Motor Integration Functions in Musicians. Journal of Cerebral
Cortex 30, 3 (2019), 1148–1158.
[26] Simon Holland, Anders J Bouwer, Mathew Dalgelish, and Topi M Hurtig. 2010. Feeling the Beat Where It Counts: Fostering Multi-limb Rhythm
Skills with the Haptic Drum Kit. In Proceedings of the 4th International Conference on Tangible, embedded, and embodied interaction, TEI ’10. 21–28.
[27] Kevin Huang, Ellen Yi-Luen Do, and Thad Starner. 2008. PianoTouch: A Wearable Haptic Piano Instruction System for Passive Learning of Piano
Skills. In Proceedings of the 12th IEEE International Symposium on Wearable Computers, ISWC ’08. 41–44.
[28] Hiroyuki Kanke, Tsutomu Terada, and Masahiko Tsukamoto. 2015. A Percussion Learning System by Rhythm Internalization Using Haptic Indication.
In Proceedings of the 12th International Conference on Advances in Computer Entertainment Technology (ACE ’15). Article 14, 5 pages.
[29] Shunichi Kasahara, Keina Konno, Richi Owaki, Tsubasa Nishi, Akiko Takeshita, Takayuki Ito, Shoko Kasuga, and Junichi Ushiba. 2017. Malleable
Embodiment: Changing Sense of Embodiment by Spatial-Temporal Deformation of Virtual Human Body. In Proceedings of the 2017 CHI Conferenceon Human Factors in Computing Systems, CHI ’17. 6438–6448.
[30] Sadao Kawamura, Mizuto Ida, Takahiro Wada, and Jing-Long Wu. 1995. Development of a Virtual Sports Machine Using a Wire Drive System - a
Trial of Virtual Tennis. In Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS ’95, Vol. 1. 111–116.
[31] Toshitaka Kimura, Takemi Mochida, Tetsuya Ijiri, and Makio Kashino. 2010. Real-Time Sonification of Motor Coordination to Support Motor Skill
Learning in Sports. In Proceedings of the 2nd International Conference on Sports Sciences Research and Technology Support, icSPORTS ’14.
[32] Niilo Konttinen, Kaisu Mononen, Jukka Viitasalo, and Toni Mets. 2004. The Effects of Augmented Auditory Feedback on Psychomotor Skill Learning
in Precision Shooting. Journal of Sport & Exercise Psychology 26 (06 2004).
15

Conference acronym ’XX, June 03–05, 2018, Woodstock, NY

Anonymous[33] Kazuhiro Kosuge, Takahiro Takeda, Yasuhisa Hirata, Mitsuru Endo, Minoru Nomura, Kazuhisa Sakai, Mizuo Koizumi, and Tatsuya Oconogi. 2008.
Partner Ballroom Dance Robot -PBDR-. SICE Journal of Control, Measurement, and System Integration 1, 1 (2008), 74–80.
[34] Matthew Kyan, Guoyu Sun, Haiyan Li, Ling Zhong, Paisarn Muneesawang, Nan Dong, Bruce Elder, and Ling Guan. 2015. An Approach to Ballet
Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment. Journal of ACM Transactions on Intelligent Systems
and Technology 6, 2, Article 23 (2015), 37 pages.
[35] Steven Landry and Myounghoon Jeon. 2020. Interactive Sonification Strategies for the Motion and Emotion of Dance Performances. Journal ofMultimodal User Interfaces 14 (2020), 167–186.
[36] Marc Lejeune, Christian Decker, and Xavier Sanchez. 1994. Mental Rehearsal in Table Tennis Performance. Journal of Perceptual and Motor Skills 79,
1 (1994), 627–641. https://doi.org/10.2466/pms.1994.79.1.627[37] Craig Lewiston. 2009. MaGKeyS : a haptic guidance keyboard system for facilitating sensorimotor training and rehabilitation. PhD Thesis. MIT
Media Laboratory (2009).
[38] Zoe Marquardt, João Beira, Natalia Em, Isabel Paiva, and Sebastian Kox. 2012. Super Mirror: A Kinect Interface for Ballet Dancers. In Extended
Abstracts Proceedings of the 2012 Conference on Human Factors in Computing Systems, CHI EA ’12. 1619–1624.
[39] J Maxwell, Richard Masters, and Frank Eves. 2003. The Role of Working Memory in Motor Learning and Performance. Journal of Consciousness andcognition 12 (10 2003), 376–402.
[40] Alex Duncan McKenzie and BL Howe. 1991. The effect of Imagery on Tackling Performance in Rugby. Journal of Human Movement Studies 20, 4(1991), 163–176.
[41] Nobuaki Mizuguchi, Hiroki Nakata, Takuji Hayashi, Masanori Sakamoto, Tetsuro Muraoka, Yusuke Uchida, and Kazuyuki Kanosue. 2013. Brain
activity during motor imagery of an action with an object: A functional magnetic resonance imaging study. Journal of Neuroscience Research 76(2013), 150–155.
[42] Luis Molina-Tanco, Carmen García-Berdonés, and Arcadio Reyes-Lecuona. 2017. The Delay Mirror: A Technological Innovation Specific to theDance Studio. In Proceedings of the 4th International Conference on Movement Computing, MOCO ’17. Article 9, 6 pages.
[43] Shane M Murphy. 1994. Imagery Interventions in Sport. Journal of Medicine & Science in Sports & Exercise (1994).
[44] Akio Nakamura, Sou Tabata, Tomoya Ueda, Shinichiro Kiyofuji, and Yoshinori Kuno. 2005. Dance Training System with Active Vibro-Devices and a
Mobile Image Display. In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS ’05. 3075–3080.
[45] Akio Nakamura, Sou Tabata, Tomoya Ueda, Shinichiro Kiyofuji, and Yoshinori Kuno. 2005. Multimodal Presentation Method for a Dance Training
System. In Extended Abstracts Proceedings of the 2005 Conference on Human Factors in Computing Systems, CHI EA ’05. 1685—-1688.
[46] Tomoyasu Nakano, Masataka Goto, and Yuzuru Hiraga. 2007. MiruSinger: A Singing Skill Visualization Interface Using Real-Time Feedback andMusic CD Recordings as Referential Data. In Proceedings of the 9th IEEE International Symposium on Multimedia, ISM ’07. 75–76.
[47] Ryo Okugawa, Kazuya Murao, Tsutomu Terada, and Masahiko Tsukamoto. 2015. Training System of Bicycle Pedaling Using Auditory Feedback. In
Proceedings of the 12th International Conference on Advances in Computer Entertainment Technology, ACE ’15. Article 17, 4 pages.
[48] Craig O’Neil, Mark D. Dunlop, and Andrew Kerr. 2015. Supporting Sit-To-Stand Rehabilitation Using Smartphone Sensors and Arduino Haptic
Feedback Modules. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct,
MobileHCI ’15. 811–818.
[49] Thomas Peeters, Eric van breda, Wim Saeys, Evi Schaerlaken, Jochen Vleugels, Steven Truijen, and Stijn Verwulgen. 2019. Vibrotactile Feedback
During Physical Exercise: Perception of Vibrotactile Cues in Cycling. International Journal of Sports Medicine 40 (04 2019).
[50] Philo Tan Chua, R. Crivella, B. Daly, Ning Hu, R. Schaaf, D. Ventura, T. Camill, J. Hodgins, and R. Pausch. 2003. Training for Physical Tasks in
Virtual Environments: Tai Chi. In Proceedings of the Proceedings of the IEEE Virtual Reality Conference, VR ’03. 87–94.
[51] Katerina El Raheb, Marina Stergiou, Akrivi Katifori, and Yannis Ioannidis. 2019. Dance Interactive Learning Systems: A Study on Interaction
Workflow and Teaching Approaches. Journal of ACM Computing Surveys 52, 3, Article 50 (2019), 37 pages.
[52] Richard Ramsey, Jennifer Cumming, and Martin Edwards. 2008. Exploring a Modified Conceptualization of Imagery Direction and Golf Putting
Performance. International Journal of Sport and Exercise Psychology 6 (01 2008), 207–223.
[53] Nicolas Robin, Laurent Dominique, Lucette Toussaint, Yannick Blandin, Aymeric Guillot, and Michel Her. 2007. Effect of Motor Imagery Training on
Service Return Accuracy in Tennis: The Role of Imagery Ability. International Journal of Sport and Exercise Psychology (01 2007), 177–188.
[54] Thomas Seabourne, Robert Weinberg, and Allen Jackson. 1984. Effect of Individualized Practice and Training of Visuo-motor Behavior Rehearsal in
Enhancing Karate Performance. Journal of Sport Behavior 7, 2 (1984), 58.
[55] Simon Senecal, Niels A Nijdam, Andreas Aristidou, and Nadia Magnenat-Thalmann. 2020. Salsa Dance Learning Evaluation and Motion Analysis in
Gamified Virtual Reality Environment. Journal of Multimedia Tools and Applications 79, 33 (2020), 24621–24643.
[56] Roger N Shepard and Jacqueline Metzler. 1971. Mental Rotation of Three-dimensional Objects. Science 171, 3972 (1971), 701–703.
[57] Roland Sigrist, Georg Rauter, Laura Marchal-Crespo, Robert Riener, and Peter Wolf. 2015. Sonification and Haptic Feedback in Addition to VisualFeedback Enhances Complex Motor Task Learning. Journal of Experimental Brain Research 233, 3 (2015), 909–925.
[58] Emanuel Todorov, Reza Shadmehr, and Emilio Bizzi. 1997. Augmented Feedback Presented in a Virtual Environment Accelerates Learning of a
Difficult Motor Task. Journal of Motor Behavior 29, 2 (1997), 147–158.
[59] Georgios Tsampounaris, Katerina El Raheb, Vivi Katifori, and Yannis Ioannidis. 2016. Exploring Visualizations in Real-Time Motion Capture forDance Education. In Proceedings of the 20th Pan-Hellenic Conference on Informatics, PCI ’16. Article 76, 6 pages.
16Effects on Separated Learning of Acquiring Physical Movement Skills Classified by Level ofConference
Difficulty acronym ’XX, June 03–05, 2018, Woodstock, NY[60] Shuhei Tsuchida, Satoru Fukayama, Masahiro Hamasaki, and Masataka Goto. 2019. AIST Dance Video Database: Multi-genre, Multi-dancer, andMulti-camera Database for Dance Information Processing. In Proceedings of the 20th International Society for Music Information Retrieval Conference,
ISMIR ’19. 501–510.
[61] Shuhei Tsuchida, Tsutomu Terada, and Masahiko Tsukamoto. 2013. A System for Practicing Formations in Dance Performance Supported by
Self-Propelled Screen. In Proceedings of the 4th Augmented Human International Conference, AH ’13. 178–185.
[62] Steven Ungerleider and Jacqueline M. Golding. 1991. Mental Practice among Olympic Athletes. Journal of Perceptual and Motor Skills 72, 3 (1991),
1007–1017.
[63] Robert Weinberg, Thomas Seabourne, and Allen Jackson. 1987. Arousal and Relaxation Instructions prior to the Use of Imagery-effects on Image
Controllability, Vividness and Performance. International Journal of Sport Psychology 18, 3 (1987), 205–214.
[64] Erwin Wu, Florian Perteneder, Hideki Koike, and Takayuki Nozawa. 2019. How to VizSki: Visualizing Captured Skier Motion in a VR Ski Training
Simulator. In Proceedings of the 17th International Conference on Virtual-Reality Continuum and Its Applications in Industry, VRCAI ’19. Article 5,
9 pages.
[65] Tomoyuki Yamaguchi and Hideki Kadone. 2014. Supporting Creative Dance Performance by Grasping-type Musical Interface. In Proceedings of the2014 IEEE International Conference on Robotics and Biomimetics, ROBIO ’14. 919–924.
[66] Ungyeon Yang and Gerard Jounghyun Kim. 2002. Implementation and Evaluation of "Just Follow Me": An Immersive, VR-Based, Motion-Training
System. Journal of the PRESENCE: Virtual and Augmented Reality 11, 3 (2002), 304–323.

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