ArticlePDF Available

Systematic mapping of technologies for supporting choreographic composition


Abstract and Figures

Technology has increasingly occupied other areas of sciences and humanities, including art and dance. Over the years, initiatives to use technological applications in artistic performances have been observed and this research is developed regarding this context and the challenge of using technology to support the artist’s imagined creations. The systematic mapping of the literature carried out is part of a broad search for studies that portray the interdisciplinarity of these two universes, aiming to find technologies that support the choreographic composition process, focusing on tools that work together with the choreographer’s activities. The methodology consisted of using search terms in research repositories, which initially returned 635 publications, which were filtered by inclusion and exclusion criteria, to undergo further analysis. Eighteen tools were identified and explored in which the main applicability was the simulation of movements through graphic animation. From the operating mode of these applications, the challenges of the existing relationship between technology and the creation of dance were discussed. This study only incorporates technologies that act as a support tool by sharing the compositional effort, which creates the opportunity for future investigations into other ways of using technology in dance creation. The main contribution of this paper is identifying and classifying the main integration strategies of technology and dance composition, as well as summarizing the data and discussing its implications, been the identification of the lack of involvement of artists (end users) in the early stages of the development process the most relevant finding.
Content may be subject to copyright.
Journal on Interactive Systems, 2022, 13:1, doi: 10.5753/jis.2022.2635
This work is licensed under a Creative Commons Attribution 4.0 International License.
Systematic mapping of technologies for supporting
choreographic composition
Iago Felicio Dornelas [ Universidade Federal de Itajubá | ]
Rodrigo Duarte Seabra [ Universidade Federal de Itajubá | ]
Adler Diniz de Souza [ Universidade Federal de Itajubá | ]
Technology has increasingly occupied other areas of sciences and humanities, including art and dance. Over
the years, initiatives to use technological applications in artistic performances have been observed and this research
is developed regarding this context and the challenge of using technology to support the artist’s imagined creations.
The systematic mapping of the literature carried out is part of a broad search for studies that portray the interdis-
ciplinarity of these two universes, aiming to find technologies that support the choreographic composition process,
focusing on tools that work together with the choreographer’s activities. The methodology consisted of using
search terms in research repositories, which initially returned 635 publications, which were filtered by inclusion
and exclusion criteria, to undergo further analysis. Eighteen tools were identified and explored in which the main
applicability was the simulation of movements through graphic animation. From the operating mode of these ap-
plications, the challenges of the existing relationship between technology and the creation of dance were discussed.
This study only incorporates technologies that act as a support tool by sharing the compositional effort, which
creates the opportunity for future investigations into other ways of using technology in dance creation. The main
contribution of this paper is identifying and classifying the main integration strategies of technology and dance
composition, as well as summarizing the data and discussing its implications, been the identification of the lack of
involvement of artists (end users) in the early stages of the development process the most relevant finding.
Keywords: Dance Creation, Choreography Composition, Choreography Design, Technology, Systematic Map-
1 Introduction
The use of technology in the artistic universe is a research
area that requires interdisciplinary efforts between computer
science and the human sciences and, therefore, has been the
subject of studies in the academic world. Part of this interest
can be explained by the evolution of computational capabil-
ities and the accessibility to technological devices and tools
by a large part of society. In 1996, Hill apud Sagasti (2019)
listed approximately 100 published articles and books deal-
ing with the interaction of dance with technology, and this
number has been growing ever since.
The application of computation to dance composition, ei-
ther as a support for choreographers or for related purposes,
can be found in studies into the early stages of the emer-
gence of computing, in the 1960s, to the present, and can be
associated with goals, such as (Sagasti 2019) motion cap-
ture, storage and access; simulation of environments and the
human body; control of resources associated with stages,
such as lighting and scenic structures; real-time interactions;
and using algorithms, neural networks and artificial intelli-
gence to complement or even to replace the work of chore-
According to Lobo and Navas (2019), the expression
‘choreography’ has a Greek origin meaning “graphy of cho-
ral dances, group dances”. However, the term began to be
used to name any spellings or writings of the movement and
was no longer restricted to collective dances over time.
Been the art of composing dance a product originated
from the choreographer’s imagination it becomes a research
problem how to create technologies with capabilities of sup-
porting the choreography composition process, since it is
not only based on imagined constructs of dance such as
movements and use of space, but also artists can present dif-
ferent skills and familiarity with technology solutions.
This work aims to carry out a systematic mapping identi-
fying the technological tools that contribute as a support to
the work of choreographic composition, to understand the
state of the art in this field of research. It is worth emphasiz-
ing that the term support represents a range of ways to aid
(Cambridge Dictionary 2021), that is, everything that facil-
itates or provides means of producing, transforming and/or
visualizing movements or some other aspect directly related
to dance, and that has some kind of interactivity with the
choreographer, in order to share the compositional effort.
Computer science and human sciences are two areas that
demand different study backgrounds, skills, and abilities
and, therefore, can present challenges to end users inherent
in both areas, such as limitations in knowledge in dance
composition or lack of ease with the use of technological
The identification and understanding, through historical
chronology, of how the end user is positioned in the devel-
opment of such applications and the characteristics of the
tools can point out the main trends, challenges, and prob-
lems in the intercalation of the two areas and make research-
ers in the area aware of critical points. This leads us to the
main motivation for this work: to support future work by
providing the state of the art in this field of research.
This paper produced a mapping of supporting technolo-
gies in choreographic by identifying and classifying the
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
main integration strategies of technology and dance compo-
sition. Besides provided chronologically the results, it also
generated an infographic summarizing the categorization of
the technologies found. The modus operandi of the tools was
discussed, and it was verified a lack of involvement of artists
(end users) in the development process.
The article is structured as follows: Section 2 presents the
research methodology used to collect and to extract infor-
mation from the articles found. Section 3 presents and dis-
cusses the results. Finally, Section 4 presents the conclu-
sions of the study.
2 Research Methodology
The systematic mapping of the literature proposed by this re-
search aimed to recognize and classify technological re-
sources developed focusing on supporting choreographic
composition, in addition to discussing aspects of the modus
operandi of these tools. The methodology used here explores
research questions, search protocols, selection, classification,
and data extraction based on (Petersen et al. 2008).
2.1 Questions of interest
Given the main objective of understanding the state of the art
in the use of technological tools to support choreographic
composition, the following research questions were defined,
to help mapping how the communication of both areas is im-
plemented and how usability concepts are used, if applied:
Q1: How do technologies work to support choreo-
graphic composition?
Q2: How is the intercalation between technological re-
sources and concepts of dance composition?
Q3: Are usability concepts applied to technological so-
lutions to support choreographers?
Research questions were determined to help understand
the features of the tools. The first question is the starting
point for extracting data from publications. The second ques-
tion contributes to understanding the interdisciplinary rela-
tionship between dance and technology, in addition to seek-
ing to list specific aspects between these two sciences. The
last question seeks to understand the challenge of applying
such technologies by identifying whether the application end
users (choreographers and dancers) were involved in the re-
search process and, therefore, whether the solution was de-
veloped seeking to meet the needs of dance composers, who,
in turn, may have different levels of familiarity with compu-
2.2 Research and selection protocol
The authors conducted a systematic approach designed to
identify and extract data from the studies. The first step con-
sisted in defining a search protocol with search terms that
cover an expressive and focused number of researches in the
field of choreographic composition using technology. The
search terms were: (“dance composition” or “choreography
design” or “dance creation”) and (“software” or “technol-
ogy” or “web” or “mobile” or “app” or “application”). The
definition of these terms was made of the author’s agreement
of using simple and generic words associated with the re-
search, to get a large sample of records opposing to increase
the specificity of the terms.
These terms were duly adapted to the restrictions of the
repositories chosen and initially returned 635 records. The
search terms used in each repository are shown in Table 1
and the searches were carried out on July 17, 2021, without
any type of date restriction, in order to include recent publi-
Table 1. Search expressions in digital libraries. Source: The authors.
Search expression
Springer Link
ACM Digital Li-
IEEE Digital Li-
(("All Metadata": DANCE COMPOSITION)
OR ("All Metadata": CHOREOGRAPHY
DESIGN) OR ("All Metadata": DANCE
CREATION) OR ("All Metadata": CHORE-
Metadata": SOFTWARE) OR ("All
Metadata": TECHNOLOGY) OR ("All
Metadata": WEB) OR ("All Metadata": MO-
BILE) OR ("All Metadata": APP) OR ("All
Metadata": APPLICATION))
After the initial search process, duplicate documents (45)
were eliminated and, of the remaining 590, inclusion (IC)
and exclusion criteria (EC) were applied, as detailed in Ta-
ble 2. Since for the same study more than one criterion could
be applied in certain circumstances, the order of registration
of the criterion was adopted. This step, conducted by one of
the authors, reduced the search to 46 articles for complete
reading and data extraction. It is noteworthy that three re-
searches from the Scopus repository were not available for
reading to the authors of this study.
During the reading task, publications (Alaoui et al. 2014,
Carlson et al. 2015, Sagasti 2019, Zhang 2020) stood out for
also bringing research work on the use of technologies in
dance composition. From them, a snowballing process was
carried out, which expanded the number of surveys included
in the analysis. In this new process, 27 references were
mapped to be added to the number of selected articles, but
only 23 were found with available access. Additionally, it is
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
important to point out that this procedure increased the total
number of articles in some repositories, besides including
others not initially planned.
Table 2. Inclusion and exclusion criteria set. Source: The authors.
The study presents a technology used in the
choreographic composition process
Snowballing (entered directly through the
references of some other study)
Language other than Portuguese, English, or
The study is not related to dance
Technology is used as an actor or character
and is not intended to support the work of
choreographers (examples: focusing on ro-
bots, game characters, among others)
Technology is used in/as a scenic element or
accessory and not as a support tool in the
choreographic creation process (examples:
automated scenarios, projections, among
Technology is used as a medium, be it for
dynamic and real-time interaction, dissemi-
nation, storage, post-production, or related
and distinct purposes from supporting chore-
ographic composition (examples: digital
files, video editing software, transmission
tools in real time, among others)
Works in which there is no evidence of the
use of technology to create a dance in the ti-
tle, abstract or body of the text (example:
works with incomplete information)
Studies that do not use any technology in the
context of dance (example: exclusive discus-
sions about dance)
Studies that, despite involving dance and
technology, do not demonstrate a relation-
ship with choreographic composition
False positive (example: surveys that present
a discussion on the subject, yet superficial or
inconclusive for this work)
The complete reading and data extraction processes were
conducted by two authors and a third one contributed by
solving divergences. The process consisted of a detailed
reading from the authors and merge of the extractions of
each author as the final data to be considered.
Only one article initially selected for analysis and extrac-
tion was classified as false positive. The work by (Felice et
al. 2016) conducted an interview with six choreographers to
identify the choreographic composition process to propose a
framework that supports the construction of digital tools to
assist in the creation of dances. However, no technology was
theorized or developed within the scope of the study, not
even a prototype. Therefore, the search classification was
changed to the EC-09 exclusion criteria. Table 3 compiles
the number of records associated with each repository and
their inclusion or exclusion criteria. A spreadsheet associat-
ing the searches found with the exclusion criteria is available
at the link
. Figure 1 shows an infographic that details the
step numbers related to the research protocol and article se-
lection. Figure 2 shows the categorization (selected, rejected
and duplicated) by year of the analyzed papers.
2.3 Data extraction and categorization
The information extracted from the articles identify 56
distinct technologies. When more than one research referred
to the same technology, the publication that best described
the tool or the most recent one was chosen.
It was used a standard form to collect details of technol-
ogies mentioned in the papers, focused on selecting or ex-
cluding applications that complied with the objective of the
study and provided understanding about the modus operandi
of the tools.
Figure 4 identifies and indexes the technologies ex-
tracted by the authors, ordered through year. Note that when
the application name did not exist, the name of the first au-
thor of the study and a generic description were used (e.g.,
author`s software). To preserve the chronology of publica-
tions, duplicate records were kept and mentioned more than
once, in addition to being referenced with the ID of the one
technology to be considered. Moreover, it is important to
emphasize that by the criteria of this research, even without
applying publication year restrictions in repository searches,
only one technology was selected in the last five years. A
trend of how the technology is used over time is discussed in
Section 3.
Additionally, some tools that were identified and ap-
proved in the inclusion criteria, due to their capability of use
in choreographic composition, were filtered in an additional
step that sought to find applications that actually operate as
an accessory or work together with the participation of the
professional in the act of composing, as expected, consider-
ing the main research goal.
Throughout the extraction process a thematic analysis,
which consists of a method that aims at identifying, analyz-
ing and reporting patterns or themes (Wohlin et al. 2012),
was carried out to organize and present the technologies.
This analysis was applied to selected and excluded technol-
ogies and categorized in two themes: output operation and
method of interaction. Table 4 shows the excluded technol-
ogies according to the operation output and method of inter-
action with users.
Regarding the output operation, the following categories
were identified:
Generation of new movements: From movements pro-
vided, either by a pre-defined database or by the user,
the system generates new movements or sequences not
previously listed in the application database.
Movement transformations: From a sequence of move-
ments, the system can apply processes, such as re-
moval, repetition, reordering, transformation, etc. The
base of predefined movements can be provided in the
application or by the user.
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
Table 3. Records found by repository and classification by inclusion and exclusion criteria. Source: The authors.
IEEE Digital Li-
Springer Link
Other repositories
42 (6.5%)
23 (3.27%)
Total inclusions (%)
10 (15.38%)
13 (20%)
10 (15.38%)
12 (18.46%)
65 (100%)
3 (0.45%)
355 (53.79%)
16 (2.42%)
6 (0.91%)
16 (2.42%)
19 (2.88%)
38 (5.76%)
91 (13.79%)
1 (0.15%)
Not found
7 (1.06%)
45 (6.82%)
Total exclusions (%)
273 (45.73%)
47 (7.87%)
212 (35.51%)
4 (0.67%)
597 (100%)
Grand total (%)
283 (42.75%)
60 (9.06%)
222 (33.53%)
16 (2.42%)
662 (100%)
Figure 1. Research methodology used in systematic mapping. Source: The authors.
Figure 2. Categorization of papers by year. Source: The authors.
Figure 3. Identification and filtering of the technologies used from the articles selected. Source: The authors.
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
Insights generation: The output of the application is not
necessarily related to dance (e.g., sounds) or with spe-
cific attributes, such as stage positioning and timing. It
requires a rule to be interpreted in the context of chore-
ographic composition.
Table 4. Categorization of excluded technologies. Source: The au-
Operation Output
/ independent
Tool con-
figuration /
or out of con-
Generation of
new move-
[T17, T25,
T31, T33,
T39, T43,
T44, T45,
T46, T47,
T48, T49,
T50, T51,
T53, T55,
[T14, T20]
[T11, T38]
[T18, T29]
Insights gener-
[T9, T15,
[T1, T3,
T4, T8,
[T34, T41]
Inconclusive or
out of context
[T12, T16,
T42, T54]
Regarding the interaction of the tool, the categories con-
sidered were:
Autonomous/independent operation: The choreogra-
pher does not act in the process; he/she just starts the
application and uses the result.
Tool configuration/parameterization: The tool has in-
ternal setting parameters. It requires code change or
basic knowledge of the core engine of the application.
In both analysis criteria, it is possible to find technologies
whose description fits into:
Inconclusive or out of context: Technologies found in
the extraction process that were not explained in suffi-
cient detail to understand their operation or output, or
were applied outside the context of this research, that
is, choreographic composition in dance.
In Section 3, we discuss some characteristics associated
with the high number of exclusions. Finally, from the 56 dif-
ferent tools found, 18 were selected for descriptions of the
modus operandi (as shown in Figure 3), to answer the re-
search questions. Figure 4 presents a timeline with all tech-
nologies found, highlighting whether they were selected, ex-
cluded or already mentioned over the history.
The extracted data established to comply with the main
objective of this study, was as follow:
1. Goal: It identifies if the developed tool helps the cho-
reographic process (primary goal) or if it is a tool de-
veloped with different purposes but also used for cho-
reographic composition (secondary goal).
2. Method of use: It informs how an interested person
would be able to use the technology, which can be di-
vided into: (i) mobile specifically developed for mo-
bile devices, such as tablets and cell phones; (ii) web
available through browsers; (iii) computer any soft-
ware or algorithms that run on computers; (iv) specific
hardware custom artifacts required to handle the tool.
3. Dance characteristics: It describes which technical as-
pect of dance is intended for the tool. In cases whereby
there is no restriction to a specific modality, the tool
was categorized as generic.
4. Final product: It focuses on describing how the objec-
tive is outputted, made available and/or represented to
the user.
5. Notation system: If any, identifies which movement
notation systems are used.
6. Movement interaction: According to Soga et al. (2001),
there are some ways to describe human movement,
which can be categorized into: (i) movement level
each specific part of the human body can be manipu-
lated (e.g., Labanotation); (ii) steps level some
dances have combinations of small movements pre-de-
fined and with specific nomenclatures (e.g., the move-
ment “rond de jambe” in classical ballet); (iii)
pieces/repertory level in restricted cases, a large se-
quence of movements can be standardized, such as rep-
ertory ballets (e.g., Marius Pepita`s ballet piece The
Sleeping Beauty).
7. Graphical user interface: It mentions whether or not it
has a graphical interface for handling the system.
8. Operation description: It focuses on the application op-
erating process.
9. Additional considerations: important information iden-
tified by the authors, not applied to the previous items.
The term “Undefined” was adopted in cases in which it
was not possible to establish a clear conclusion. Table 5
shows the result of the extraction process, detailing items 1
to 7 for each of the technologies selected. Table 6 presents
the operating characteristics of the tools (interaction and out-
put of the system). A table with more detailed description
and considerations about the modus operandi of the tools
(Item 9) can be accessed at the link
Moreover, it’s important to note regarding the research
protocol, according to Wohlin et al. (2012), the validation of
a study denotes the confidence of its results, in order to guar-
antee that they are true and not biased by the researchers. The
main threats identified throughout the research protocol are
associated with reliability and generalizations, since data ex-
traction depends on the authors' identification of the charac-
teristics of the technologies. The mitigation of these threats
was carried out by the following actions:
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
The extraction of data was made by two authors and a
third one resolved conflict.
A data extraction form, with objective and direct items,
was used in an online tool for supporting literature re-
views (Parsifal, 2021).
It was included the options "inconclusive", "out of con-
text” or “undefined” in the extraction items to portray
the lack of information in the categorization of technol-
ogies and to minimize subjectivity and assumptions in
the act of extraction.
Figure 4. Timeline of the technologies considered in this research and classification in the filtering process. Source: The authors.
Table 5. Modus operandi of the technologies selected. Source: The authors.
Method of use
Dance charac-
Final product
Notation system
Movement inter-
action (level)
Graphical User In-
Noll’s Project
Graphic Animation
Choreology Project
Classical Ballet
Graphic Animation
Benesh Notation
Graphic Animation
Peter Rajka Symbolic
Graphic Animation
ARTBODIES’ software
Graphic Animation
Pas Editor
Classical Ballet
Graphic Animation
Graphic Animation
Web3D Dance Composer
Classical Ballet
Graphic Animation
TED (Tele-Immersive Dance)
Web; Specific
Modern Dance
Video Interaction
Specific Hardware
Graphic Animation
Choreographer’s Notebook
Video Interaction
Cabral’s video annotator
Video Interaction
Viewpoints AI
Computer; Spe-
cific Hardware
Video Interaction
TKB Creation Tool
Video Interaction
Counterpoint Tool
Graphic Animation
iDanceForms (iDF)
Graphic Animation
Graphic Animation
Eshkol-Wachman Move-
ment Notation; Labanotation
Krylov and Samsonovich’s
Graphic Animation
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
3 Discussion and Results
This section discusses the results from the questions pro-
posed in this research, relating them to the information ac-
quired from the data extraction.
Q1: How do technologies work to support choreo-
graphic composition?
From the analysis of the articles initially selected and
with the identification of technologies that aim to support the
choreographic composition process, it was possible to ob-
serve that there is a concentration of tools in two categories:
movement simulation through graphic animation (72.3%)
and digital interaction in video content (27.7%), summarized
in Table 6.
Table 6. Categorization of selected technologies based on interaction and
operation output (item 9). Source: The authors.
Operation Output
Graphic animation
Video interaction (off-
line or real-time)
Application in-
[T2, T7, T10, T13,
T21, T24, T36,
T37, T52]
[T22, T26, T27, T30,
Movement no-
tation records
[T5, T6, T19, T40]
Graphic animation outputs manifested different charac-
teristics between technologies. T2 and T24 produced anima-
tions in stick figures, and similarly, T13 and T21 offered a
skeleton visualization, while T7 mentioned the use of ani-
mations with cartoons. T5, T6, T19, T37, T40 and T52 set
out to work with humanoid bodies. T10 explicitly mentioned
the use of animations but gave no indication as to whether
they would be stick figures or humanoid bodies.
The ability to move a human body, at first, seems advan-
tageous given the number of details that can be worked on,
even if the complexity of the tool increases. However, no
study has discussed or presented solutions for the diversity
of bodies and consequently diverse motor skills present in
the dance universe, forcing the choreographer to create using
very specific bodies whose idealized movements dancers
may not be able to reproduce. Applications with stick figures
or symbolic images of human bodies (cartoons) can be seen
as a solution to reduce this restriction, however, it limits the
visual richness in the final product and loses the appeal of
using such tools.
T22 and T30 proposed the virtualization of bodies,
through motion capture sensors, such as Kinect, and allowed
the generation of graphic animations with insertions of other
virtual objects (augmented reality). However, in these cases
infrastructure and equipment were needed, which not only
restricted movement throughout the space, but also repre-
sented extra expenses and investments, opposing to the tech-
nological apparatus of other technologies that required sim-
pler equipment such as computers, notebooks, smartphones
and/or tablets. T24 mentioned the use of a touch screen
panel, in the first citation in 2010, and the latest version, from
2019, the tool was adapted to computers.
The technology with the simplest animation generation
was T36, whose focus was restricted to the use of space by
dancers. By using particles to represent people, the applica-
tion has become simplified, but despite dealing with one of
the inherent elements of a choreography, which is spatial
movement, it does not offer help regarding body movement
details. As Calvert et al. (1993) noted, users sometimes pre-
fer a simpler, more abstract representation, since it’s still
possible to represent movement patterns and it offers less
distractions than more realistic models that draw attention to
aspects which depart from reality.
Moreover, tools whose output was video interaction
(T26, T27 and T32) made possible what no previously men-
tioned tool was capable of: preserving bodies and natural
mechanics. However, it started from the input of previously
recorded videos of real dancers and requires initial work be-
fore using the tool, in addition to being restricted to draw-
ings, annotations and comments.
It is important to highlight that, according to the infor-
mation provided in Figure 4, it was clear that numerous tools
were excluded from the final selection. This is because the
field of arts is directly associated with creativity. Thus, any
type of interaction with any event or object can be used, even
if minimally, as inspiration. Hence, this event or item could
be understood as support. However, Carlson et al. (2015) re-
inforce that many current technologies do not allow creative
compositional choices and do not help in the process of de-
signing dance movements and choreographies. Tools, such
as Photoshop, Blender and Microsoft PowerPoint, provide a
blank space for inputting ideas, and even a generator of num-
bers, images or random words could contribute, but they do
not necessarily offer artistic assistance. Another feature
found were tools that use artificial intelligence for automatic
compositions, which drastically reduces or eliminates the
choreographer`s participation in the process, as shown in Ta-
ble 4.
It was noted a trend in using artificial intelligence nowa-
days, which restricts the involvement of the choreographers
in the tools using this strategy. The fact that, in the latest five
years only one technology was selected to this study, can be
explained by this tendency of using such no inclusive or pur-
posely exclusionary strategies. The tool that best presented
an effort sharing relationship was T52, as it applied compu-
tational intelligence to suggest viable options for transition-
ing between inserted movements and the choreographer
should choose which option to use.
Q2: How is the intercalation between technological re-
sources and concepts of dance composition?
It was found that the most practiced form of intercalation
between technological resources and specific concepts of
dance creation occurred through movement notation sys-
tems. These tools aimed to understand how dance should be
performed converting notation scores to allow choreogra-
phers to visualize a graphic animation of their ideas. A nota-
tion consists of characters, signs, or registers that, connected
together, create new forms with different meanings. The
most common movement notations today are the systems
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
created by Laban (Labanotation), Benesh and Eshkol-Wach-
man (Dania et al. 2015). T19 exclusively used Labanotation
while T40 also included Eshkol-Wachman Movement Nota-
tion. T5 was developed with Benesh Notation. It is notewor-
thy that T6 proposed to use Peter Rajka Symbolic Notation
developed exclusively for the tool, which would make the
use of such technology even more complex and with the re-
quirement to study a non-popular notation.
Ribeiro et al. (2017) contrasts the music universe and its
globally readable scores with the universe of dance and the
lack of a notation widely known from the latter. Carlson et
al. (2015) also highlights the challenge of developing an in-
ternational standard and discussions about the creation of
LabanXML or, more broadly, DanceXML, which would
open opportunities for creating diverse tools that could share
resources with each other if they adopted a convention on
how to represent dance digitally.
Another form of intercalation is using libraries of move-
ments or known dance steps that are made available to the
user to compose a choreographic sequence from; however,
this strategy is limited to the application steps database.
The dance characteristic in the technologies proved to be
crucial only in the T5 tool that used Benesh, a notation fo-
cused only on classical ballet, and it was also noted to be
influential in the tools that used the movement interaction
level as “steps”, since a specific database is required and
therefore can be chosen to restrict to one dance style only.
Q3: Are usability concepts applied to technological so-
lutions to support choreographers?
The studies did not focus on usability, by not performing
enough research with end users in the requirement gathering
process. The main trend in the studies was to focus on the
quality and methods of movement manipulation, be it
through body parts or existing steps, and on the form of vis-
ualization. Still, it notes that of the 18 selected technologies,
eight explicitly pointed out some basic type of end-user in-
volvement during the tool development or experimentation
process, even though such engagement was not prioritized in
the definition of the functional requirements.
T6 had an interdisciplinary development team that in-
cluded choreographers, the number of members and their ex-
perience was not mentioned.
T21 involved five ballet teachers with experience rang-
ing from 7 to 25 years just in the process of evaluating some
of the automatically generated products. The authors were
able to extract some considerations around the evaluation
and interview with teachers, such as suggestions for expand-
ing the step database, improving the algorithm responsible
for transitions and the ability to generate memorable content.
It is not clear whether these teachers were involved in the
creation of the tool.
T22 also tested the tool with groups of dancers/choreog-
raphers and dance critics, storing it through video and col-
lecting data through questionnaires to identify improve-
ments, priorities, and limitations, with the main challenges
found associated with 3D cameras/sensors having limita-
tions in the area they can cover and the infrastructure to
transfer the captured content must be of high quality to guar-
antee quality of processing and visualization of graphic ani-
T24 conducted two experiments with undergraduate and
majored students trained in dance in addition to filling out a
questionnaire. They concluded that the tool is useful for stu-
dents to discover new movements and choreographic pro-
cesses and graduates can be aided by the tool, but it requires
them to use their practical expertise to improve the final ar-
tistic production.
T26 carried out an ethnographic study with focus groups
and interviews using the tool. A choreographer recorded a
set of dancers and later used the tool to enter comments. De-
spite being well evaluated, it was possible to extract sugges-
tions for improvements to the system.
T27 and T32 mentioned the presence of a choreographer
in the research team, but it was not explicit in which stages
of the research, in addition to the use of the tool at the end,
the choreographer's contribution was made.
Finally, T52 conducted a form with 14 volunteers who
experienced the tool's output, however, none were described
as being dancer or choreographer.
Although some end-user interventions were mentioned
above as part of some stage of the papers, only one research
(Felice et al. 2016) among all the articles investigated, pre-
sented a conceptual mapping work of a technology aimed at
digitally assisting choreographers, based on interviews with
potential end users.
Overall, it was possible to observe that the main role of
technology has been the simulation of movement through
animations, with the conversion of motion notation systems
being the most characteristic intercalation between computer
science and the arts of choreographic composition. Figure 5
presents a conceptual map based on all the technologies
listed during the research process summarizing and associat-
ing it with interaction and operation output (refer to Figure
4 to associate the technology index with its name and refer-
Figure 5. Research methodology used in systematic mapping. Source:
The authors.
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
4 Conclusions
This study conducted a systematic mapping of technologies
used as support for choreographic composition over time.
The research process started from a broad search that in-
cluded 662 articles, which were reduced to 65 for a detailed
analysis of their contents, resulting in the identification of 56
different technologies, of which 18 were selected, as sum-
marized in Figure 3.
Movement simulation through animation was the main
application of technology in the analyzed tools and conver-
sion of notation systems was the most unique characteristic
of the interdisciplinarity between these two research fields.
This paper contributes mainly by identifying and classi-
fying integration strategies of technology and dance compo-
sition in developing tools for support purposes. It chronolog-
ically analyzed different types of software and their modus
operandi, showing a lack of any sort of usability techniques
in the development process of such applications. The find-
ings imply that academic research must increase the involve-
ment of artists in the early stages of modeling technology
solutions to understand the practical demands of choreogra-
phers and aspiring dancers.
The research restriction to focus only on technologies
that act as a support tool by sharing the compositional effort
is a limitation of this study, since it reduces the number of
studies selected.
Future investigations might include other ways of using
technology in dance creation such as using computer vision
and motion capture, virtual and augmented reality, machine
learning to generate suggestions as the choreographer cre-
ates, web-connected applications to allow geographically
distant cooperative work and internet of things with the de-
velopment of specific hardware. Additionally, there is op-
portunities for a philosophical discussion over the limits of
technology intervention/contribution, in order to do not cross
the tenue line that divides art and a mere technological prod-
uct without artistic value.
Ahn, H., Kim, J., Kim, K., & Oh, S. (2020). Generative
autoregressive networks for 3d dancing move synthesis
from music. IEEE Robot. Autom. Lett., 5, 2: 35013508.
Alaoui, S. F., Carlson, K., & Schiphorst, T. (2014).
Choreography as mediated through compositional tools for
movement: constructing a historical perspective. In ACM
Int. Conf. Proceeding Ser., pages 16.
Alemi, O. (2017). GrooveNet : real-time music-driven dance
movement generation using artificial neural networks.
Sigkdd-W, July, page 6.
Aristidou, A., Stavrakis, E., Papaefthimiou, M.,
Papagiannakis, G., & Chrysanthou, Y. (2018). Style-based
motion analysis for dance composition. Vis. Comput., 34,
12: 17251737.
Bermudez, B., & Ziegler, C. (2014). Pre-choreographic
movement kit. In ACM Int. Conf. Proceeding Ser., pages
Bradford, J. H., & Côté-Laurence, P. (1995). An application
of artificial intelligence to the choreography of dance.
Comput. Hum., 29, 4: 233240.
Brand, M., & Hertzmann, A. (2000). Style machines. In
Proc. ACM SIGGRAPH Conf. Comput. Graph., pages
Cabral, D., Fernandes, C., Carvalho, U., Correia, N., Silva,
J., & Valente, J. (2011). Multimodal video annotation for
contemporary dance creation. In Conf. Hum. Factors
Comput. Syst., pages 22932298.
Cambridge Dictionary. SUPPORT | Significado, definição
em Dicionário Inglês.
ort (accessed Jun. 28, 2021).
Calvert, T., Bruderlin, A., Dill, J., Schiphorst, T., &
Welman, C. (1993). Desktop animation of multiple human
figures. IEEE Comput. Graph. Appl., 13, 3: 1826.
Calvert, T. W., Bruderlin, A., Mah, S., Schiphorst, T., &
Welman, C. (1993). The evolution of an interface for
choreographers. In Conf. Hum. Factors Comput. Syst. -
Proc., 5, pages 115122.
Calvert, T. W., Welman, C., Gaudet, S., Schiphorst, T., &
Lee, C. (1991). Composition of multiple figure sequences
for dance and animation. Vis. Comput., 7, 23: 114121.
Calvert, T., Wilke, L., Ryman, R., & Fox, I. (2005).
Applications of computers to dance. IEEE Comput. Graph.
Appl., 25, 2: 612.
Camurri, A., Hashimoto, S., Ricchetti, M., Ricci, A., Suzuki,
K., Trocca, R., & Volpe, G. (2000). Eyesweb: toward
gesture and affect recognition in interactive dance and
music systems. Comput. Music J., 24, 1: 5769.
Carlson, G., Lundén, P., Lundin, M., Nyman, G., Rajka, P.,
& Ungvary, T. (1992). How to build a multimedial
communication/creation system for music and human
motion. Multimedia, 153169.
Carlson, K., Schiphorst, T., & Pasquier, P. (2011). Scuddle:
generating movement catalysts for computer-aided
choreography. In Proc. 2nd Int. Conf. Comput. Creat.
ICCC 2011, pages 123128.
Carlson, K., Tsang, H. H., Phillips, J., Schiphorst, & T.,
Calvert, T. (2015). Sketching movement: designing
creativity tools for in-situ, whole-body authorship. In ACM
Int. Conf. Proceeding Ser., pages 6875.
Carroll, E. A., Lottridge, D., Latulipe, C., Singh, V., &
Word, M. (2012). Bodies in critique: a technological
intervention in the dance production process. In Proc.
ACM Conf. Comput. Support. Coop. Work. CSCW, pages
Corness, G., & Schiphorst, T. (2013). Performing with a
system’s intention: embodied cues in performer-system
interaction. In Proceedings of the 9th ACM Conference on
Creativity & Cognition, pages 156164.
Crnkovic-friis, L., & Crnkovic-friis, L. (2015). Generative
choreography using deep learning long short-term
memory. In 7th Int. Conf. Comput. Creat. ICCC2016,
pages 16.
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
Dania, A., Koutsouba, M., & Tyrovola, V. (2015). The
ability of using symbols and its contribution to dance
learning: the Laban Notation System. Choros International
Dance Journal, [s. l.], 3749.
Dong, R., Cai, D., & Asai, N. (2017). Nonlinear dance
motion analysis and motion editing using Hilbert-Huang
transform. In ACM Int. Conf. Proceeding Ser., Part F1286.
Du, X., Ma, H., & Wang, H. (2017). A knowledge-based
intelligent system for distributed and collaborative
choreography. In International Conference on Intelligent
Robotics and Applications. Springer, Cham, pages 617
Eiben, A. E., & Smith, J. E. (2015). Creating choreography
with interactive evolutionary algorithms. In Introduction
to Evolutionary Computing. Natural Computing Series.
Springer, Berlin, Heidelberg, pages 215222.
Felice, M. C., Alaoui, S. F., & MacKay, W. E. (2016). How
do choreographers craft dance? Designing for a
choreographer-technology partnership. In ACM Int. Conf.
Proceeding Ser., 05-06-July.
Fernandes, C. (2013). The TKB project: creative
technologies for performance composition, analysis and
documentation. Lect. Notes Comput. Sci. (including
Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics), 7990 LNCS, 2010: 205217.
Hsieh, C. M., & Luciani, A. (2005). Generating dance verbs
and assisting computer choreography. In Proc. 13th ACM
Int. Conf. Multimedia, MM 2005, pages 774782.
Jacob, M., Zook, A., & Magerko, B. (2013). Viewpoints AI:
procedurally representing and reasoning about gestures. In
DiGRA 2013 - Proc. 2013 DiGRA Int. Conf. DeFragging
Jadhav, S., & Pawar, J. (2013). Art to SMart : automation for
BharataNatyam choreography. In Proceedings of the 19th
International Conference on Management of Data, pages
Kar, R., Konar, A., & Chakraborty, A. (2015). Dance
composition using microsoft Kinect. Lect. Notes Comput.
Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics), 9030: 2034.
Kohno, Y., Soga, A., & Shiba, M. (2010). A system for
motion synthesis of human body parts using a touch panel.
In Proc. ACM SIGGRAPH Conf. Virtual-Reality Contin.
Its Appl. to Ind., pages 145146.
Konar, A., & Saha, S. (2018). Differential evolution based
dance composition. Stud. Comput. Intell., 724: 225241.
Krylov, D. I., & Samsonovich, A. V. (2019). Designing an
emotionally-intelligent assistant of a virtual dance creator.
Biologically Inspired Cognitive Architectures Meeting,
Springer, Cham, pages 197-202.
Lapointe, F. J. (2005). Choreogenetics: the generation of
choreographic variants through genetic mutations and
selection. In Proc. 2005 Work. Genet. Evol. Comput.,
pages 366369.
Lee, J., Kim, S., & Lee, K. (2018). Listen to dance: music-
driven choreography generation using autoregressive
encoder-decoder network. arXiv preprint
Lee, H. Y., Yang, X., Liu, M. Y., Wang, T. C., Lu, Y. D.,
Yang, M. H., Kautz, J. (2019). Dancing to music. Adv.
Neural Inf. Process. Syst., 32, November.
Lobo, L., & Navas, C. (2019). Arte da composição: teatro do
movimento. LGE Editora.
Mironcika, S., Pek, J., Franse, J., & Shu, Y. (2016). Whoosh
gloves: interactive tool to form a dialog between dancer
and choreographer. In TEI 2016 - Proc. 10th Anniv. Conf.
Tangible Embed. Embodied Interact., pages 729732.
Nakazawa, M., & Paezold-Ruehl, A. (2009). DANCING,
dance and choreography; an intelligent nondeterministic
generator. In Proc. Richard Tapia Celebr. Divers. Comput.
Conf. 2009 Intellect, Initiat. Insight, Innov., pages 3034.
Parsifal. (2021). (accessed Mar. 10, 2021).
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008).
Systematic mapping studies in software engineering. In
12th Int. Conf. Eval. Assess. Softw. Eng. EASE 2008.
Ribeiro, C., dos Anjos, R. K., & Fernandes, C. (2017).
Capturing and documenting creative processes in
contemporary dance. ACM Int. Conf. Proceeding Ser., vol.
Part F1291.
Rossignac, J., Luffel, M., & Vinacua, À. (2012). SAMBA:
steadied choreographies. Computational Aesthetics in
Graphics, Visualization, and Imaging, 19.
Sagasti, F. (2019). Information technology and the arts: the
evolution of computer choreography during the last half
century. Dance Chronicle, 42, 1:152.
Schiphorst, T., Calvert, T., Lee, C., Welman, C., & Gaudet,
S. (1990). Tools for interaction with the creative process of
composition. In Conf. Hum. Factors Comput. Syst. - Proc.,
pages 167174.
Singh, V., Latulipe, C., Carroll, E., & Lottridge, D. (2011).
The choreographer’s notebook: a video annotation system
for dancers and choreographers. In Proceedings of the 8th
ACM Conference on Creativity and Cognition, pages 197-
Shapiro, A., Cao, Y., & Faloutsos, P. (2006). Style
components. In Proc. - Graph. Interface, pages 3339.
Sheppard, R. M., Kamali, M., Rivas, R., Tamai, M., Yang,
Z., Wu, W., Nahrstedt, K. (2008). Advancing interactive
collaborative mediums through tele-immersive dance
(TED): a symbiotic creativity and design environment for
art and computer science. In MM’08 - Proc. 2008 ACM Int.
Conf. Multimedia, with co-located Symp. Work., pages
Soga, A. (2019). Creation and live performance of dance and
music based on a body-part motion synthesis system. In
17th ACM SIGGRAPH Int. Conf. Virtual-Reality Contin.
its Appl. Ind., pages 34.
Soga, A., Shiba, M., & Salz, J. (2009a). Choreography
composition and live performance on a Noh stage. In 8th
Int. Conf. Virtual Real. Contin. its Appl. Ind., 1, 212, pages
Systematic mapping of technologies for supporting choreographic composition Dornelas et al. 2022
Soga, A., Endo, M., & Yasuda, T. (2001). Motion
description and composing system for classic ballet
animation on the web. In Proc. - IEEE Int. Work. Robot
Hum. Interact. Commun., pages 134139.
Soga, A., Umino, B., & Hirayama, M. (2009b). Automatic
composition for contemporary dance using 3D motion
clips: experiment on dance training and system evaluation.
In Int. Conf. CyberWorlds, CW ’09, pages 171176.
Soga, A., Umino, B., & Hirayama, M. (2020). Experimental
creation of dance by professional choreographers using a
body-part motion synthesis system. In Proc. Int. Conf.
Cyberworlds, CW 2020, pages 117120.
Soga, A., Umino, B., Yasuda, T., & Yokoi, S. (2006a).
Automatic composition and simulation system for ballet
sequences using 3D motion archive. In 2006 Int. Conf.
Cyberworlds, CW’06, pages 4349.
Soga, A., Umino, B., Yasuda, T., & Yokoi, S. (2006b).
Web3D dance composer: automatic composition of ballet
sequences. In ACM SIGGRAPH 2006 Res. Posters,
SIGGRAPH 2006, page 3.
Soga, A., Umino, B., Yasuda, T., & Yokoi, S. (2007).
Automatic composition and simulation system for ballet
sequences. Vis. Comput., 23, 5: 309316.
Soga, A., Yazaki, Y., Umino, B., & Hirayama, M. (2016).
Body-part motion synthesis system for contemporary
dance creation. In SIGGRAPH 2016 - ACM SIGGRAPH
2016 Posters, pages 12.
Sun, G., Wong, Y., Cheng, Z., Kankanhalli, M. S., Geng, W.,
& Li, X. (2021). DeepDance: music-to-dance motion
choreography with adversarial learning. IEEE Trans.
Multimed., 23: 497509.
Tamai, M., Wu, W., Nahrstedt, K., & Yasumoto, K. (2008).
A view control interface for 3D Tele-immersive
environments. In 2008 IEEE International Conference on
Multimedia and Expo. IEEE, pages 1101-1104.
Tang, T., Jia, J., & Mao, H. (2018). Dance with melody: an
LSTM-autoencoder approach to music-oriented dance
synthesis. In MM 2018 - Proc. 2018 ACM Multimed. Conf.,
pages 15981606.
Tang, T., Mao, H., & Jia, J. (2018). Anidance: real-time
dance motion synthesize to the song. In MM 2018 - Proc.
2018 ACM Multimed. Conf., pages 12371239.
Umino, B., Soga, A., & Hirayama, M. (2014). Feasibility
study for contemporary dance e-learning: an interactive
creation support system using 3D motion data. In Proc. -
2014 Int. Conf. Cyberworlds, CW 2014, pages 7176.
Venture, G., Yabuki, T., Kinase, Y., Berthoz, A., & Abe, N.
(2016). MovEngine developing a movement language
for 3D visualization and composition of dance. 111: 91
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell,
B., & Wesslén, A. (2012). Experimentation in Software
Engineering. Springer Berlin, Heidelberg.
Xue, L. (2020). Multimedia system and information
modelling system for computer aided red dance creation
with virtual reality. In Proc. 3rd Int. Conf. Intell. Sustain.
Syst. ICISS 2020, pages 146149.
Yalta, N., Watanabe, S., Nakadai, K., & Ogata, T. (2019).
Weakly-supervised deep recurrent neural networks for
basic dance step generation. In 2019 International Joint
Conference on Neural Networks (IJCNN), IEEE, pages 1
Yazaki, Y., Soga, A., Umino, B., & Hirayama, M. (2016).
Automatic composition by body-part motion synthesis for
supporting dance creation. In Proc. - 2015 Int. Conf.
Cyberworlds, CW 2015, pages 200203.
Yu, T., & Johnson, P. (2003). Tour jeté, pirouette: dance
choreographing by computers. Lect. Notes Comput. Sci.
(including Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics), 2723, pages 156157.
Zaman, L., Sumpeno, S., Hariadi, M., Kristian, Y., Setyati,
E., & Kondo, K. (2020). Modeling basic movements of
Indonesian traditional dance using generative long short-
term memory network. IAENG Int. J. Comput. Sci., 47, 2:
Zhang, S. (2020). Recent advances in the application of deep
learning to choreography. In Proc. 2020 Int. Conf. Comput.
Data Sci. CDS 2020, pages 8891.
Zhou, L. (2014). Image mosaic technology of dance
movements based on digital multimedia and Matlab
implementation. Appl. Mech. Mater., 644650: 4187
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Synthesizing human motions from existing motion capture data is the approach of choice in most applications requiring high- quality visual results. Usually to synthesize motion, short motion segments are concatenated into longer sequences by finding transitions at points where character poses are similar. If similarity is only a measure of posture correlation, without consideration for the stylistic variations of movement, the resulting motion might have unnatural discontinuities. Particularly prone to this problem are highly stylized motions, such as dance performances. This work presents a motion analysis framework, based on Laban Movement Analysis, that also accounts for stylistic variations of the movement. Implemented in the context of Motion Graphs, it is used to eliminate potentially problematic transitions and synthesize style-coherent animation, without requiring prior labeling of the data. The effectiveness of our method is demonstrated by synthesizing contemporary dance performances that include a variety of different emotional states. The algorithm is able to compose highly stylized motions that are reminiscent to dancing scenarios using only plausible movements from existing clips.
Conference Paper
Full-text available
Human motions (especially dance motions) are very noisy, and it is hard to analyze and edit the motions. To resolve this problem, we propose a new method to decompose and modify the motions using the Hilbert-Huang transform (HHT). First, HHT decomposes a chromatic signal into "monochromatic" signals that are the so-called Intrinsic Mode Functions (IMFs) using an Empirical Mode Decomposition (EMD) [6]. After applying the Hilbert Transform to each IMF, the instantaneous frequencies of the "monochromatic" signals can be obtained. The HHT has the advantage to analyze non-stationary and nonlinear signals such as human-joint-motions over FFT or Wavelet transform. In the present paper, we propose a new framework to analyze and extract some new features from a famous Japanese threesome pop singer group called "Perfume", and compare it with Waltz and Salsa dance. Using the EMD, their dance motions can be decomposed into motion (choreographic) primitives or IMFs. Therefore we can scale, combine, subtract, exchange, and modify those IMFs, and can blend them into new dance motions self-consistently. Our analysis and framework can lead to a motion editing and blending method to create a new dance motion from different dance motions.
The creation of improvised dancing choreographies is an important research field of cross-modal analysis. A key point of this task is how to effectively create and correlate music and dance with a probabilistic one-to-many mapping, which is essential to create realistic dances of various genres. To address this issue, we propose a GAN-based cross-modal association framework, DeepDance, which correlates two different modalities (dance motion and music) together, aiming at creating the desired dance sequence in terms of the input music. Its generator is to predictively produce the dance movements best-fit to current music piece by learning from examples. In another hand, its discriminator acts as an external evaluation from the audience and judges the whole performance. The generated dance movements and the corresponding input music are considered to be well-matched if the discriminator cannot distinguish the generated movements from the training samples according to the estimated probability. By adding motion consistency constraints in our loss function, the proposed framework is able to create long realistic dance sequences. To address the difficulty of expensive and inefficient data collection, we propose an effective approach to create a large-scale dataset, YouTube-Dance3D, from open data source. Extensive experiments on currently available music-dance datasets and our YouTube-Dance3D dataset demonstrate that our approach effectively captures the correlation between music and dance and can be used to choreograph appropriate dance sequences.
This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 1,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music. A user study is also performed to evaluate the performance of the proposed method.
This article explores the history of the relations between computer science, information technology, and the art of dance. In the early years of computer choreography, scientists envisaged the development of visual displays and software tools to help in choreographic design. They used random number generators to create a variety of spatial displacements and body movements for the dancers—work that suggested that computer programs could be customized to suit the preferences of the individual choreographer. Such projections fell woefully short of what a large number of choreographers, computer scientists, digital artists, and professionals from different fields eventually achieved during the last half-century, not to mention what we might expect in the future. The convergence of dance creation and performance with advances in information science and technology constitutes a privileged ground on which to explore deep philosophical implications of our embodied mind.
Conference Paper
As a knowledge-intensive process, choreography is increasingly done in a collaborative and distributed environment. However, current research on digital choreography is mainly focused on developing digital tools to facilitate the design of movements, spaces, effects, etc. while little work has been done on capturing and supplying knowledge to support decision-making in particular when multiple experts are involved in the choreography process. In this research, a knowledge-based intelligent system is proposed to effectively capture useful knowledge from a complex choreography process and to provide intelligent support to various experts in a distributed environment. Specifically, the system framework of a distributed and collaborative choreography scheme enabled by advanced computing technologies is proposed. A knowledge representation model is developed based on design rationale to represent both procedural and causal knowledge. A prototype system is also designed in this work and its key technologies are developed. A literature search has shown that little work has been found on knowledge-based intelligent support for choreographers. A preliminary evaluation of the methods in a real-world example shows that they have a range of advantages and more importantly this works opens up opportunities for this new area of work.