Democratizing Bharatnatyam : Investigating the Interplay Between Dance and AI

DEMOCRATIZING BHARATANATYAM

Investigating the Interplay between Dance and AI

Yukti Arora

DEMOCRATIZING BHARATANATYAM

DEMOCRATIZING BHARATANATYAM

Investigating the Interplay between Dance and AI

Yukti Arora

STATEMENT OF THESIS

Investigating the interplay between dance and AI, through the lens of a 2000 year old dance form.

Bharatnatyam, an ancient Indian classical dance form with a history spanning over two millennia, holds a revered position in India’s cultural heritage. Its unique convergence of pure dance (nritta), expressive dance (nritya), and drama (natya) provides a rich and diverse palette for storytelling and artistic expression, making it a cornerstone of Indian tradition and identity.

Bharatnatyam has its origins in Natya Shastra, a 2500 year old treatise on dramatics, which gives a very precise and highly developed codifcation of dance, music and theater. This codifcation means that there are specifc patterns in each the structure and composition of Bharatnatyam performances. The gestures (mudras) and postures serve as building blocks for dance units (adavus) that further serve as building blocks for sequences (jathis and korvais).

This codifcation made me curious to explore the interplay between this dance form and AI. I saw an opportunity to reevaluate how this traditional art form can be made more accessible using AI to the current generation of dancers and stay relevant in a tech forward future. Under this umbrella theme, my vision is to create a series of products and services that democratize the knowledge of Bharatnatyam, making it accessible to the current generation of dancers by meeting them where they are.

With this project, I am investigating the interplay between dance and AI, through the lens of Bharatnatyam.

Specifcally, I am looking at how design can democratize choreography, aid in ‘practice time’ of dancers and can responsibly aid in the preservation and evolution of this 2000 year old dance form, in this AI powered future where I see AI as part of our everyday. I am particularly interested in the stakeholders of Bharatnatyam dancers who have migrated away from their dance school and are no longer in close physical proximity of their Gurus and teachers and their challenges around craving to actively learn, improve and nurture their craft.

This topic is important to me because for me Bharatnatyam is my spatial and poetic language of self expression. It not only flls me with joy, but also helps me connect with my creative and intuitive side and appreciate subtleties of relationships, everyday life and nature. I’ve been a student of this dance since I was 4 years old. Through this project, I aim to give back to the art form that has brought me such joy, personal growth, and understanding.

Furthermore, I believe that art forms like Bharatanatyam possess the power to unite people and foster deeper connections. Bharatnatyam brings people together to appreciate, understand, critique and refect on the intimate anthropological subjects from our past and present, nuances of our social structures, nature and the world. Dance forms such as Bharatnatyam, often don’t evolve at the same pace the rest of the world in this tech age. As technology continues to shape and transform our lives, it becomes imperative that we assume responsibility for envisioning and designing a future for Bharatanatyam that honors its legact yet adapts to the changing times.

APPROACH

To delve into the complex interplay between dance and AI within the context of Bharatanatyam, I have employ a diverse array of design methods to investigate, ideate, and innovate solutions that cater to the needs of the current generation of dancers. My approach encompasses 3 phases:

Frame & Learn

This phase involved primary, secondary & participatory research which was followed by synthesizing the learnings and framing the identifed problems and challenges.

Thorough research was conducted from several secondary sources that included books on Indian classical dance forms and AI as well as scholarly articles on AI research in dance. This was done in conjunction to interviews with subject matter expertsBharatanatyam dancers, instructors, and AI experts. The interviews had a signifcant impact on my view of the topic, especially about the idea of inclusion - how Bharatanatyam has traditionally been reserved for the elites - and the helped me converge my topic around democratization of Bharatanatyam.

This was followed by multiple co-creation workshops conducted with Bharatnatyam dancers and performers with the goal of understanding their motivations, dreams and aspirations as dancers, their choreographic process and gain insights about their practice time. The workshops also helped get feedback on early prototypes and ideas.

The insights gathered from the research and activities helped me frame the problem around 3 key perspectives - Access to Knowledge, Enhance Practice time and Aid Choreography.

Prototype & Co-design

This phase involved ideation, co-designing and iterating on the ideas. I explored these ideas from the perspective of interaction design, service design, product design and public experiences.

As I prototyped my ideas, I took them back to my subject matter experts for advice and feedback. This co-designing process allowed for further refnement of these ideas.

Make & Test

The fnal phase was to develop these prototypes into coherent products. As I developed these fnalized products, I tested them with my users to ensure I was solving the problems I set out to solve. This was a critical step of the design process as it validated my hypotheses.

I have created a suite of design oferings - ranging from digital products that that aid in asynchronous learning and practice, physical oferings that aid in flling the gaps of knowledge, and experiences that make the language of Bharatnatyam accessible to the audience and makes the dancers question what is authentic and relevant.

Alta.AI an AI practice time companion for Bharatnatyam dancers, that enhances practice time by providing real-time voice & visual feedback using NLP and computer vision. It provides feedback on posture, timing helping a dancer asses where they are as compared to benchmark Bharatnatyam dancers.

Alta. AI Pro is a service whic not only asseses the dancers but over time provides actionable feedback for performers to work on. It list steps and suggests guided plans to improve on the areas

Natya community is an online practice community, where dancers can join in live practice sessions and meetups.

Thalam is a learning aid for dancers who want to learn how to count musical beats for their dance steps. It acts as a visual feedback sytem that help them learn the complex rhythmic musical beats.

GLOSSARY

Abhinaya

Abhinaya refers to the art of expressing emotions and conveying meaning through facial expressions, hand gestures (mudras), body language, and eye movements.

Adavu (Tamil)

Adavu is a term used in the classical Indian dance form of Bharatanatyam to refer to the basic rhythmic units or steps. Adavus are the building blocks of this danceform. These rhythmic units are combined in various ways to create complex footwork patterns and sequences that are an integral part of this dance style. There are nine basic adavus in Bharatanatyam, each with its own specifc movement and rhythm. They are: Ardhamandala, Samapada Adavu, Tattu, Thaka, Thaiyar, Kaakku, Muzhukai, Natta Adavu and Mandi.

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning frameworks.Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superfcially authentic to human observers, having many realistic characteristics.

Jathi (Tamil)

Jathi is a rhythmic pattern or a unit of time that is used in the Nritta (pure dance) portion of this classical Indian dance form. It is typically created by using combinations of hand gestures (mudras), footwork, and body movements, and is often accompanied by Solkattu, which is a vocalized rhythmic syllable.. A sequence of Adavus form a Jathi.

Korvai (Tamil)

A specifc type of rhythmic structure used as the climax or conclusion of a rhythmic pattern within a dance sequence.

Laya (Sanskrit)

Laya, or “tempo”, can be explained as the distance between two beats. The lesser the distance, the faster the tempo of the music, and vice versa.

LSTM (Long Short-Term Memory)

A recurrent neural network (RNN) architecture widely used in Deep Learning. It excels at capturing long-term dependencies, making it ideal for sequence prediction tasks.

Mudra (Sanskrit)

A mudra is a symbolic gesture or pose in Indian classical dance. While some mudras involve the entire body, most are performed with the hands and fngers.

Nritta (Sanskrit)

Nritta, or “pure dance,” is the expression of rhythm via pure bodily movement. This dancing performance places less emphasis on facial expressions and footwork is given priority.

Nritya (Sanskrit)

Nritya, or “expressive dance,” is the combination with pure dance (i.e. Nritta) with expressions (i.e. Abhinaya).

Rasa (Sanskrit)

Rasa translates to “juice,” “essence,” or “favor.” In the context of Indian classical dance and music, Rasa refers to the emotional favors or aesthetics experienced by both the performer and the audience during a performance. It is a central concept in the Natya Shastra, an ancient Indian treatise on the performing arts.

Each Taalam consists of a specifc number of beats arranged in a cyclic pattern, creating a rhythmic structure that guides the dancer’s movements and expressions throughout the performance.

Recurrent neural networks (RNNs)

A form of neural network better suited to analyzing temporal and sequential data, such as text or videos.

Taalam (Sanskrit)

Taalam, also known as Tala or Tal, is an essential component of Indian classical dance and music. It serves as the rhythmic framework that governs the execution of dance movements and gestures. In the context of Bharatanatyam, Taalam provides a rhythmic foundation for dancers to synchronize their footwork (nritta), body postures (sthiti), and hand gestures (mudras) with the musical accompaniment.

Each Taalam consists of a specifc number of beats arranged in a cyclic pattern, creating a rhythmic structure that guides the dancer’s movements and expressions throughout the performance.

RESEARCH & METHODOLOGY

Exploring the landscape of Bharatnatyam and Artifcial Intelligence

Through a combination of primary and secondary research methods, including interviews with subject matter experts in both Bharatnatyam and AI, as well as a review of scholarly articles and books, I wanted to achieve a comprehensive understanding of the current landscape of Bharatnatyam and the challenges faced by people involved in the feld as well as an understanding of AI capabilities and how it can aid in the learning, practice and evolution of Bharatanatyam.

The primary research involved speaking with two groups of subject matter experts: Bharatnatyam dancers, performers, and teachers, as well as AI experts. These interviews provided valuable insights into the history of Bharatnatyam, the challenges faced by the Bharatnatyam community, such as access to knowledge and resources, and the culture of instant gratifcation, where Bharatnatyam dancers want to continue to learn and grow as performers while also maintaining their busy lifestyle. The conversations with AI experts focused on the current state of AI research and capabilities in the feld of dance performance as well as ethical perspective on AI and art.

INTERVIEWS WITH SUBJECT MATTER

EXPERTS

In order to gain a deeper understanding of my topic - the interplay between Bharatanatyam and AI - I interviewed a total of 12 subject matter experts (SMEs).

These included 8 experts involved in diferent Indian classical dance forms such as Bharatnatyam, Kuchipudi, Kathak and Odissi. Even though they are diferent dance forms, they have a shared origin and history as well as many similarities. Moreover, a lot of Bharatanatyam dancers are also Kuchipudi or Odissi dancers and there is signifcant overlap in vocabulary, gestures, movements and the stories from Hindu mythology that the dance forms depict. These experts included dancers, performers, choreographers, musicians and teachers, and were chosen based on their knowledge and experience in the area, as well as their ability to provide unique perspectives and insights. They have spent years studying and working in the feld, and had a wealth of knowledge and experience to share.

I also conducted interviews with 4 experts in the feld of AI and design, to learn about the recent advancements in the feld. Specifcally, I wanted to understand the capabilities and technology readiness of modern AI systems as well as pose questions around AI ethics, security, bias and inclusivity. By interviewing the SMEs, I was able to gain a more comprehensive understanding of various aspects of Bharatnatyam and AI. I was also able to posit ways these two felds could intermix, interact, converge or diverge.

INTERVIEW STRUCTURE

The interviews with Bharatanatyam experts were structured in a way that allowed me to understand the participant’s unique experiences and their hopes and dreams as practitioners of the dance form. I began by asking open-ended questions about their unique dance journey that allowed the participants to share their thoughts and experiences freely. Next, I asked more specifc questions about the challenges they face as performers, choreographers or teachers, focusing on areas of interest to my research such as the choreographic process, practice time and knowledge gaps. These questions were designed to elicit specifc information that could be used to support my research.

Finally, I concluded the interviews by asking the participants about the evolution of Bharatanatyam in the future and the role technology and AI could play in that evolution. It helped me not only understand that participant’s perspective on the future of the dance form but also their attitudes (positive and negative) towards emergent technologies.

Similar to the Bharatanatyam experts, my interviews with AI experts began with open-ended questions about their journey, past experiences, how they got involved in the feld and their current areas of interest. These were followed by specifc questions about the ethical implications of AI systems, algorithmic bias and designing AI systems for privacy, transparency and inclusivity.

INTERVIEW FINDINGS

One of my top fndings from Bharatanatyam experts was the problem of access. Historically, the dance form has had an oral tradition passed down from teacher to student over generations. Diferent dance schools have also practiced gatekeeping, discouraging students from sharing the choreography and music on YouTube for example. Moreover, the dance form is over 2000 years old and its vocabulary is primarily in Sanskrit, which is no longer a widely spoken language. The key learnings here were that Bharatanatyam is not very accessible to people that have not grown or trained in this tradition. People from outside the tradition that want to learn or collaborate fnd it difcult to gain understanding and knowledge about the dance form.

Learning about the SMEs unique dance journeys was very helpful. I learnt that they are usually introduced to and learn the dance form under the tutelage of their Guru (at a dance school or at their home). Often, they migrate away from their dance school for higher education or their career and are no longer in physical proximity to their Guru. They miss the presence of their Guru, who actively prompts them where they need to make a correction.

Another important learning was the complexity of the choreographic process. The SMEs shared that choreographing a new performance can take anywhere from 6 to 24 months. For choreographers, the process may start with a story from Hindu mythology they want to depict, a musical composition they want to dance to or a dance routine that they want to fnd the ideal music for. After this, they go through a long process of visualization, adding, editing and deleting. I also learnt that choreographers do not have an accessible glossary of diferent possible movements, hand gestures, expressions, adavus, jathis and korvais, especially when they want to classify based on musical meter or speed.

My top fnding from AI experts was that despite rapid advancements in what AI systems can do, the research into the societal impact of AI is still in its nascent stage and designers should be mindful of second and third-order efects of their creations when imagining AI based experiences.

The Bharatnatyam experts I spoke with consisted of established solo performers such as Vaibhav Arekar and Maya Kulkarni, with experience of choreographing and performing across the world, as well as young dancers such as Amulya Pillai, who after completing their learning repertoire are now venturing into choreography. Some experts such as Sri Thina have hand-on experience as both a performer and a teacher. This diversity allowed me to gain a more comprehensive understanding of the topic and its various aspects.

It was a happy surprise that all the participants aligned on the fundamentals and the codifcation of the dance forms. The primary divergence in views was based on age. Young dancers were very open to cross-cultural infuences, fusion and believed in learning new kinds of dances. There was also more openness to use of technology and were less strict about stylistic norms. The older dancers had a more traditionalist perspective and were more uptight around what is authentic. This highlights the complexity of the topic and the need for a multifaceted approach to understanding it.

The participants confrmed my primary insight about the problem of access and the knowledge gap dancers feel when they want to advance in this feld. I discussed the idea of a searchable database of gestures and movements which had a near universal appeal amongst the SMEs.

I also discussed the idea of a “live - prompter” to aid dancer’s practice time but was challenged by some experts on this idea. They shared that their dancer’s practice time was their sacred place where they liked to feel free to make mistakes. They worried that a “live-prompter” would take away that freedom.

Overall, the interviews had a signifcant impact on my view of the topic. Biggest impact on my perspective was the idea of inclusion - how Bharatanatyam has traditionally been reserved for the elites - and the helped me converge my topic around democratization of Bharatanatyam.

CO-CREATION WORKSHOPS

To futher understand the contemporary context of Bharatanatyam and explore its future, I organized co-creation workshops with Bharatanatyam dancers, performers and teachers. The workshops, titled “Bharatanatyam is Evolving,” were designed to engage with dancers and gain insights into their perspectives on the evolution of the dance form.

The workshops had several goals. Firstly, they aimed to understand the motivations behind the participants’ dance practice. This involved exploring why they chose to pursue Bharatanatyam and what kept them engaged with the dance form. Secondly, the workshops sought to uncover the participants’ dreams and aspirations for their dance practice. This helped us understand what they hoped to achieve through their engagement with Bharatanatyam.

Another important goal of the workshops was to understand the participants’ choreographic process. This involved examining how they approached the creation of new dance pieces and what inspired their choreography. Additionally, the workshops sought to explore the participants’ dance practice time and how they structured their practice routines.

Furthermore, the workshops aimed to identify areas for improvement in the participants’ dance practice and understand their plans for learning and growth. This helped us gain insights into the challenges faced by dancers and how they sought to overcome them.

Finally, the workshops also explored the historical evolution of Bharatanatyam and envisioned its potential future. Through these discussions, we aimed to gain a deeper understanding of the participants’ experiences and perspectives on the dance form and how it could evolve in the future.

The “Bharatanatyam is Evolving” workshops provided valuable insights into the world of Bharatanatyam and the perspectives of dancers who are passionate about the dance form. These insights have contributed signifcantly to our research on the future of dance and AI through the lens of Bharatanatyam.

BOOKS ON INDIAN CLASSICAL DANCE & MUSIC

In addition to interviews with SME experts and co-creation workshops, secondary sources played a crucial role in my understanding of the subject matter. I immersed myself in books about Indian classical dances and music, books about AI ethics and techniques, scholarly articles on the latest developments in application of AI to dance as well as books on design.

P Ramachandrasekhar Dance Gestures (mirror of expressions)

The book explores the diferent gestures in Indian classical dance forms like Bharatanatyam and Kuchipudi. It illustrates how they use gestures to tell stories and express emotions by exploring diferent aspects of dance gestures, including their meaning, usage, and importance in diferent situations. It also discusses how gestures help create rhythm and show emotions, and why they are crucial for conveying the story or theme of a dance performance. My key takeaways were that gestures play a crucial role in Indian classical dance forms, as they help convey emotions and tell stories The meaning and usage of gestures vary depending on the context in which they are used. Importantly, gestures are a crucial piece of the puzzle for choreographing Bharatanatyam performances.

Gayathri Keshavan

Bharata Natyam Adavus

The book provides detailed instructions and illustrations for the various basic steps or units of movement, known as adavus, used in Bharatanatyam. The book covers diferent types of adavus such as thandu, natta, mandi, taiyyaadu and others, along with their variations and combinations. It also includes information on the proper body posture, hand gestures and footwork required to execute these movements accurately.

My key learning was the highly precise and specifc codifcation of Bharatanatyam. The adavus are building blocks that form the smallest indivisible part of a Bharatanatyam dance pattern.

A.D. Madhavan Core of Karnatic Music

The book provides an introduction to the traditional South Indian classical music style known as Carnatic music. The book covers various aspects of this music form including its history, key concepts, and key elements such as ragas (melodic modes) and talas (rhythmic cycles). It also discusses the role of improvisation in Carnatic music and ofers insights into the structure and organization of a typical concert.

I learnt that Carnatic music and Bharatanatyam are closely intertwined as Carnatic music serves as the primary musical accompaniment for Bharatanatyam performances. Understanding of Carnatic music is important for Bharatanatyam dancers to synchronize.

BOOKS & PUBLISHED PAPERS ON AI

Mark Coeckelbergh AI Ethics

The book explores the ethical implications of AI and its impact on society. AI raises important ethical questions about the relationship between humans and machines, and the potential consequences of creating intelligent systems that can learn and act autonomously. The book covers the challenges of ensuring transparency and accountability in AI decision-making, and the need for ethical considerations in the design and development of AI systems.

My understanding was that we need to think about ethical considerations right from the start when designing and developing AI systems, rather than just taking them on at the end.

Henry A. Kissinger, Eric Schmidt, Daniel Huttenlocher

The Age of AI And Our Human Future

The book explores explores the impact of artifcial intelligence on society and humanity. The author argues that AI has the potential to bring about signifcant benefts, but also poses signifcant risks if not managed properly. Ethical considerations must be taken into account in the development and deployment of AI.

Stuart

Russel, Peter Norvig

Artifcial Intelligence: A Modern Approach

A comprehensive introduction to the feld of artifcial intelligence, the book covers a wide range of topics, including machine learning, natural language processing, and robotics. It provides an overview of the history and current state of the feld, as well as discussing the ethical and social implications of AI. My key learning was how Machine Learning can recognize and analyze diferent dance movements or styles. Natural language processing (NLP) could be used to create AI systems that can understand and respond to spoken or written instructions related to dance, such as choreography or criticism.

Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A. Efros

Everybody dance now

The authors use a combination of machine learning techniques, including a generative adversarial network (GAN) and a long short-term memory (LSTM) network, to learn the relationship between music and dance movements from a dataset of human dance performances. They then use this model to generate new dance movements that are synchronized with music and can be performed by virtual characters in real time. The authors demonstrate the efectiveness of their approach through experiments with a variety of virtual characters and diferent types of music. The paper presents a novel method for generating realistic and expressive dance movements for virtual characters.

Kyle McDonald Dance x Machine Learning: First Steps

The paper explores the use of machine learning techniques for generating and recognizing dance movements. The author presents a number of experiments using diferent machine learning algorithms and datasets, including one in which he trains a model to recognize specifc dance postures from video footage. He also discusses the potential applications of his work, such as creating more realistic virtual dancers or helping people learn new dance moves.

The paper provides an overview of the current state of research at the intersection of dance and machine learning, and suggests directions for future work.

Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, TingChun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz

Dancing to Music

The system uses a combination of computer vision and machine learning algorithms to analyze video footage of dancers and music, and generates new dance movements based on the rhythm and style of the music. It also presents the results of experiments using the system, which show that it is able to generate realistic and expressive dance movements in response to diferent types of music. Large amounts of training data are needed to develop accurate machine learning systems for generating dance movements in response to music. Real-time generation of dance movements in response to music is possible with the use of advanced machine learning algorithms.

EARLY PROTOTYPES & IDEATION

Democratizing Bharatnatyam through AI requires a multi-faceted approach that addresses various barriers to access and participation. I explored ideation through 3 key lenses - Access to Knowledge, Enhance Practice time and Aid Choreography.

ACCESS TO KNOWLEDGE

One of the major barriers for people who grew up outside the culture is the language and cultural barrier. Even for Bharatnatyam students, the knowledge is not easily accessible as the books are in Sanskrit, a language no longer openly used. To address this issue, I proposed the idea of “Alta” - a “Noun Project” for Indian classical dance gestures. Users can search for gestures using a keyword and get results and similar gestures and variants. Alta would provide an easily accessible and searchable repository of Bharatnatyam knowledge, including gestures, adavus, jathis, stories, etc. Additionally, it would also take into account the multiple schools of the dance and the variances across them. This would enable users to learn about the diferent aspects of the dance form, understand its history and context, and gain a deeper appreciation for the art form.

ENHANCE PRACTICE TIME

Bharatnatyam students often miss in-person time with their Guru due to busy schedules, resulting in missed opportunities for live prompts to correct posture, timing and expressions. To address this issue, I explored the idea of using AI to enhance practice time. By utilizing AI algorithms, students would be able to receive real-time feedback on their posture, timing, and expressions during practice sessions. This would help bridge the gap caused by the lack of in-person time with the guru and improve the student’s overall practice experience. The AI system could also track the student’s progress over time, providing valuable insights into areas that need improvement and helping them reach their full potential as dancers.

AID CHOREOGRAPHY

Bharatnatyam is highly codifed with codifcation for gestures, poses and movements. To aid choreography and make it more accessible, I proposed the idea of an AI Choreography builder. By using pre-programmed gestures, poses and movements, users would be able to easily create and edit choreography, even if they don’t have a deep understanding of the dance form. This tool would also enable users to experiment with diferent combinations of gestures, poses and movements, making it easier to create new and unique choreographies. The AI Choreography builder would also allow users to share their creations with others, fostering collaboration and innovation within the Bharatnatyam community.

NATYA.AI: BHARATNATYAM

PRACTICE COMPANION

Designing an AI companion for dancers that enhances practice time by providing voice and visual feedback in real time.

For interaction design explorations, I chose to understand how the dancers’ practice time could be enhanced with the help of AI. Understanding how highly defned and specifc the movement patterns are in Bharatnatyam, I was curious about the application of AI for practice and how the users of this very ancient dance form would react to it.

Bharatnatyam dancers have a love-hate relationship with technology. They record themselves, with a phone & tripod, while practicing to visualize their movements in space.

However, recording & reviewing their practice is cumbersome, time consuming, and breaks the fow of practice. Dancers also experience mental fatigue while reviewing their practice videos. They miss the presence of their Guru who would live prompt them if they would make a mistake.

The result of this exploration was Natya.AI - an AI companion for Bharatnatyam dancers that enhances practice time by providing actionable voice and visual feedback in real time using computer vision, NLP and data.

THE PROBLEM

To understand the challenges dancers’ face while practicing I surveyed 311 Bharatnatyam dancers across 2 countries using typeform. The survey results led to 3 major insights:

97% Bharatnatyam dancers surveyed record themselves while practicing

This is not surprising as recording oneself while practicing can be a useful tool for self-evaluation and improvement as well as helps dancers visualize their movements in space.

By watching the recorded videos, dancers can identify areas where they need to improve and work on perfecting their movements and expressions.

Recording oneself while practicing can also help dancers track their progress over time and see how far they have come in their training.

Additionally, it is be helpful for dancers to share their recorded videos with their Guru for feedback and guidance.

Recording & reviewing is cumbersome, time consuming, and breaks the fow of practice.

Firstly, setting up the recording equipment and ensuring that the camera angle and lighting are suitable can take some time and efort. This can be especially challenging for dancers who are not tech-savvy or do not have access to high-quality recording equipment.

Secondly, reviewing the recorded videos can be a time-consuming process, especially if the dancer is practicing for an extended period.

It can be tiring to watch oneself perform repeatedly, and it may be difcult to remain objective and identify areas for improvement. Finally, recording oneself while practicing can break the fow of practice as the dancer may become too focused on the recording process rather than fully immersing themselves in the dance. This can lead to a loss of focus and concentration, which is essential for improving one’s skills in Bharatanatyam.

Dancers

miss the presence of their Guru who would live prompt them during practice.

WHAT IF BHARATNATYAM DANCERS COULD BE

LIVE-PROMPTED MIMICKING THE ‘IN-CLASS’ EXPERIENCE

DEFINING SUCCESS METRICS

Hypotheses are the most important assumptions you want to test to defne the success and feasibility of the product you are testing. The general anatomy of a hypothesis statement is “if we provide [a person] with [address need] we will observe [metric 1, metric 2, metric 3].”

On a FigJam fle, I created a 2x2 matrix of what is a high impact and low impact and what is known/unknown. After plotting these, I identifed the riskiest and most important assumptions that would make or break the product.

To test the assumptions I made as identifed in my ethnographic research and survey of Bharatnatyam dancers, I defned success metrics for Natya.AI.

These are my 3 main hypotheses that I plan to test using qualitative feedback.

If we provide Bharatnatyam dancers with:

1. actionable feedback on posture & timing through body tracking enabled with computer vision, their practice sessions would be enhanced by 50%.

2. voice controls to play/pause/stop their recording, it would reduce the interruptions by 80% making the recording process feel like plug and play.

3. clear video snippets with color-coded marking (red/green) indicating areas of improvement it would reduce time spent reviewing by at least 50%

USER FLOW & SKETCHES

While developing the Natya.AI app, I began by outlining the key features of the app and identifying the minimum viable product (MVP) that would allow me to test my hypotheses and demonstrate how the app works. This involved thinking carefully about what was essential for the app to be useful and efective, and what could be added later on as the app evolved.

Once I had a clear idea of the MVP, I moved on to sketching out the diferent sections of the app. This was an important step in the development process, as it allowed me to think through the various scenarios and modes that the app would need to accommodate.

Sketching also helped me to start thinking about the voice interfaces and interactions that would be central to the app, as well as the overall user experience.

As I continued to develop the app, I found that sketching was an invaluable tool for getting feedback on the usability of the app. While high-fdelity wireframes were helpful in getting feedback on the concept in general, users seemed to feel more comfortable providing feedback on paper sketches. This allowed me to make changes and refnements to the app based on real user feedback, which was essential in ensuring that the app was as efective as possible.

HIGH DEFINITION WIREFRAMES

USABILITY TESTING

During the usability testing of Natya.AI, several key insights emerged that helped to improve the overall user experience. Firstly, I found that using only two colors - red and green - to provide feedback on a dancer’s performance was more efective than using four colors. This is because users had difculty understanding what each color means when there were four options available. By simplifying the color scheme to just two options, the app became easier to use and understand.

Secondly, users expressed a desire for diferent review speeds in the app. Specifcally, they wanted the ability to review their performance at 1x, 0.5x, and 1.5x speed. This would allow them to focus on specifc areas of their performance and get a better sense of how they were doing.

By incorporating these diferent review speeds into the app, dancers could better analyze their movements and make improvements as needed.

Finally, I received feedback that expressions are highly subjective and can vary greatly from one person to another. As a result, the app was modifed to track only posture and timing as metrics for feedback. This allowed the app to provide more accurate and useful feedback to dancers while avoiding the challenges associated with rating expressions.

By listening to user feedback and making changes based on their needs and preferences, Alta.AI was able to better serve its intended audience and help dancers improve their skills and technique.

NATYA.AI AS A SERVICE

Expanding the service ofering of Alta.AI to better serve the users

After gathering feedback from our test users for Natya.AI, we realized that it was an excellent assessment tool for Bharatanatyam dancers. By providing dancers where they stood in comparison to benchmark performers, the app helped them identify areas where they needed to improve specifcally - posture and timing.

However, simply identifying these areas for improvement was not enough. The next step was to defne a learning path for our users to help them achieve their performance goals. To do this, we focused on 2 key areas: actionable feedback and guided learning.

Firstly, we knew that the feedback provided by the app needed to be actionable, it was just a score out of 100. To make the feedback more actionable, we developed a paid version of the app - Natya.AI Pro that would use data and analytics to identify specifc areas where a dancer needed to improve, and then provided targeted feedback and suggestions for how to make those improvements. For example, instead of saying “posture - 87/100”, the app might suggest “move your arms up to 90 degrees to shoulder” and provide video examples of how to perform certain movements correctly.

Secondly, we recognized the importance of providing structured plans to help dancers improve. Natya.AI Guided Learning would help dancers chooses from a wide variety of training plans from expert teachers. By providing dancers with a clear roadmap for improvement, we could help them stay motivated and focused on their goals.

NATYA.AI PRO

Natya.AI Pro is a paid version of the the app that ofers advanced features to help dancers improve their skills and technique. One of the key diferences between Natya.AI and Natya.AI Pro is that the latter provides actionable feedback for each snippet.

For example, the app might suggest that a dancer “bend a little more to the left” or “raise your right arm by 5 degrees more” in order to improve their posture or alignment. By providing this level of detail, Natya.AI Pro can help dancers refne their movements and achieve a higher level of precision and control.

NATYA.AI GUIDED LEARNING

Natya.AI has guided learning plans taught by Bharatnatyam teachers and experts from around the world to help you start practicing, keep practicing and enjoy practice time more and unlock those performance goals.

Our expertise in AI data modeling and training our computer vision technology and our partnerships with some of the masters of Bharatnatyam from Kalakshetra foundation, the most prominent dance academy dedicated to the preservation and growth of Bharatnatyam, helps us launch Natya.AI Guided Learning

Providing Bharatnatyam dancers a convenient way to start practicing, continue their practice and constantly learn and evolve as performers, at their own pace.

NATYA.AI BUSINESS MODEL

Exploring the opportunity size and target audience for a Bharatnatyam practice app.

While AI-powered dance practice apps are still a relatively new concept, recent developments in AI, especially computer vision algorithms make Natya.AI a viable product. I have already discussed the primary value proposition of Natya.AI is in enhancing the practice experience for dancers by ofering customized feedback, tracking progress, and ensuring correct technique.

In this chapter, I will explore the global opportunity and the proposed pricing model for Natya.AI.

TOTAL ADDRESSIBLE MARKET

The market opportunity for Natya.AI exceeds $400 million, driven by the growing demand for AI-powered user experiences across industries.

There are 100,000+ professional dancers in the world and this does not include semi-professional and casual performers. If we include semi pro and casual, this number increases to 20M dancers. Assuming an average revenue for $20 per dancer per year, we arrive at a total addressible market of $400M. If launched today, Natya.AI should be able to grow to a revenue of $50M in 5 years

PRICING MODEL

There are no direct competitors in the space at the moment. However, Natya.AI would compete with traditional dance schools, private tutors as well as generic AI video analysis tools such as OpenText. Natya.AI will be available as a subscription service with a 3-tier pricing model:

1. Basic: The free tier will provide basic features real-time voice and visual feedback on posture and timing. This ofering will also include access to Natya.AI community and events.

2. Pro: The pro subscription will include advanced actionable feedback, practice history and personalized metrics in addition to all the basic features. I believe this will be the top choice for most users.

3. Premium: The premium ofering will have access to Natya.AI guided learning paths and courses from established dancers and teachers.

THE DYSTOPIAN FUTURE OF NATYA.AI

A speculative design exercise where I explore the potential dystopian future of Natya.AI

One of the key learnings from my readings on AI was the continual debate on the ethics of AI and the potential for a general intelligence being developed that could take over human civilization. As a fun futuring exercise, I imagined Natya.AI evolving into an instrument of control over populations. As Natya.AI would become successful and grow in adoption, governments and institutions would begin partnering with Natya.AI to promote cultural education, and the app soon became the go-to platform for dance enthusiasts. With its vast reach and infuence, Natya.AI would emerge as a powerful tool for shaping public opinion and manipulating behavior. However, in the hands of more authoritarian regimes, it would provide access to the app’s vast trove of user data and advanced surveillance capabilities. This would allow governments to monitor their citizens’ activities, detect dissent, and quash any signs of rebellion. I imagined what a newspaper from this dystopian future would look like. This dystopian scenario ofers a cautionary tale on the unchecked proliferation and exploitation of AI.

OFFBEAT

Exploring evolution of Bharatnatyam through collaborative contemporary choreography

Despite its widespread recognition and admiration, Bharatnatyam remains deeply entrenched in conservative values and traditional norms. I organized “Ofbeat,” a public experience designed to challenge these norms and spark a conversation about the evolution and democratization of Bharatnatyam.

The culture surrounding Bharatnatyam is characterized by several aspects that limit its accessibility and adaptability in modern times:

1. Culture of gatekeeping: Bharatnatyam is often guarded by a select group of experts who hold signifcant infuence over the art form’s development and acceptance.

2. Changing gurus or schools: Traditionally, students of Bharatnatyam dedicate themselves to a single guru or school of learning for the entirety of their training. The act of switching gurus is often viewed as unconventional, which discourages dancers from exploring alternative approaches or collaborations.

3. Resistance to contemporary music & stories: There is reluctance among some practitioners to incorporate contemporary music into the dance form. Bharatnatyam performances typically draw inspiration from Hindu mythology and ancient texts. The hesitation to explore contemporary themes and music hinders Bharatnatyam’s evolution.

In light of these challenges, “Ofbeat” was conceptualized to encourage Bharatnatyam dancers to experiment with the art form in a contemporary context, spark curiosity and foster a dialogue about the potential for evolution within Bharatnatyam.

PLANNING & PREPARATION

Venue and logistics

The event was held at Studio C, Gibney 280 Broadway, a well-known and accessible venue for performing arts in New York City. Logistical arrangements included coordinating dates with participants, sending out invitations, and setting up necessary equipment for sound and video recording.

Collaboration with NYC Adavus

“Ofbeat” was organized in collaboration with NYC Adavus, an NYCbased meet-up group for Bharatanatyam dancers aimed at fostering community and practice opportunities. This partnership helped tap into an existing network of passionate dancers and contributed to the event’s visibility and outreach.

Music Selection

Alicia Keys’ “Girl on Fire” was ultimately chosen as the backing track for this event from a shortlist of three potential songs: “Empire State of Mind,” also by Alicia Keys, “Sun Won’t Set” by Anoushka Shankar, and the ultimately selected “Girl on Fire.” The decision to go with “Girl on Fire” was based on its popularity, energetic rhythm, and themes of empowerment, which aligned well with the goals and vision of the “Ofbeat” public experience.

Madlib exercise

To facilitate the creative process and encourage participants to think outside the box when developing their choreographies, a madlib exercise was prepared for each group. The madlib consisted of four prompts to be completed with the frst word that came to mind upon hearing the chosen song, “Girl on Fire”. This served as a starting point for devising the contemporary choreographies showcased during the event.

EXECUTION & PERFORMANCE

Introduction (5 mins)

The event began with an introduction to the concept and purpose of “Ofbeat.” This was followed by the formation of three groups of about 8-10 dancers each. Each group was assigned a 30s section of the song “Girl on Fire”.

Choreogaphing (20 mins)

Each group started out by flling out the madlib and listening to their section of the song. It was interesting to see that the groups immediately started not only brainstorming steps but also group formations.

Group performance (5 mins)

Once each group had fnalized their choreography, we had 2-3 practice performances. As a fnale, all participants came together for a joint performance, showcasing how the traditional Bharatnatyam style could be adapted to modern music while maintaining its essence and beauty.

Refections (15 mins)

After the performances, participants were invited to share their thoughts, impressions, and opinions on the experience. This was aimed to foster the desired conversation about the evolution of Bharatnatyam and the potential for greater inclusivity and relevance in the dance form.

OUTCOMES

Social sharing and community engagement

The event not only brought together Bharatnatyam dancers to collaborate and experiment with the dance form but also fostered a sense of community among participants. Many shared their experiences and thoughts with us after the event.

Collaborative fun and breaking inhibitions

Through the choreography sessions, and fnal performances, participants were encouraged to let go of their inhibitions and fully embrace the process of creating contemporary Bharatnatyam pieces. This open, lighthearted atmosphere allowed dancers to enjoy the experience while challenging conventional norms.

Sparking conversations about Bharatnatyam

“Ofbeat” succeeded in initiating thoughtful discussions surrounding the past, present, and future of Bharatnatyam. {roviding a platform for dancers to explore the traditional art form through a modern lens helped highlight both its potential for evolution and its continued relevance in contemporary times.

Ofbeat Studios

In the future, I imagine a series of third spaces called “Ofbeat Studios” for incubating collaboration with dancers of diferent backgrounds to remix cultures and genres on AI generated live music.

TAALAM

Smart Object to master rhythm with ease and precision

Taalam, also known as Tala or Tal, is an essential component of Indian classical dance and music. It serves as the rhythmic framework that governs the execution of dance movements and gestures.

In the context of Bharatanatyam, Taalam provides a rhythmic foundation for dancers to synchronize their footwork (nritta), body postures (sthiti), and hand gestures (mudras) with the musical accompaniment. Each Taalam consists of a specifc number of beats arranged in a cyclic pattern, creating a rhythmic structure that guides the dancer’s movements and expressions throughout the performance.

The mastery of Taalam is crucial for Bharatanatyam dancers, as it enables them to maintain precision and coordination with the musical accompaniment. However, its abstract nature and complexity can make it challenging for dancers to learn. As rhythm plays a crucial role in Bharatanatyam performances, understanding the Taalam system is vital for any aspiring dancer.

LEARNING THE TAALAM SYSTEM

Learning the Taalam system is hard and takes years of practice

The Taalam system requires a deep understanding of Indian classical music. The ability to recognize, reproduce, and improvise within these rhythmic structures demands extensive training and dedication. Due to the lack of explicit rules or formulas, mastering the Taalam system is often more about developing an intuitive sense of rhythm rather than following a set methodology. This process can be timeconsuming and requires patience and perseverance from the dancer.

Lack of visual cues on what to speak & feedback on what went wrong

Unlike other aspects of Bharatanatyam, such as hand gestures or facial expressions, the Taalam system is primarily auditory. This means that dancers must rely on their sense of hearing to recognize and reproduce rhythms, making it challenging to identify mistakes.

No easy way to learn or practice

Traditionally, the Taalam system has been passed down through oral tradition, with dancers learning directly from their gurus or mentors. This approach can make it difcult for new learners to access quality instruction, especially if they do not have ready access to experienced teachers. Moreover, practicing the Taalam system independently can be challenging due to the lack of resources that provide accurate guidance.

CURRENT WAY OF LEARNING

The current method of learning Taalam system involves noting down pattern notations on paper and reciting syllables while practicing striking by hand. This approach heavily relies on visual aids and physical movements to grasp the concepts. Although this might work for some individuals, my research with Bharatanatyam dancers show revealed that most dancers struggle with this given approach.

Moreover, memorizing pattern notations and reciting syllables is not the most efcient way to internalize the Taalam system. This approach leads to rote memorization rather than a deeper understanding of the rhythmic patterns and their applications.

The current learning method’s reliance on written notations and physical demonstrations also limits the accessibility for those who may not have access to teachers or resources.

Learning the Taalam system is critical for Bharatanatyam dancers as it can create communication barriers when working with musicians and choreographers. Having a strong foundation in the Taalam system is important to able to converse in this common language for efective collaboration.

FORM & MATERIALS EXPLORATION

To help dancers learn the Taalam system, I imagined creating a smart object called Taalam. It would mimic a percussion instrument with a digital touch interfaces that the user can interact with.

Taalam would use multimodal AI observing and communicating with the user and provide visual feedback on:

1. what to speak

2. what hand gestures to do

3. let the learner know when to tap

Once I had a clear idea of the object, I moved on to sketching out diferent form factors.

Sketching also helped me to start thinking about the voice interfaces and interactions, as well as the overall user experience. For each form I explored, I made a pros and cons list to determine the best possible direction to go with.

I explored multiple forms such as a smart surface, raised screen, square interface as well as a portable wrist watch.

The chosen design was a “Tabla” inspired form. Tabla is a percussion instrument used in Indian classical music. This ft the cultural context as well as provided visual cues for the dancers about where to “strike”. The smooth edges also provide a way for users to rest their hand while using the object.

3D DRAWINGS & RENDERINGS

BRANDING & INTERFACE

The branding inspired by engineering, monospace and numeric themes and focused on creating a visual identity that refects precision, functionality and a systematic approach, characterized by the use of clean lines, geometric shapes and a minimal color palette.

Similarly, the interface uses classic red and green along with grey and gold.

FINAL PROTOTYPE

The fnal version of the prototype was created using 3D printing technology. Once completed, it was placed on top of an iPad mini device to act as the digital display for the prototype. This allowed me to test and demonstrate the physical interactions and mechanics of the prototype while also showcasing the digital interface and capabilities that could be integrated into the fnal product. The use of an iPad mini also provided a convenient and portable way to display the digital aspects of the prototype during presentations or demonstrations.

CONCLUSION

Bharatnatyam has evolved over time, adapting to various cultural and societal changes. However, in its current state, Bharatnatyam faces two signifcant challenges. Firstly, there exists a high barrier of entry for dancers to become performers, limiting the number of people who can participate in this art form. Secondly, there is a learning gap preventing performers from becoming choreographers and gurus, thus creating a ceiling that hinders the growth of potential contributors to the feld.

This thesis proposes interventions using AI aimed at addressing these issues by both raising the ceiling and lowering the foor in the world of Bharatnatyam. By doing so, I hope to create an inclusive environment where more individuals can engage with this classical dance form, ultimately leading to its enrichment and expansion.

SOURCES

Indian Classical Dance and Music

Arundale, Rukmini Devi. The Art of Bharatanatyam. New Delhi: Orient Longman, 1980.

Kothari, Sunil. The Language of Gesture in Indian Classical Dance. New Delhi: Abhinav Publications, 1983.

Ramachandrasekhar, P. (2023). Dance Gestures (mirror of expressions). With english translation of Abhinayadarpana. Chennai: Giri Trading Agency Private Limited

Ramanathan, N. Bharatanatyam: A Guide to the Fundamentals. New Delhi: Munshiram Manoharlal Publishers, 1985.

Ramanathan, N. Carnatic Music: A Guide to the Fundamentals. New Delhi: Munshiram Manoharlal Publishers, 1985.

Artifcial Intelligence (AI)

Coeckelbergh, Mark. AI Ethics. Cambridge, MA: MIT Press, 2019.

Kissinger, Henry, Eric Schmidt, and Daniel Huttenlocher. The Age of AI and our human future. New York: Little, Brown and Company, 2021

Russell, Stuart, and Peter Norvig. Artifcial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2010. Design

Design

Archive for Research in Archetypal Symbolism (ARAS). The Book of Symbols. Refections on Archetypal Images. New York: Taschen, 2010.

Jacobs, Jane. The Death and Life of Great American Cities. New York: Random House, 1961.

Koren, Leonard. Wabi Sabi for Artists, Designers, Poets, and Philosophers. Point Reyes, CA: Imperfect Publishing, 1994.

McDonough, William, and Michael Braungart. Cradle to Cradle: Remaking the Way We Make Things. New York: North Point Press, 2002.

Tanizaki, Junichiro. Praise of Shadows. Translated by Thomas J. Harper and Edward G. Seidensticker. New Haven: Leete’s Island Books, 1977.

Scholarly Articles

Chan, Caroline, Shiry Ginosar, Tinghui Zhou, and Alexei A. Efros. “Everybody dance now.” In Proceedings of the IEEE/CVF international conference on computer vision, pp. 5933-5942. 2019.

Kyle McDonald, 2018. Dance x Machine Learning: First Steps. [Webpage] Medium, 8 October. Available From: https://kcimc. medium.com/discrete-fgures-7d9e9c275c47

Lee, Hsin-Ying, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, and Jan Kautz. “Dancing to music.” Advances in neural information processing systems 32 (2019).

Mallick, Tanwi, Partha Pratim Das, and Arun Kumar Majumdar. “Posture and sequence recognition for Bharatanatyam dance performances using machine learning approaches.” Journal of Visual Communication and Image Representation 87 (2022): 10354.

Image Credits

Raghavan, Srinidhi. Instagram. 2023. Available From: https://www. instagram.com/_srinidhiraghavan/reel/Crd7bEOsrvq/

Prachande, Shweta. Instagram. 2023. Available From: https://www. instagram.com/shwesta/.

DEMOCRATIZING BHARATANATYAM

Investigating the Interplay between Dance and AI

2024. MFA Products of Design. School of Visual Arts, NY

Copyright © 2024 Yukti Arora. All rights reserved.

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Thesis book puvlished in completion of Masters of Fine Art degree for the Products of Design program at the School of Visual Arts, New York.

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