01. ABSTRACT
This project reimagines the surfboard as a data-driven tool, integrating advanced sensors to measure wave interaction, surfer dynamics, and board performance. By merging this scientific data with creative visualization, it opens new dimensions for surfboard shaping, surfer training, and interactive art. Using innovative tools like the x-IMU3 sensor, Pure Data, and TouchDesigner, this project seeks to translate surfing’s raw energy into visuals and soundscapes. The outcome will not only enhance understanding for surfers and shapers but also inspire broader cultural and artistic engagement with the sport. By transforming raw surfing data into emotionally resonant soundscapes and visuals, this project creates a new artistic medium for experiencing the sport, pushing the boundaries of what’s possible in surfing, technology, and art.
The project is divided into four main phases: research and preparation, prototype development and field testing, data processing and visualization, and finalization and presentation. Each phase is designed to ensure the project’s success, from the initial selection of sensors to the final interactive installation. The project also lays the foundation for a future master thesis exploring real-time applications of this technology, with potential commercial applications such as an app or software for surfers and shapers.
02. Introduction and Background
Surfing is a deeply technical sport, where the relationship between the surfer, the board, and the wave is essential. However, much of this interaction remains intuitive, with limited data-driven insights available to inform board design or surfer performance. Current technologies like TRACE and Surflogic GPS focus on external metrics such as speed and location, leaving critical factors—such as board flex, wave impact, and surfer positioning—unexplored.
Building on the 2015 TorFlex project by Cabianca Surfboards, this research uses the latest sensor technology to achieve levels of accuracy and data detail that were not done before. Collaborating with professional shapers and surfers, this project will integrate sensors into surfboards, transforming them into tools for analysis, visualization, and artistic expression. While existing technologies focus on performance metrics, this project goes beyond by exploring the artistic potential of surfing. By translating motion, speed, and vibrations into sound and visuals, we aim to create a new way to experience and appreciate the sport.
The project also draws inspiration from other fields, such as computational fluid dynamics (CFD) and sports technology, to ensure a robust scientific foundation. By combining these elements, the project aims to create a surfboard that not only performs well but also provides valuable data for surfers and shapers, while also serving as a medium for artistic expression.
03. Research Question
How can embedded sensors on a surfboard capture environmental and performance data to create auditory and visual representations of surfing?
Sub-Questions:
- Can sensor data be used to create emotionally resonant sound and visuals that enhance the surfing experience?
- How can a sensor-embedded surfboard improve performance without compromising the traditional surfing experience?
- What are the most effective methods for visualizing and sonifying complex surfing data in real-time?
- How can the data collected from sensor-embedded surfboards inform future surfboard design and surfer training?
Research on Existing Projects and Technological Advancements
To develop a sensor-integrated surfboard that captures and translates surfing data into artistic visualizations and soundscapes, it is crucial to understand past and ongoing research in this field. Several projects have laid the groundwork for data-driven surfboard innovation, yet technological advancements in machine learning and sensor accuracy now enable deeper exploration and improved results.
One of the most notable initiatives is the SurfSens Project, a collaboration between Pukas Surf and Tecnalia, which equipped surfboards with pressure sensors, flex sensors, GPS, and accelerometers. The data was recorded via an embedded computer and later analyzed through the Robot Operating System (ROS). While this project provided valuable insights into surfer technique and board performance, it was conducted several years ago, meaning that modern sensors and data analysis tools can now achieve even greater precision and applicability.
(https://www.ros.org/news/2011/02/robots-using-ros-surfsens-high-performance-surfboard-with-integrated-sensors.html)
– video: https://vimeo.com/20197603
Another compelling approach was explored in the
Data-Generated Surfboards Project, which utilized onboard sensors to analyze movement and pressure data. This data informed the creation of CNC-shaped surfboards customized for individual surfers. Though promising, this project remained relatively small in scope and did not fully explore the integration of artistic visualization or real-time data processing.
(https://hackaday.io/project/166977-data-generated-surfboards)
The Smartfin Project took a different approach, focusing on environmental data collection. By embedding sensors into a surfboard fin, it recorded ocean parameters like temperature and wave characteristics, transmitting data over cellular networks. While this project contributed to oceanographic research, it did not directly address the dynamics of board performance or surfer technique.
(https://blog.scistarter.org/2021/09/with-smartfin-surfers-collect-ocean-data-while-they-hang-ten/)
Insights from Industry and Academic Collaborations
Beyond analyzing existing projects, I have actively engaged with industry professionals to gain deeper insights. I connected with Jonny from Cabianca Surfboards, who previously conducted extensive research into surfboard flex through the TorFlex Project. This system allowed shapers to measure flex, torsion, and vibration in boards to refine their performance. However, Jonny mentioned that the project was halted due to high costs and the complexity of testing different board designs. With today’s more accessible and advanced sensor technology, alongside machine learning applications, I believe these challenges can be overcome, allowing for a more streamlined and scalable approach.
Additionally, I am in discussions with Pukas Surf regarding their past research and potential collaboration. I have applied for an internship with them, which would allow me to gain firsthand knowledge of their findings and integrate their expertise into my project. I am also considering working with Cabianca Surfboards to build and test my prototype surfboard.
Leveraging Modern Technology for a New Approach
While these previous projects laid a strong foundation, I aim to push the boundaries further by integrating:
- Machine Learning for Data Analysis: Unlike past projects, I will apply AI models to recognize movement patterns, board flex characteristics, and wave interactions, providing deeper insights into surfer performance and board design.
- Real-Time Data Visualization and Sonification: Using Pure Data and TouchDesigner, I will transform surfboard motion and environmental data into an immersive, artistic experience, making the project not just a scientific tool but also an expressive medium.
- Advanced Sensor Integration: With support from my university, I will have access to cutting-edge sensors and funding, allowing me to integrate high-precision IMUs (like the x-IMU3), pressure sensors, and hydrophonesinto the surfboard for detailed data collection.
- Collaboration with Experts: I will work closely with professors specializing in sensor integration and data visualization, ensuring a rigorous research approach.
Conclusion
By combining elements from previous research projects with modern advancements in machine learning, real-time data processing, and artistic representation, my project will not only provide insights into surfboard performance but also transform raw surfing data into a unique audiovisual experience. Given the rapid evolution of sensor technology and data science, this project has the potential to set a new standard in surfboard innovation, offering both scientific and artistic contributions to the field.
04. Objectives
Primary Objective:
To develop a surfboard prototype equipped with sensors that collects performance and environmental data, which is then translated into immersive visual and auditory experiences.
Specific Goals:
- Data Collection:
- Capture motion, wave interaction, and board dynamics using sensors like the x-IMU3.
- Explore additional measurements, including flex, pressure distribution, and surfer positioning.
- Visualization and Sound Design:
- Use tools like TouchDesigner and Pure Data to transform collected data into compelling visuals and soundscapes.
- Ensure the artistic output reflects surf culture, board shaping processes, and wave dynamics.
- Collaboration and Practical Application:
- Work closely with professional shapers to design boards informed by collected data.
- Test the prototype in real surfing conditions with professional surfers.
- Documentation:
- Document the entire process to establish a foundation for a master thesis and future research, potentially including a commercial application like an app or software for surfers and shapers.
- Artistic Expression:
- Create an immersive artpiece that allows audiences to experience the rhythm and beauty of surfing through sound and visuals.
05. Methodology
Phase 1: Research and Preparation (Summer Semester 2024)
- Sensor Exploration:
- Initial trials with x-IMU3 for motion tracking and gyroscopic data.
- Investigate additional sensors for pressure mapping and flex analysis.
- Collaborations:
- Partner with Cabianca Surfboards and other professional shapers for guidance on sensor placement.
- Consult with professors specializing in sensor integration and data visualization.
- Skateboard Simulations:
- Attach sensors to skateboards for controlled land-based testing.
Phase 2: Prototype Development and Field Testing (July–August 2024)
- Sensor Integration:
- Embed sensors into a surfboard during the shaping process.
- Ensure waterproofing and durability for real-world testing.
- Maybe also putting sensors on the surfer (shoulders) to capture spesific movement patterns
- Field Testing:
- Conduct trials in various surf conditions (e.g., small waves, large swells) to gather comprehensive data.
- Interview surfers to evaluate the board’s performance and usability.
Phase 3: Data Processing and Visualization (Winter Semester 2024/25)
- Data Analysis:
- Process collected data to identify patterns in motion, wave dynamics, and surfer-board interaction.
- Use tools like Grafana and Kafka for in-depth analysis
- Using AI and machine learing tools to define clear patterns and give concret numbers which are important for the surfboard shaper and surfers
- Sound Design and Visualization:
- Map motion and wave data to sound parameters (e.g., speed → pitch, pressure → amplitude).
- Create real-time visual representations inspired by ocean waves and board dynamics using TouchDesigner.
Phase 4: Finalization and Presentation (Early 2026)
- Refine the prototype and integrate feedback from field tests.
- Create an artpiece and a film showcasing the project.
- Prepare final documentation and a pitch for academic and industry presentations.
06. Risk Analysis
Technical Risks:
- Sensor failure due to water exposure or impact during surfing.
- Data loss or corruption during transmission from the surfboard to the processing unit.
Mitigation Strategies:
- Use waterproof and shock-resistant sensors.
- Implement redundant data storage and backup systems.
- Conduct rigorous testing in controlled environments before field deployment.
07. Stakeholder Engagement Plan
Surfers:
Conduct interviews and surveys to understand their needs and preferences.
Involve them in field testing to gather feedback on the prototype’s performance.
Shapers:
Collaborate with professional shapers like Cabianca Surfboards to ensure the sensors do not compromise the board’s design or performance.
Artists and Technologists:
Conduct independent creative research to explore innovative ways to visualize and sonify the data.
Immerse myself deeply in the surf culture, studying wave dynamics, board designs, and the aesthetics of surfing to create visually and thematically fitting designs for the project.
Experiment with artistic techniques and technologies to develop unique visual and auditory representations that resonate with the essence of surfing.
08. Ethical Considerations
Data Privacy:
Ensure that any personal data collected from surfers (e.g., performance metrics) is anonymized and stored securely.
Environmental Impact:
Use eco-friendly materials for the surfboard and sensors to minimize environmental harm.
Consider the long-term sustainability of the technology, especially if it were to be commercialized.
09. Broader Impact Statement
Cultural Impact:
The project could inspire new forms of artistic expression by merging sports data with creative visualization.
Educational Impact:
The technology could be used in educational settings to teach students about data science, oceanography, and sports technology.
Economic Impact:
If commercialized, the technology could create new opportunities in the surfing industry, such as data-driven surfboard design or interactive art installations.
Helps shapers have a better understanding of their products and supports surfers and professional athlets to improve their interaction with the board.
10. Potential Future Applications
Commercialization:
Develop a consumer-friendly app that allows surfers to track their performance and visualize their data in real-time.
Expansion to Other Sports:
Adapt the technology for use in other board sports, such as snowboarding or skateboarding.
Scientific Research:
Use the data collected to contribute to oceanographic research, such as studying wave patterns or the impact of climate change on surfing conditions.
11. Detailed Evaluation Metrics
Technical Metrics:
Accuracy of sensor data (e.g., motion tracking, pressure mapping).
Reliability of the system in various surf conditions.
User Experience Metrics:
Feedback from surfers on the board’s performance and usability.
Audience engagement during the interactive installation.
Artistic Metrics:
Emotional impact of the soundscapes and visuals on the audience.
Creativity and innovation in the artistic representation of surfing data.
12. Collaboration with Academic and Industry Partners
Academic Collaborations:
Work with professors specializing in sensor integration, data visualization, and computational fluid dynamics.
Industry Partnerships:
Partner with companies like Cabianca Surfboards for surfboard design and sensor integration.
Collaborate with software companies specializing in real-time data processing and visualization tools.
Professional Organizations:
Engage with surfing associations to promote the project and gather feedback from professional surfers.
13. Detailed Timeline with Milestones
| Phase | Tasks | Timeline |
| Research & Prototyping | Sensor selection, skateboard testing, collaboration with shapers, and preparation for field testing. | Summer 2024 |
| Field Testing | Integration of sensors into surfboards, data collection, and surfer feedback. | July–August 2024 |
| Data Processing | Analysis of data, sound and visual mapping, and adjustments based on findings. | Winter 2024/25 |
| Final Presentation | Prototype refinement, surf film creation, and academic/public presentations. | Early 2026 |
14. Budget Justification
| Item | Cost Estimate (EUR) |
| Sensors (x-IMU3, pressure, etc.) | 2,500 |
| Surfboard materials | 1,500 |
| Software and hardware | 1,000 |
| Travel costs | 2,000 |
| Miscellaneous | 1,000 |
| Total | 8,000 |
15. Conclusion
This project is a pioneering step in merging surfing, technology, and art. By providing real-time data on the interplay between surfers, boards, and waves, it offers transformative possibilities for surfboard design, athletic performance, and cultural expression. The strong technical foundation, combined with artistic innovation, ensures this project’s relevance to both scientific and creative communities. With its potential applications in sports analytics, art, and education, this project is poised to leave a lasting impact on the surfing world and beyond. The Sonic Wave is a project that pushes the boundaries of what’s possible in surfing, technology, and art. By transforming data into sound and visuals, we create a new way to experience and appreciate the sport. The project has the potential to inspire new ways of thinking about the intersection of sports, technology, and art, and I’m excited to see where it takes us.
16. Bibliography
- Grand View Research. Surfing equipment market size, share & trends analysis report by product (apparel & accessories, surfing boards), by distribution channel (online, offline), by region (APAC, North America), and segment forecasts, 2021–2028 (2022, accessed 30 Sep 2022). Link.
- Elshahomi, A. et al. Computational fluid dynamics performance evaluation of grooved fins for surfboards. MRS Adv. DOI (2022).
- Shormann, D. E. & in het Panhuis, M. Performance evaluation of humpback whale-inspired shortboard surfing fins based on ocean wave fieldwork. PLoS ONE 15(4), e0232035. DOI (2020).
- Gately, R. D. et al. Additive manufacturing, modeling and performance evaluation of 3D printed fins for surfboards. MRS Adv. 2, 913–920. DOI (2017).
- Gudimetla, P., Kelson, N. & El-Atm, B. Analysis of the hydrodynamic performance of three- and four-fin surfboards using computational fluid dynamics. Aust. J. Mech. Eng. 7(1), 61–67. DOI (2009).
- Falk, S. et al. Computational hydrodynamics of a typical 3-fin surfboard setup. J. Fluids Struct. 90, 297–314. DOI (2019).
- Falk, S. et al. Numerical investigation of the hydrodynamics of changing fin positions within a 4-fin surfboard configuration. Appl. Sci. 10(3), 816. DOI (2020).
- Romanin, A. et al. Surfing equipment and design: A scoping review. Sports Eng. 24, 1–13. DOI(2021).
- Roberts, J. R., Jones, R., Mansfield, N. J. & Rothberg, S. J. Evaluation of vibrotactile sensations in the feel of a golf shot. J. Sound Vibr. 285, 303–319. DOI (2004).
- Fisher, C. et al. What static and dynamic properties should slalom skis possess? Judgements by advanced and expert skiers. J. Sports Sci. 25(14), 1567–1576. DOI (2007).
- Hackaday. (n.d.). Data-Generated Surfboards. Retrieved from https://hackaday.io/project/166977-data-generated-surfboards
- ROS (Robot Operating System). (n.d.). ROS framework. Retrieved from https://www.ros.org
- SciStarter. (n.d.). Smartfin: Surfing for Science. Retrieved from https://scistarter.org/smartfin
- Vimeo. (2011). SurfSens: Intelligent Surfboard [Video]. Retrieved from https://vimeo.com/20197603
