– The Emotional Vision Behind Surfboard Sonification
Surfing is more than just a sport. For many surfers, it is a ritual, a form of meditation, and an experience of deep emotional release. There is a unique silence that exists out on the water. It is not the absence of sound but the presence of something else: a sense of connection, stillness, and immersion. This is where the idea for “Surfboard Sonification” was born. It began not with technology, but with a feeling. A moment on the water when the world quiets, and the only thing left is motion and sensation.
The project started with a simple question: how can one translate the feeling of surfing into sound? What if we could make that feeling audible? What if we could tell the story of a wave, not through pictures or words, but through vibrations, resonance, and sonic movement?
My inspiration came from both my personal experiences as a surfer and from sound art and acoustic ecology. I was particularly drawn to the work of marine biologist Wallace J. Nichols and his theory of the “Blue Mind.” According to Nichols, being in or near water has a scientifically measurable impact on our mental state. It relaxes us, improves focus, and connects us to something larger than ourselves. It made me wonder: can we create soundscapes that replicate or amplify that feeling?
In addition to Nichols’ research, I studied the sound design approaches of artists like Chris Watson and Jana Winderen, who work with natural sound recordings to create immersive environments. I also looked at data-driven artists such as Ryoji Ikeda, who transform abstract numerical inputs into rich, minimalist sonic works.
The goal of Surfboard Sonification was to merge these worlds. I wanted to use real sensor data and field recordings to tell a story. I did not want to rely on synthesizers or artificial sound effects. I wanted to use the board itself as an instrument. Every crackle, vibration, and movement would be captured and turned into music—not just any music, but one that feels like surfing.
The emotional journey of a surf session is dynamic. You begin on the beach, often overstimulated by the environment. There is tension, anticipation, the chaos of wind, people, and crashing waves. Then, as you paddle out, things change. The noise recedes. You become attuned to your body and the water. You wait, breathe, and listen. When the wave comes and you stand up, everything disappears. It’s just you and the ocean. And then it’s over, and a sense of calm returns.
This narrative arc became the structure of the sonic composition I set out to create. Beginning in noise and ending in stillness. Moving from overstimulation to focus. From red mind to blue mind.
To achieve this, I knew I needed to design a system that could collect as much authentic data as possible. This meant embedding sensors into a real surfboard without affecting its function. It meant using microphones that could capture the real vibrations of the board. It meant synchronizing video, sound, and movement into one coherent timeline.
This was not just an artistic experiment. It was also a technical challenge, an engineering project, and a sound design exploration. Each part of the system had to be carefully selected and tested. The hardware had to survive saltwater, sun, and impact. The software had to process large amounts of motion data and translate it into sound in real time or through post-processing.
And at the heart of all this was one simple but powerful principle, spoken to me once by a surf teacher in Sri Lanka:
“You are only a good surfer if you catch a wave with your eyes closed.”
That phrase stayed with me. It encapsulates the essence of surfing. Surfing is not about seeing; it’s about sensing. Feeling. Listening. This project was my way of honoring that philosophy—by creating a system that lets us catch a wave with our ears.
This blog series will walk through every step of that journey. From emotional concept to hardware integration, from dry-land simulation to ocean deployment. You will learn how motion data becomes music. How a surfboard becomes a speaker. And how the ocean becomes an orchestra.
In the next post, I will dive into the technical setup: the sensors, microphones, recorders, and housing that make it all possible. I will describe the engineering process behind building a waterproof, surfable, sound-recording device—and what it took to embed that into a real surfboard without compromising performance.
But for now, I invite you to close your eyes. Imagine paddling out past the break. The sound of your breath, the splash of water, the silence between waves. This is the world of Surfboard Sonification. And this is just the beginning.
References
Nichols, W. J. (2014). Blue Mind. Little, Brown Spark.
Have fun with this Video to find out what my actual Prototype is.
Reflection
This project began with a vague idea to visualize CO₂ emissions — and slowly took shape through cables, sensors, and a healthy amount of trial and error. Using a potentiometer and a proximity sensor, I built a simple system to scroll through time and trigger animated data based on presence. The inspiration came from NFC tags and a wizard VR game (yes, really), both built on the idea of placing something physical to trigger something digital. That concept stuck with me and led to this interactive desk setup. I refined the visuals, made the particles feel more alive. I really want to point out how important it is to ideate and keep testing your ideas, because there will always be changes in your plans or something won’t work etc. Let’s go on summer vacation now 😎
In the current digital era, Customer Experience has evolved with multichannel support, chatbots, self-care, and virtual assistants to reduce customer effort, driving the development of Customer Relationship Management tools. However, while significant investments have focused on empowering AI-assisted agents, the role of managers has been largely underserved.
I’m designing a solution to empower the “Augmented Manager”, equipping leaders with advanced tools and analytics to optimize real-time-onsite–remote-team performances and deliver outstanding results in an increasingly complex, tech-driven customer experience ecosystem.
Beside.you is a Software As A Service (SaaS) solution, is developed to simplify decision-making and boost efficiency for managers.
By solving the biggest business challenges, with intuitive functionalities simplifying Steering, Performance management, and resources’ Growth, it is shaping a future whereall business tools work seamlessly together, unlocking unmatched operational excellence for organizations everywhere.
This small demo showcases some parts of the product experience that is offered beginning with Steering on its macro micro levels:
As part of my SaaS project, one of the user interfaces that had to be done is the Steering dashboard but it required a lot of back and fourth of feedback, iterations and meetings to finally come to a great enough version in terms of functionality and business but as a product designer and using Lean UX as a methodolgy , at some stage I had to do an iteration test.
Macro and Steering are the fundamentals of what consists this interface, they serve the business internally as much as they serve it externally with clients and prospects.
Macro: refers to the holistic overview of an insights dashboard and its part of the macro-micro dynamic that I’m using as a system thinking for the whole project.
Steering: provides a real-time view of every KPI needed to make better decisions and steer the business towards a better course.
This testing/iteration process shows the first level of a user interface that required little user interview, user feedback and zero iteration but it served as a good foundation and a stepping stone to a later version that will pave the way to the “final” iteration.
The first version was even called ‘Adherence Planning’ which was an innacurate term of what we are trying to achieve and also because it defined only one aspect of the whole dashboard.
It had the basic insights that we want to show without much output but it was limited, inefficient and not value driven on a business level.
So after much stakeholder’s feedback we iterated that version to a much effective version that visualised the data in a much more functional manner and I did that through designing the work force performance gauge chart, rearranging the cards to suit the data monitoring priority, tweaked the charts and their colors on a UI level for more effective anomaly identification, streamlied the data visualisation with proper alerts identification and finally made the 3 dots ‘kebab’ compartment the drawer where every additional setting is hidden, and here is where I arrived:
The Steering Dashboard is now much more structured and serves the decision making business goal on a decent level, every KPI can be checked and monitored in real time along with checking the business performance in terms of profit and loss through the “Work force performance” gauge.
What I learned
A disruptive data vizualisation product can never be done, but through proper UI UX design, testing, iterating and coming up with the optimal solutions, it can reach a certain level of efficient structure that will propel it to achieve some of the business goals that were far further in the roadmap of the product experience and all of the above can only be done through collaboration and proper communication within a team.
All and all, I’m just getting started and i’m thrilled to work more on the refinement of both my skills and everything that I get involved in
At the current stage of the project, before engaging with real-time biofeedback sensors or integrating hardware systems, I deliberately chose to begin with a simulation-based exploration using publicly available physiological datasets. This decision was grounded in both practical and conceptual motivations. On one hand, working with curated datasets provides a stable, low-risk environment to test hypotheses, establish workflows, and identify relevant signal characteristics. On the other hand, it also offered an opportunity to engage critically with the structure, semantics, and limitations of real-world psychophysiological recordings—particularly in the context of trauma-related conditions.
My personal interest in trauma physiology, shaped by lived experience during the war in Ukraine, has influenced the conceptual direction of this work. I was particularly drawn to understanding how conditions such as post-traumatic stress disorder (PTSD) might leave measurable traces in the body—traces that could be explored not only scientifically, but also sonically and artistically. This interest was further informed by reading The Body Keeps the Score by Bessel van der Kolk, which inspired me to look for empirical signals that reflect internal states which often remain inaccessible through language alone.
With this perspective in mind, I selected a large dataset focused on stress-induced myocardial ischemia as a starting point. Although the dataset was not originally designed to study PTSD, it includes a diverse group of participants—among them individuals diagnosed with PTSD, as well as others with complex comorbidities such as coronary artery disease and anxiety-related disorders. The richness of this cohort, combined with the inclusion of multiple biosignals (ECG, respiration, blood pressure), makes it a promising foundation for exploratory analyses.
Rather than seeking definitive conclusions at this point, my aim is to uncover what is possible—to understand which physiological patterns may hold relevance for trauma detection, and how they might be interpreted or transformed into expressive modalities such as sound or movement. This stage of the project is therefore best described as investigative and generative: it is about opening up space for experimentation and reflection, rather than narrowing toward specific outcomes.
Data Preparation and Extraction of Clinical Metadata from JSON Records
To efficiently identify suitable subjects for simulation, I first downloaded a complete index of data file links provided by the repository. Using a regular expression-based filtering mechanism implemented in Python, I programmatically extracted only those links pointing to disease-related JSON records for individual subjects (i.e., files following the pattern sub_XXX_disease.json). This was performed using a custom script (see downloaded_disease_files.py) which reads the full list of URLs from a text file and downloads the filtered subset into a local directory. A total of 119 such JSON records were retrieved.
Following acquisition, a second Python script (summary.py) was used to parse each JSON file and consolidate its contents into a single structured table. Each JSON file contained binary and categorical information corresponding to specific diagnostic criteria, including presence of PTSD, angina, stress-induced ischemia, pharmacological responses, and psychological traits (e.g., anxiety and depression scale scores, Type D personality indicators).
The script extracted all available key-value pairs and added a subject_id field derived from the filename. These entries were stored in a pandas DataFrame and exported to a CSV file (patients_disease_table.csv). The resulting table forms the basis for all subsequent filtering and selection of patient profiles for simulation.
This pipeline enabled me to rapidly triage a heterogeneous dataset by transforming a decentralized JSON structure into a unified tabular format suitable for further querying, visualization, and real-time signal emulation.
Group Selection and Dataset Subsetting
In order to meaningfully simulate and compare physiological responses, I manually selected a subset of subjects from the larger dataset and organized them into four distinct groups based on diagnostic profiles derived from the structured disease table:
Healthy group: Subjects with no recorded psychological or cardiovascular abnormalities.
Mental health group: Subjects presenting only with psychological traits such as elevated anxiety, depressive symptoms, or Type D personality, but without ischemic or cardiac diagnoses.
PTSD group: Subjects with a verified PTSD diagnosis, often accompanied by anxiety traits and non-obstructive angina, but without broader comorbidities.
Clinically sick group: Subjects with extensive multi-morbidity, showing positive indicators across most of the diagnostic criteria including ischemia, psychological disorders, and cardiovascular dysfunctions.
This manual classification enabled targeted downloading of signal data for only those subjects who are of particular interest to the ongoing research.
A custom Python script was then used to selectively retrieve only the relevant signal files—namely, 500 Hz ECG recordings from three phases (rest, stress, recovery) and the corresponding clinical complaint JSON files. The script filters a list of raw download links by matching both the subject ID and filename patterns. Each file is downloaded into a separate folder named after its respective group, thereby preserving the classification structure for downstream analysis and simulation.
Preprocessing of Raw ECG Signal Files
Upon downloading the raw signal files for the selected subjects, several structural and formatting issues became immediately apparent. These challenges rendered the original data format unsuitable for direct use in real-time simulation or further analysis. Specifically:
Scientific Notation Format All signal values were encoded in scientific notation (e.g., 3.2641e+02), requiring transformation into standard integer format suitable for time-domain processing and sonification.
Flattened and Fragmented Data Each file contained a single long sequence of values with no clear delimiters or column headers. In some cases, the formatting introduced line breaks within numbers, further complicating parsing and extraction.
Twelve-Lead ECG in a Single File The signal for all 12 ECG leads was stored in a single continuous stream, without metadata or segmentation markers. The only known constraint was that the total length of the signal was always divisible by 12, implying equal-length segments per lead.
Separated Recording Phases Data for each subject was distributed across three files, each corresponding to one of the experimental phases: rest, stress, and recovery. For the purposes of this project—particularly real-time emulation and comparative analysis—I required a single, merged file per lead containing the full time course across all three conditions.
Solution: Custom Parsing and Lead Separation Script
To address these challenges, I developed a two-stage Python script to convert the raw .csv files into a structured and usable format:
Step 1: Parsing and Lead Extraction The script recursively traverses the directory tree to identify ECG files by filename patterns. For each file:
The ECG phase (rest, stress, or recover) is inferred from the filename.
The subject ID is extracted using regular expressions.
All scientific-notation numbers are matched and converted into integers. Unrealistically large values (above 10,000) are filtered out to prevent corruption.
The signal is split into 12 equally sized segments, corresponding to the 12 ECG leads. Each lead is saved as a separate .csv file inside a folder structure organized by subject and phase.
Step 2: Lead-Wise Concatenation Across Phases Once individual leads for each phase were saved, the script proceeds to merge the rest, stress, and recovery segments for each lead:
For every subject, it locates the three corresponding files for each lead.
These files are concatenated vertically (along the time axis) to form a continuous signal.
The resulting merged signals are saved in a dedicated combined folder per subject, with filenames that indicate the lead number and sampling rate.
This conversion pipeline transforms non-tabular raw data into standardized time series inputs suitable for further processing, visualization, or real-time simulation.
Manual Signal Inspection and Selection of Representative Subjects
While the data transformation pipeline produced technically readable ECG signals, closer inspection revealed a range of physiological artifacts likely introduced during the original data acquisition process. These irregularities included signal clipping, baseline drift, and abrupt discontinuities. In many cases, such artifacts can be attributed not to software or conversion errors, but to common physiological and mechanical factors—such as subject movement, poor electrode contact, skin conductivity variation due to perspiration, or unstable placement of leads during the recording.
These artifacts are highly relevant from a performative and conceptual standpoint. Movement-induced noise and instability in bodily measurements reflect the lived, embodied realities of trauma, and they may eventually be used as expressive material within the performance itself. However, for the purpose of initial analysis and especially heart rate variability (HRV) extraction, such disruptions compromise signal clarity and algorithmic robustness.
To navigate this complexity, I conducted a manual review of the ECG signals for all 16 selected subjects. Each of the 12 leads per subject was visually examined across the three experimental phases (rest, stress, and recovery). From this process, I identified one subject from each group (healthy, mental health, PTSD, and clinically sick) whose signals displayed the least amount of distortion and were most suitable for initial HRV-focused simulation.
Diagnostic Visualization Tool
To facilitate and streamline this selection process, I implemented a simple interactive visualization tool in Python. This utility allows for scrollable navigation through long ECG recordings, with a resizable window and basic summary statistics (min, max, mean values). It was essential for rapidly identifying where signal corruption occurred and assessing lead quality in a non-automated but highly effective way.
This tool enabled both quantitative assessment and intuitive engagement with the data, providing a necessary bridge between raw measurement and informed experimental design.
Selection of Final Subjects and Optimal ECG Leads
Following visual inspection of all 12 ECG leads across the 16 initially shortlisted subjects, I selected one representative from each diagnostic group whose recordings exhibited the highest signal quality and least interference. The selection was based on manual analysis using the custom-built scrollable ECG viewer, with particular attention given to the clarity and prominence of QRS complexes—a critical factor for accurate heart rate variability (HRV) analysis. The final subjects are: 005, 044, 091, 115:
These signals will serve as the primary input for all upcoming simulation and sonification stages. While artifacts remain an inherent part of physiological measurement—especially in ambulatory or emotionally charged conditions—this selection aims to provide a clean analytical baseline from which to explore more experimental, expressive interpretations in later phases.
References:
Van der Kolk, Bessel A. 2014. The Body Keeps the Score: Brain, Mind, and Body in the Healing of Trauma. New York: Viking.
At this year’s WebExpo in Prague, one of the talks that stood out most to me was Nadieh Bremer’s session titled “Creating an Effective & Beautiful Data Visualisation from Scratch.” With no prior experience using d3.js, I didn’t quite know what to expect. I was mainly curious about how data visualisation could be approached from a design perspective. But what Nadieh shared was much more than a technical intro, it felt like a live deep dive into creative thinking, problem-solving, and visual storytelling.
What set this talk apart was its format. Rather than giving a traditional slide-based talk, Nadieh did a live coding session. She started with a completely empty browser window and built the data visualisation from the ground up using d3.js. This format made the talk feel refreshingly honest and grounded. It was engaging to watch her work through the logic in real time – narrating each decision as she went, pointing out potential issues, and offering insight into how she solves problems as they arise. This transparency made the whole process feel approachable, even though I was unfamiliar with the tool.
What I appreciated most was how she balanced the technical with the creative. It wasn’t just about writing functional code; it was about shaping something visually appealing and meaningful. Nadieh showed how, with a bit of imagination, SVG can be used in unconventional and expressive ways. The result wasn’t a generic bar chart or pie graph – it was a visually rich and thoughtfully composed visualisation that clearly communicated the underlying data while also looking beautiful.
Her message about simplification really resonated with me. I often struggle with the tendency to include too much information in my designs, believing that more content adds value. Nadieh’s approach showed the opposite: that complexity can be made understandable through clarity, and that thoughtful visual design can make even dense data feel intuitive. She emphasized that effective data visualisation doesn’t just display information – it tells a story. And when done well, it can communicate more with less.
Beyond the content, I also want to mention how well-structured and calm her presentation style was. Live coding can be stressful to watch (and probably to do), but she created a relaxed atmosphere that made it easy to follow along. Even when something didn’t work immediately, she explained why and showed how to fix it – normalizing the trial-and-error nature of coding.
Overall, this talk was a highlight of WebExpo for me. It was both inspiring and informative, offering practical insights into a tool I hadn’t encountered before. It made me want to experiment with data visualisation myself and gave me a clearer sense of how design can play a crucial role in making complex information understandable, and even beautiful.
Clutter and confusion are not attributes of data—they are shortcomings of design. – Edward Tufte
Michael Friendly defines data visualization as “information which has been abstracted in some schematic form, including attributes or variables for the units of information.” In other words, it is a coherent way to visually communicate quantitative content. Depending on its attributes, the data may be represented in many different ways, such as a line graph, bar chart, pie chart, scatter plot, or map.
It’s important for product designers to adhere to data visualization best practices and determine the best way to present a data set visually. Data visualizations should be useful, visually appealing and never misleading. Especially when working with very large data sets, developing a cohesive format is vital to creating visualizations that are both useful and aesthetic.
Principles
Define a Clear Purpose
Data visualization should answer vital strategic questions, provide real value, and help solve real problems. It can be used to track performance, monitor customer behavior, and measure effectiveness of processes, for instance. Taking time at the outset of a data visualization project to clearly define the purpose and priorities will make the end result more useful and prevent wasting time creating visuals that are unnecessary.
Know the Audience
A data visualization is useless if not designed to communicate clearly with the target audience. It should be compatible with the audience’s expertise and allow viewers to view and process data easily and quickly. Take into account how familiar the audience is with the basic principles being presented by the data, as well as whether they’re likely to have a background in STEM fields, where charts and graphs are more likely to be viewed on a regular basis.
Visual Features to Show the Data Properly
There are so many different types of charts. Deciding what type is best for visualizing the data being presented is an art unto itself. The right chart will not only make the data easier to understand, but also present it in the most accurate light. To make the right choice, consider what type of data you need to convey, and to whom it is being conveyed.
Make Data Visualization Inclusive
Color is used extensively as a way to represent and differentiate information. According to a recent study conducted by Salesforce, it is also a key factor in user decisions.
They analyzed how people responded to different color combinations used in charts, assuming that they would have stronger preferences for palettes that had subtle color variations since it would be more aesthetically appealing.
However, they found that while appealing, subtle palettes made the charts more difficult to analyze and gain insights. That entirely defeats the purpose of creating a visualization to display data.
The font choice can affect the legibility of text, enhancing or detracting from the intended meaning. Because of this, it’s better to avoid display fonts and stick to more basic serif or sans serif typefaces.
Conclusion
Good data visualization should communicate a data set clearly and effectively by using graphics. The best visualizations make it easy to comprehend data at a glance. They take complex information and break it down in a way that makes it simple for the target audience to understand and on which to base their decisions.
As Edward R. Tufte pointed out, “the essential test of design is how well it assists the understanding of the content, not how stylish it is.” Data visualizations, especially, should adhere to this idea. The goal is to enhance the data through design, not draw attention to the design itself.
Keeping these data visualization best practices in mind simplifies the process of designing infographics that are genuinely useful to their audience.
Introducing Beside You: the Augmented Managment software that will later be introduced as a SaaS (Software as a Service).
Initially, working on this project has been a ride since the beginning, being the sole product designer at a company that specializes in customer experience and business management along with a team of full stack devs and data scientists meant that we either we will get things done or we will get things done.
One of the early challenges was to convert a data driven platform to an intuitive and cohesive interface that offers the ease of analysis and ultimately efficient decision making.
One of the ‘Pre Design Sprint’ Data Interface that I found when I came onboard.
One of the early challenges was to convert a data driven platform to intuitive and cohesive interface that offers the ease of analysis and ultimately efficient decision making.
Sketching consisted of building a skeleton that will overaly the first foundation that will eventually convey the design language that the software will adopt and in the meantime everything has to be succintly explained to the stakeholders in order for them to grasp the design system and for me to extract more insights from them since they will be the first users of the software.
Moving on to a low fidelity wireframe with the intention of giving more meaning and structure to the interface meant that progress is happening and things are starting to evolve in terms of results from the ideation and the user interviews. Also, the latter gave me the playground to begin building the design system from the layout, color palette, and typography to the spacing and the accessibilty.
First draft of the dashboard’s lo-fi wireframe
Moving on to a highfidelity wireframe that paved the way to add real data to the interface that allowed us as a team to get a realistic first look on how the software will behave in terms of data visualisation and user navigation and from there began our first user test which will give us a direction to make the right adjustments and iterations.
First iteration of the dashboard’s hi-fi wireframe
To conclude, this article is a condensed version of what actually happened and the process behind creating a data driven product from the ground up, otherwise I will have to write a journal that won’t serve nobody but nonetheless it is always beneficial to write about a design process that was complicated and challenging at times, the benefits are a documentation and a clarity that will even pave the way to iterate better on the next stages of the product life cycle.
In this second part of the IDW25 recap I want to talk about the prototype I created of my CO2 project. The goal for me was to build an installation to interact with the data visualisation. For this I chose makey makey and built a control mechanism with aluminium foil and a pressure plate. To send the data from processing to the projector I used Arena Resolume. My concept was based on the CO2 footprint. So you activated the animation with steping on the pressure plate. Here are first sketches of my idea.
I thought about making an interactive map where you step on different countries and realease fog in a glas container. But that was too much to create in one week so I reduced the concept to projecting the boundary I showed in my last blog post. I kept it simple and connected the makey makey to aluminum foil to control the time line with a finger tap. The processing output is being projected on the wall.
Conclusion
It was interesting to get to know the possibilities of the used technology to visualise data and therefore I had a lot of fun during production. I will definitly keep working on this project to see where it can go. Also expand the data set to compare more countries. In the first part of the blog post I mentioned that there are probably better programs to make more visually appealing animations for such a topic. But for a rough protoype this was completly fine.
The International Design Week 2025 is over and this first part of the blog will be a recap of my process. I joined the workshop #6—Beyond Data Visualisation with Eva-Maria Heinrich. The goal was to present a self chosen data set on a socio-political topic. I chose a data set on the Co2 emission worldwide per country (https://ourworldindata.org/co2-emissions). The process started with evaluating the data span I want to show and the method of visualisation. Because the task of the workshop was to present data in an abstract way and to step back from the conventional methods, to make the experience more memorable.
Cutting the Data with Python
So to get a specific range of data to make a prototype i used python to cut the csv file to my liking. I used the pandas tool for python to manipulate the file. At first I wanted to compare three countries, but later in the process I realized that this goal was a bit too much for the given time, since I haven’t used python like this before. It was a nice way to get to know the first steps of data analysis with coding.
I created a new csv file with a selected country, in this case it was Austria in a time span from 1900—2023. Now it was time to visualise it.
Let’s get creative!
In my research on how CO2 was being visualised before I looked up some videos of NASA showing how the emission covers the world. I got inspired by this video.
I chose processing to create my own interpretation of visualising emission. In hindsight, there are probably better tools to do that, but it was interesting to work with processing and code some visuals relative to a data set. I created a radial boundary which is invisible. Inside this shape, i let a particle system flow around which is relative to the CO2 emission in the specific year, shown in the top left corner. This visualisation works like a timeline. You can use your LEFT and RIGHT arrow keys to go back and forth in 10 years steps. The boundary expands or be reduced, which depends if the emission of that year is higher or less. The particle system also draws more or less circles, depending on the amount of CO2.
After the workshop was done I tried out other methods to make the particle system flow more and create a feeling of gas and air.
Conclusion
The whole week was a nice experience. I got to try out new techniques and tools and create something i never done have before. A problem I encountered was the time. It’s hard to estimate what you can do, if you try out something completly new. The presentation day at the end was really inspiring and emotional to see what the all the other students have created and talking about their process and results.
