AI as the hot topic and one of the disruptive technologies of this decade. Within the next months and years there will be more and more use cases to use AI in our daily life. As an enthuastic freediver and experience designer it was sure for me to combine these things within an application made for freediver.
Problem
Freediving is a challenging sport with several common problems in training and performance.
Lack of personalized training programs: Freediving presents a challenge because each person's body reacts differently to the pressure and depth of the water.
Difficulty in tracking progress: Freedivers often struggle to track their progress over time due to difficulties in accurately measuring their depth and time underwater.
Lack of training resources: Many freedivers live in areas without access to professional coaching and training facilities.
Limited access to data: The amount of data generated during a freediving session can be overwhelming, and it can be a challenge to analyze and interpret it all.
Solution
Tackling these problems, a freediving training application helps improve the overall performance of professional and recreational freedivers.
Personalized training plans: The application uses an algorithm to analyze a user's physical attributes and diving history to create a personalized training plan tailored to their individual needs. This plan includes specific drills, exercises, and breathing techniques to improve their diving skills.
Real-time monitoring: Wearable technology monitors an athlete's vital signs while diving. The application alerts the athlete if they are at risk of injury or provides suggestions to improve their performance.
Performance tracking: Sensors track depth, time underwater, and other performance metrics during a dive. The data is analyzed and displayed in a dashboard, allowing the athlete to monitor their progress over time and adjust their training plan as necessary.
Virtual coaching: The application offers virtual coaching sessions with a professional freediving coach. This is particularly useful for athletes who don't have access to professional coaching in their area.
Data analysis: Machine learning algorithms analyze data generated during a dive. The analysis provides insights into areas where the athlete needs to improve, such as breath-holding or equalization techniques.
Process
Discover
Conduct extensive market research, including secondary research and existing data to identify customer pain points and develop relevant freediving personas.
conducted Interviews with potential users to understand their needs and habits within the freediving community
Define
Develop a customer-centric user journey that prioritizes the user experience.
Ideate
Identify opportunities to improve the customer experience, such as personalized training plans, real-time monitoring, performance tracking, virtual coaching, and data analysis using machine learning algorithms.
Prototype
Develop a proof of concept for the application, including the use of wearable technology to monitor vital signs and sensors to track depth and time underwater.
Develop a dashboard to display performance metrics and progress over time.
Conduct virtual coaching sessions with a professional freediving coach and incorporate machine learning algorithms for data analysis.
Next Steps
Further testing and refinement of the application are necessary to ensure that it meets the needs of users and provides a seamless and engaging experience.
Additionally, identifying other key personas, such as stakeholders, and considering their potential within the future can further increase the potential for success on the transformation journey.