Next Step

Project Motivation

Reflecting on my 13 years as a competitive swimmer, 8 years in rowing, and my time teaching swim lessons, I’ve seen time and again how crucial real-time data can be to athletic success. In the heat of competition or training, coaches often struggle to make proactive, data-informed decisions, like catching a minor ankle tweak before it becomes a full-blown injury. My recent pursuit of Pilates instructor training reinforces how subtle physical cues can drastically affect performance. Technology should be helping us spot and address those signs as they happen, not just after the fact.

That insight sparked Next Step: a system leveraging AI and IoT wearables (smart socks) to provide immediate, actionable feedback on player well-being. The platform continuously monitors biometric signals, flags potential injuries or dips in performance, and offers real-time recommendations for substitutions or technique adjustments. This project fits neatly into my broader philosophy of using human–AI collaboration to amplify, not replace, human expertise, especially in fields like sports where nuanced physical intelligence is paramount.

‍

Scope and Methodology

This was a four-week prototype initiative aimed at demonstrating how AI–IoT integration could transform on-field decision-making. Our timeline was tight, so we followed a rapid, iterative process similar to a design sprint:

1. Initial Ideation and “Drunk Island” Evaluation:
   • Generated over 50 AI+IoT concepts, focusing on options feasible with minimal specialized AI expertise.
   • Narrowed down to wearables for sports, given both a clear need (preventing injuries) and a strong market opportunity (teams lose millions due to player injuries).
2. User Interviews and Wireframing:
   • Consulted with peers who coach or train regularly, gathering insights on data overload and real-time pressure.
   • Sketched preliminary dashboard layouts, emphasizing simplicity and quick recognition of injury risk indicators.
3. Prototype Development and Feedback Loops:
   • Built a low-fidelity UI to test our core features, from athlete flags to substitution recommendations.
   • Integrated feedback on clarity, responsiveness, and how best to visualize player performance metrics live.
4. Final Pitch and Proof-of-Concept Deliverable:
   • Wrapped up with a polished dashboard prototype showing real-time biometric overlays on a field map.
   • Proposed a business model emphasizing cost offsets for sports leagues and potential partnerships with insurance and device makers.

This compact sprint approach echoed my GIA project experience, distilling a big idea into a focused pilot that demonstrates tangible feasibility and user value, all while embracing iterative feedback.

‍

My Role and Goals

I served multiple roles to ensure Next Step balanced ambition with practicality:

• Design Researcher and Dashboard Lead: Created wireframes, user flows, and prototypes, applying past insights on minimizing cognitive load.
• Co-Project Manager: Aligned our four-week schedule, coordinated tasks, and synthesized coach feedback into actionable changes.
• Technical Strategist: Guided the AI–IoT data pipeline based on prior AI project work, ensuring real-time metrics were both accurate and easy to interpret.

Our key objectives were:

1. Real-Time Injury Prevention: Continuously monitor athletes for warning signs, like unusual gait or elevated body temperature, to intervene early.
2. Actionable Insights for Coaches: Simplify data visualization so time-pressed coaches can make quick, data-informed decisions during games or intense practice sessions.
3. Showcase AI–Human Augmentation: Demonstrate how strategic IoT and AI tools can heighten human expertise rather than overshadow it.

‍

DETAILED PROCESS

Brainstorm and Initial Filtering

Leveraging lessons from previous projects, we started with a broad spectrum of ideas, then used a “Drunk Island” filter (achievable within a tight timeframe, requiring only generalized AI expertise). Wearable smart socks stood out for its clear use case: minimize performance-robbing injuries in high-pressure sports contexts.

Rapid Evaluation

We developed a minimal UI focusing on real-time alerts. Early testers (coaches, athlete peers) noted the need for baseline performance data to differentiate genuine injury from normal fatigue. We addressed this by capturing each athlete’s typical metrics (e.g., stride symmetry, average velocity) to detect significant deviations.

Iterative Dashboard Design

• Alerts and Recommended Actions: A central feed highlighting injury risks, plus quick-substitution recommendations.
• Live Field Map: Real-time overlays indicating each player’s status, with green, yellow, or red markers reflecting performance and potential risks.
• Smart Socks Integration: Sensor data fed into an AI model that flagged anomalies, enabling the dashboard to visualize changes almost immediately.

Financial Feasibility and Scaling

Professional teams face steep costs when key players are sidelined. By offsetting these losses via early-detection wearable tech, Next Step offers compelling ROI. Partnerships with hardware vendors and insurance providers further anchored the solution’s commercial viability.

‍

Final Solution

Next Step is a real-time coaching assistant that merges intuitive design with robust AI. The dashboard highlights each athlete’s status, overlaying performance metrics like body temperature, stride symmetry, and stamina so coaches can make swift decisions:

1. Real-Time Biometric Alerts: If a sock sensor flags an anomaly, like persistent limping or increased physiological stress, coaches see an immediate notification.
2. Comprehensive Player Profiles: A quick-access sidebar shows each player’s baseline metrics and current stats, helping coaches assess if a dip is due to normal fatigue or a budding injury.
3. Data-Driven Substitutions: The system recommends potential subs based on available players’ readiness and historical performance.
4. Visual Field Map: A live view of the court or field shows all active players. Icons change color based on performance and health, providing a rapid at-a-glance update for the entire team.

By seamlessly integrating IoT wearables with user-centric AI dashboards, Next Step empowers coaches to prioritize both performance and player well-being, even under the pressure of live competition.

‍

Honed Skills

• User-Centered Sports Tech: Built on my athletic background to identify critical pain points for coaches and players alike.
• Rapid AI–IoT Prototyping: Expanded my expertise in sensor-based data pipelines and real-time, in-game analytics.
• Iterative Design & Pitching: Sharpened my ability to refine concepts under tight deadlines, drawing on feedback at every stage.
• Cross-Functional Alignment: Balanced input from coaches, hardware experts, and business stakeholders to craft a realistic, impactful solution.

‍

Conclusion

Next Step embodies my passion for sports and AI-human augmentation, transforming real-time data into meaningful, injury-preventing insights. From years of swimming and rowing to my ongoing Pilates instructor training, I’ve seen firsthand how even the smallest physical imbalance can disrupt an athlete’s progress. By integrating AI and IoT wearables, we can equip coaches with the tools to intervene at the right moment, before minor discomfort escalates into a career-altering injury.

In just four weeks, we built a proof-of-concept that reinforces how technology can enhance, not replace, the art of coaching. This project exemplifies my commitment to developing AI that enhances decision-making in high-stakes environments, ensuring technology serves as a partner rather than a replacement.