Overuse injuries like tendonitis affect 8 out of 10 arm-sport athletes. Finally, there’s a smarter way to heal and get back in the game.
Meet Spotter: your injury prevention and recovery partner. Spotter is a revolutionary AI-powered athletic wearable that helps correct your arm mechanics and muscular effort in real time, so you can push past nagging overuse injuries and move with confidence.
Built for athletes. Spotter wraps your forearm with precision FMG and IMU sensors to capture critical muscle signals and movement, delivering instant haptic, visual, and audible alerts the moment your technique drifts into dangerous territory.
Say “Ouch!” When discomfort strikes during activity, just say the word – or press the pain button – and Spotter instantly flags that moment. Over time, these data points shape your personal threshold profile and sharpen your recovery outcomes.
Powered by AI. Spotter’s machine learning engine creates a deeply personal model of how you move – combining sensor data with your pain inputs to evolve your unique biomechanical threshold profile, continuously refined by anonymized data from the entire Spotter community.
Better together. One Spotter is powerful. Two are transformative. Pair two devices and they communicate with each other as you move, building a richer picture of your biomechanics and delivering even more precise threshold profile updates.
Your movement, decoded. Spotter’s app puts your performance data at your fingertips. Review sessions, track progress, benchmark against norms, tune your threshold profile, and share insights with your care team.
Your Spotter, your way. Express yourself with five fresh colorways and a modular design you can mix and match to suit your unique style. Spotter’s low-profile, rugged construction is ready for whatever sport you throw at it.
Process
The problem. Overuse injuries like tendonitis are invisible until it’s too late. Repeated improper movement quietly accumulates damage – masked by the endorphins exercise releases. By the time an athlete feels pain, the harm is already done.
Competitive analysis. Athletic wearables have a blind spot: injury prevention. Most infer workout load from generic movement and heart rate data. None track muscle strain or body mechanics directly.
Market research. To map the injury landscape and identify disruption opportunities, I built an online poll, distributed through gym fliers and word of mouth. I surveyed arm-sport athletes across climbing, tennis, cycling, and more – validating product variables before a single design decision was made.
Functional prototype. To prove the biometric tracking concept, I built a test unit from an Arduino Nano, dual IMU sensors for motion capture, FSR sensors for muscle activation, a haptic motor, and an LED indicator – the fully functional sensing and alert stack in raw form.
Sketching. With functional requirements locked, I built Spotter’s visual language around its name. Circles and spots repeat throughout – from the puck form to the FSR sensor bumps to the LED ring.
Gen AI. I used Vizcom throughout to rapidly explore form directions, test materials, and validate appearance concepts – before committing to CAD or physical prototypes.
Rendering studies. With the concept direction set, I moved into SolidWorks to dial in proportions, explore snap-fit mechanisms, and develop precise geometries.
Prototypes. Physical prototypes were a constant throughout the process – the fastest way to understand how Spotter would look and feel on the body.
User testing. Concepts were tested in real athletic environments to identify shifting, discomfort, and sizing issues – and to sharpen the design through each iteration.
CMF. Taking cues from 1980s Swatch watches but with a modern palette, I developed five interchangeable colorways that feel bold and fashionable – not clinical. Band materials had to hold up to sweat and the demands of the most active athletes.
