Sports at every level now rely on data to drive performance gains, whether in Formula 1 or professional football. While spectators enjoy seeing these metrics displayed, their true value lies in helping athletes and teams optimize their training and competition strategies. Quantified training approaches work across virtually any discipline—and Manivannan has demonstrated this by creating a Smart AI Boxing Band powered by the Arduino Nesso N1.
The Hardware Foundation
Introduced in November, the Nesso N1 is a capable development board featuring a full-color touchscreen, built-in rechargeable battery, and an array of onboard sensors. The platform is designed to handle many projects independently, without requiring external components. For Manivannan's boxing application, however, one additional piece was essential: a pulse sensor to complement the board's native capabilities.
Biometrics Meets Movement
The wearable band simultaneously monitors both physiological data and physical motion. The Nesso N1's display dynamically shifts color in sync with the wearer's heartbeat, allowing boxers to gauge their exertion level without interrupting their training flow. Simultaneously, the device streams live performance analytics directly to the screen. A boxer can instantly see how executing an uppercut correlates with their heart rate, or discover patterns like a tendency to rely more on jabs as fatigue sets in.
Punch Recognition and Analysis
To distinguish between different punch types, Manivannan leveraged Edge Impulse, training the system using data collected from the Nesso N1's integrated IMU sensor. The resulting model achieved reliable classification across hooks, jabs, uppercuts, and optimal timing between strikes. The band logs each punch event alongside corresponding biometric readings, creating a comprehensive dataset for review after training sessions or bouts.
The Smart AI Boxing Band functions as a compact training tool that enables boxers to refine their technique, manage round pacing, and respond to real-time performance feedback. Additional technical details about the project are documented in Manivannan's full write-up.
Source: Arduino Blog
