Motion Recognition for Fitness on Android Wear
Article Summary
Edward Dale from Freeletics shows how a simple peak detection algorithm can count pushup reps on Android Wear with surprising accuracy. The secret? Freeletics-style pushups include a hand lift that creates a perfect acceleration spike.
This deep dive from the Freeletics engineering team tackles the challenge of automatically counting exercise repetitions on wearables. Rather than solving the broader problem of recognizing any exercise, they focus on a constrained use case: counting reps of a known exercise in real-time using accelerometer data.
Key Takeaways
- Peakdet algorithm counts pushups with zero transcendental functions for battery efficiency
- Hand lift in Freeletics pushups creates measurable 40+ m/s² acceleration spikes
- Delta parameter tuning is critical: wrong value doubles false positives
- Sensor sampling at 15Hz balances accuracy with wearable battery constraints
- Online algorithms process data as it arrives, no post-processing needed
A lightweight peak detection algorithm running at 15Hz can accurately count pushup reps on Android Wear by detecting hand-lift acceleration spikes, proving that constrained motion recognition problems don't need complex ML solutions.
