Juefei Wang¹ and Andrew Park², ¹USA, ²California State Polytechnic University, USA

Resistance-training adoption remains low because certified coaching is costly, and poor form risks injury. RepSync tackles this gap with a US$30 ESP32-based wearable and a cross-platform Flutter app. A 50 Hz wrist-IMU feeds 128-sample windows into an on-device TorchScript CNN that classifies 18 lifts plus idle, while a finite-state machine counts reps and a rule-based engine scales sets using seven-day soreness and BMI. Key implementation challenges—BLE congestion and limited battery—were mitigated through Nordic-UART filtering, packet CRC, and adaptive connection intervals. In a crowded gym, the system achieved sub-200 ms end-to-end latency with <1 % packet loss; a 30-volunteer study recorded macro-F1 = 0.91 across all classes. Compared with WHOOP’s delayed HRV analytics and Apple Watch’s self-reported strength sessions, RepSync delivers real-time, rep-level feedback at one-fifth the hardware cost. Future work will add a forearm sensor for multi-joint resolution and replace the rule tree with reinforcement learning, but current results already offer an affordable, data-driven path to safer strength training.

Resistance training, Accelerometer, Flutter, Machine Learning, Bluetooth LE