LiftIQ: A Wearable IMU-Based Connected Health System for Real-Time Strength Training Monitoring

Abstract

Improper technique in strength training is a common cause of injury, yet most wearable fitness devices provide limited support for monitoring resistance exercises. We present LiftIQ, a real-time strength training monitoring system that integrates wearable inertial measurement unit (IMU) sensors with signal processing algorithms. The system uses Symbolic Aggregate approXimation (SAX) for motion segmentation and Dynamic Time Warping (DTW) for repetition alignment and analysis. LiftIQ extracts biomechanical features such as range of motion and velocity to provide real-time auditory and visual feedback, along with a gamification system to enhance user engagement. Preliminary results demonstrate reliable repetition segmentation and real-time operation under varying movement speeds. These findings suggest that LiftIQ supports the connected health vision of continuous, unobtrusive monitoring and can enable more accessible, feedback-driven strength training guidance across diverse user populations. Future work will expand coverage to additional exercises, conduct larger-scale validation studies, and integrate AI-driven fatigue detection and personalized training adjustment.