This paper introduces the Proactive Assistance through action-Completion Estimation (PACE) framework, designed to enhance human-robot collaboration through real-time monitoring of human progress. PACE incorporates a novel method that combines Dynamic Time Warping (DTW) with correlation analysis to track human task progression from hand movements. PACE trains a reinforcement learning policy from limited demonstrations to generate a proactive assistance policy that synchronizes robotic actions with human activities, minimizing idle time and enhancing collaboration efficiency. We validate the framework through user studies involving 12 participants, showing significant improvements in interaction fluency, reduced waiting times, and positive user feedback compared to traditional methods.