This research addresses exercise-related injuries by modeling individual physical capacity rather than relying on population averages. Using physiological and biomechanical data combined with machine learning, it aims to create personalized, dynamic thresholds for training. The goal is to prevent injury by aligning workload with real-time capacity, improving safety and long-term fitness outcomes.

This research develops an electrochemical sensor to continuously monitor stress by detecting cortisol, a key stress hormone. Using DNA aptamers and nanostructured electrodes, the sensor overcomes traditional detection limits, improving signal strength and durability. The technology offers a noninvasive method for long-term stress tracking to support prevention and treatment.