A research team from the University of Arizona Center for Health Sciences has recently achieved two significant research outcomes by combining wearable devices with artificial intelligence technology, innovating breakthroughs in health monitoring. The related studies were published in BMC Pregnancy and Childbirth and the International Journal of Environmental Research and Public Health, respectively.

In the labor prediction study, the team developed an AI system based on deep neural networks. Biomedical informatics expert Dr. Shravan Aras introduced: "We used wearable ring sensors to collect body temperature data every minute, and the predictive model can identify signs of natural labor with 79% accuracy 7 days in advance." The system includes a multi-layer algorithmic structure that simulates human brain information processing mechanisms, performing excellently in a 4.6-day prediction window. Currently, the team is expanding the research scale to further enhance the model's clinical applicability.

The other study focused on stress monitoring. By analyzing physiological data from walkers on "Green Roads" and urban roads, the team found that natural environments more effectively reduce stress hormone levels. The research showed that after 20 minutes of walking on green roads, participants' cortisol levels dropped significantly more than in urban settings. Dr. Aras pointed out: "There are significant individual differences in heart rate variability responses, providing a direction for personalized stress management."

The research team is extending AI technology to new areas such as sweat biomarker analysis. Dr. Aras stated: "Our goal is to develop intelligent monitoring systems capable of early warning for asymptomatic diseases. AI's autonomous learning capabilities create entirely new possibilities for proactive health management."