Stress is widely recognized as a major contributor to mental and physical health challenges, yet accurate measurement of it remains difficult. Many common approaches are either subjective, relying on self-reporting, or limited to one-time snapshots that fail to capture how stress changes over time.

The UC Irvine team designed SQC-SAS to remedy this by integrating multimodal biosensing, wireless operation and machine learning into a wearable device intended for objectiveness and ease of use.