Researchers at the University of Nottingham Ningbo China are developing an AI-powered system that relies on wearable sensors and VR-based emotional tasks to help detect depression earlier and more objectively than traditional clinical interviews.

For decades, depression diagnosis has largely depended on patient self-reports and clinician-administered questionnaires, which can be influenced by memory bias, communication difficulties, and subjective interpretation. This becomes especially challenging in early-stage cases, when symptoms are subtle and patients may struggle to articulate their condition.

To address this gap, a multidisciplinary team at the university has built a prototype system that combines artificial intelligence with multimodal physiological signals, including eye movement, skin conductance, heart rate, and brain activity. The setup includes a VR headset, a fingertip sensor, and an ear-worn heart rate monitor.

Inside a controlled virtual environment, participants are exposed to carefully selected emotional stimuli such as images, sounds, and videos designed to evoke happiness, sadness, or calmness. While they respond to these cues, the system continuously records physiological changes.

Researchers found that individuals with depression often show distinct patterns, such as prolonged pupil dilation in response to negative stimuli and reduced prefrontal brain activity during reward anticipation.

The project, led by Professor He Xiangjian, Associate Professor Sun Xu, and PhD candidate Liu Jing, has already collected clinical data in collaboration with the First Affiliated Hospital of Ningbo University. Early models reportedly show more than 90 percent sensitivity compared with clinical diagnoses.

Researchers say the system is not intended to replace psychiatrists, but to function as an objective physiological report, similar to a blood test. With further refinement and larger datasets, the team hopes to improve accuracy and expand its applications, potentially enabling at-home screening and earlier intervention in emerging cases of depression.