NTU scientists develop predictive tech to detect depression

The machine learning-enabled Ycogni model detects a person's risk of depression by analysing their physical activity, sleep patterns, and circadian rhythms, whose data are acquired from wearable devices measuring steps, heart rate, energy, and sleep. In Singapore, the pandemic has led to an increase in mental health cases, including depression. Based on research conducted by the Institute of Mental Health, around 13% of over a thousand adults have reported experiencing symptoms of depression or anxiety since the start of the lockdowns. The researchers also believe that their study can be used as a basis for using wearable tech to help improve people's mental well-being, according to Georgios Christopoulos, an associate professor at NTU Nanyang Business School who also co-led the study Aside from detecting risks of depression, the study has also successfully associated certain behavioural patterns among participants to depressive symptoms, like feelings of helplessness and hopelessness, loss of interest in daily activities, and changes in appetite or weight.