AI-based Detection of Signs of Depression from Physiological Data obtained from Health Trackers

According to the National Institute of Mental Health, Major Depressive Disorder affected an estimated 21.0 million American adults in 2020, which represents 8.4% of the U.S. population aged 18 or older in a given year. Even though the percentage is substantial, it reflects only the diagnosed cases. Most depression cases remain undiagnosed and thus untreated. Real-time monitoring of physiological indicators of depression using wearable health monitoring devices can help increase the chances of early detection and eventual treatment. In this research, various Artificial Intelligence algorithms are developed to look for signs of stress and anomalies in activity patterns from the data captured by wearable health devices. The Random Forest algorithm performed well in detecting depression from users' activity levels, while the K-Nearest Neighbours algorithm detected stress, one of the key indicators of depression, with an accuracy of 96.2% from Heart Rate variability. This research takes advantage of real-time access to one's physiological data to minimize the number of undiagnosed depression cases.