Stress Detection via Sensor Translation

Stress increases the risk of several mental and physical health problems like anxiety, hypertension, and cardiovascular diseases. Better guidance and interventions towards mitigating the impact of stress can be provided if stress can be monitored continuously. The recent proliferation of wearable devices and their capability in measuring several physiological signals related to stress have created the opportunity to measure stress continuously in the wild. Wearable devices used to measure physiological signals are mostly placed on the wrist and the chest. Though currently chest sensors, with/without wrist sensors, provide better results in detecting stress than using wrist sensors only, chest devices are not as convenient and prevalent as wrist devices, particularly in the free-living context. In this paper, we present a solution to detect stress using wrist sensors that emulate the gold standard chest sensors. Data from wrist sensors are translated into the data from chest sensors, and the translated data is used for stress detection without requiring the users to wear any device on the chest. We evaluated our solution using a public dataset, and results show that our solution detects stress with accuracy comparable to the gold standard chest devices which are impractical for daily use.