Abstract

Depression is amongst the most prevalent health problems worldwide. Despite the magnitude of this global health problem, there are currently no methods for non-invasive, objective and accurate measurement of the level of clinical depression, reaction to a treatment or symptoms of deteriorating mental health. The current standard for diagnosis is still based on subjective clinical rating scales such as the Hamilton Depression Rating Scale, derived from methods that were developed in the early 1960s. While the efficacy of these scales has been proven in medically diagnosing depression, they have their drawbacks: they are a potential source of subjectivity in the diagnosis and they require attendance of a physician. Given recent advances in wearable sensors and with the widespread use of smartphones there is an opportunity to quantitatively and accurately assess depression state by using Affective Sensing methods, analysis of speech patterns and behaviour not only in a clinical environment but also during daily life activities. Dr Szymon Fedor will present his research results in diagnosing mental health disorders on the basis of long-term physiological measurements. He will also show how depression state can be assessed in the future using long-term physiologic and behavioural measures collected with wearable sensors and a mobile phone.