Traditionally, mood is assessed by either considering physiological data or smartphones-
based self-reports. Physiological data is objective and continuous, but difficult to be …

We present MoA 2, a context-aware smartphone app for the ambulatory assessment of
mood, tiredness and stress level. In principle, it has two features:(1) mood assessment and …

In ambulatory assessment, subjects are monitored in everyday life. Though, it is difficult to
unobtrusively assess information--eg about their context and affective state--which results in …

Modern sensing technology is becoming increasingly ubiquitous. Mobile phone sensing
data has been used in research to address health and wellbeing; but in the last years …

We introduce a system to measure individual happiness based on interpreting body sensors
on smartwatches. In our prototype system we use a Pebble smartwatch to track activity …

We report a first-of-its-kind smartphone software system, MoodScope, which infers the mood
of its user based on how the smartphone is used. Compared to smartphone sensors that …

Automatic recognition of user context is essential for a variety of emerging applications, such
as context-dependent content delivery, telemonitoring of medical patients, or quantified life …

We present an intelligent wearable system to monitor and predict mood states of elderly
people during their daily life activities. Our system is composed of a wristband to record …

We measure and predict states of Activation and Happiness using a body sensing
application connected to smartwatches. Through the sensors of commercially available …

Wearables equipped with pervasive sensors enable us to monitor physiological and
behavioral signals in our everyday life. We propose the WellAff system able to recognize …