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-logging. Although not explicitly observable as, e.g., activities, an important aspect towards understanding user context lies in the affective state of mood.While significant work has been done to assess mood, most approaches require the use of customized sensors and controlled laboratory settings.In this work, we engineer a recognition pipeline to recognize daily activities from commercially popularized wearable electronics.