This piece of research is situated in the domain of multi-modal analytics. New commercial off the shelf wearables, such as smartwatches or wristbands, are becoming popular and increasingly used for fitness and wellness in a new trend known as the quantified-self movement. The sensors included in these devices (e.g. accelerometer, heart rate) in conjunction with data analytics algorithms are used to provide information such as steps walked, calories consumed, etc. The main goal of this piece of research is to check if new wearable technologies could be used to estimate sleep indicators in an automatic way. The available medical literature proposes several sleep-related features and methods to calculate them involving direct user observation, interviews or specific medical instrumentation. Off the shelf wearable vendors also provide some sleep indicators, such as the sleep duration, the number of awakes or the time to fall asleep. Taking as a reference the results and methods described in the medical literature and the data available in commercial off the shelf wearables, we propose new sleep indicators offering a greater interpretative value: sleep quality, sleepiness level, chronotype. The results obtained after initial experiments demonstrate the feasibility of this approach to be applied in real contexts. Eventually, we plan to apply these solutions to support educational scenarios related to self-regulated learning and teaching support.Agencia Estatal de Investigación | Ref. TIN2016-80515-RXunta de Galicia | Ref. GRC2013-006Universidade de Vig
