I. Obermiller and S. I. Ahamed 5 (a) Assessing a night’s sleep (b) In-depth look at a night’s sleep (c) Iconic, easy to use menu (d) Detailed graph and summary Figure 3: Some screenshots of our implementation. temperature and humidity because it may be covered by blankets or moved frequently during sleep. 4.2. Daytime data collection The major aspect that diﬀerentiates our approach from the others is that we seek to continuously monitor the person’s lifestyle and habits that factor into the condition of her sleep. This allows us to make a more accurate assessment of factors that inﬂuence sleep and weigh the data collected during the night accordingly. Since many sleep assessments rely on survey data to collect habits and lifestyle information from patients, most of which are less than accurate, an easy to use system that gathers information as it happens could be a valuable resource for doctors diagnosing a sleep disorder. Some of the factors that will be monitored throughout the day are eating and drinking patterns (caﬀeine, alcohol, heavy/light meals, snacking, drinking before bed), exercise patterns (frequency, time of day, diﬃculty), and lifestyle (smoking, relaxing or stimulating activities before bedtime, sleeping schedule, amount of fatigue, and stress level). This data could be entered in a variety of ways, but must be convenient for the user or it will not get entered at all. Therefore, the system will rely on everyday technology that the user already has access to. For instance, she could enter information through a text message or voice automated prompt on a cell phone. Or she could go to a website and ﬁll out a quick survey form. If none of the above is a viable option, the user could keep track in a paper log and enter the data manually later in the day. 4.3. Data analysis All of the data collected throughout the day and night is relayed to a central location, which could be on the user’s