Abbott Freestyle Libre: this groundbreaking device allows accurate and affordable 24-hour glucose monitoring. It also makes it possible to register meals as carbohydrate portions (CP) (one CP is equal to 10 g of carbohydrate) and the insulin dosage (units)
Fitbit Charge HR: this is an advanced tracking wristband that gives an automatic, continuous heart rate and activity tracking during workouts and throughout the day. It records number of steps, distance, floors climbed, and sleep time. It stays connected wirelessly and can be synchronized with a smartphone and computer to monitor trends. With this smartband the following features were recorded: steps, heart rate, and sleep.
In order to explore the possibilities of the monitoring described above, a case study was carried out. For that, the following devices were used: 
Patients also recorded their meal intake and schedule, indicating periods of work time or playing sports.
Although we listed other features (temperature, blood pressure, and perspiration), they were not monitored due to limitations in the chosen devices, and the additional devices would not have been able to be acquired easily and/or were not affordable. As this was a preliminary study, we considered the measured features with the available tools to be relevant enough to make a first evaluation of the monitoring experience.
The patient studied was a healthy man, 25 years old, DM1 for 12 years, and with diabetes under control. We considered this individual to be a good example for a preliminary study. He was under continuous monitoring for 14 days. In this period, he continued with his daily routine, including work, sports, dietary habits, and insulin dosages. The monitoring was carried out in a passive way, so there was no intervention in his diabetes treatment. A physician checked his medical condition daily, with the aim of assuring the safety of the process.
Data were collected every 5 minutes, although it could have been recorded with a higher frequency. At this rate, the datasheet comprises more than 24,000 measurements; the level of detail achieved is really high. However, in terms of data storage, it is easily affordable.
 Figure 2 shows the results from a few days and represents an example of the results that can be achieved.
As can be observed, the data provides more information than a simple glucose graph, and, therefore, caregivers can better understand the phenomena that influence the patient's status. With just a glance, it is now possible to understand glycemia evolution just by looking at the relationship with physical activity, for example.
Furthermore, all of the data can easily be transmitted to a smartphone or to the Cloud because of the connections of the CGM and smartbands, or other medical devices, mainly via Bluetooth. With all of these values, the possibilities that arise using machine learning techniques are boundless. It is in this context that the derived "on-board" variables are especially meaningful and have sense to be generated, that this idea encloses aggregative significance, and it is in this situation where the power of this perspective can be completely exploited.