### Telemedicine
Nonetheless, the proposed system uses uncomfortable sensors, and some important features are not measured, like glycemia. Furthermore, it is not able to deal with risky situations. In fact, in diabetes management, this is an important issue, mainly due to the risk that is involved in a hypoglycemia. In [21], an alarm-based mobile diabetes management system (ADMAN) is proposed. By using remote monitoring, it is aimed at improving diabetes control in elderly patients. Although it includes an emergency system, most of the features are not monitored and there is no prediction of future glycemia levels. Other good examples on data management using smartphones are iBGSTAR(r), manufactured by Sanofi (Paris, France), in combination with the Diabetes Manager app, also from Sanofi, or the G5 Mobile CGM System by Dexcom Inc. (San Diego, CA, USA). The IoT is proposed as a good environment for diabetes management in [22]. Smartphones are proposed for remote collection and monitoring of data, with feedback for the patient and medical caregivers. Although such work is a complete proposal, some of the features are not monitored, and, what is more, CGM is not included. In another vein, the proposed model tries to modify, through advice, the patient's insulin dosage, but it should be noted that, without 24-h monitoring, this could not be carried out 100% successfully. Advancements in cloud computing are proposed in [23] for diabetes control. In this case, the cloud provides an easy way to exchange information, but unfortunately full monitoring and management of emergency situations is missing. On the other hand, it should be mentioned another approach, called COMMODITY12 [7], which is presented as a personal-health system to assist diabetic patients. This proposition consists of wearable and portable devices that acquire vital body signs, which are interpreted by algorithms based on expert knowledge. Active feedback goes directly to the patient or health professionals. Although it is an interesting proposal that includes physical activity and heart rate, it could be completed with other features from biosensors. Expert knowledge is used to interpret data, but the potential of other machine learning methods may be bigger; for instance, they may be able to predict glycemia or to make autonomous decisions. These tasks are not expected in this system, not even to avoid hypoglycemia. Lastly, the lack of inclusion of CGM in the monitoring, which should be an essential part, is an omission that needs to be rectified. A similar weakness can be found in [24]. Although the development of a BAN is complete, including others features that are usually non-considered (like body temperature, for example) and there is an online display to offer information, there should be a generation of knowledge. Finally, it is necessary to deal with the acceptance level of telemedicine. A continuous monitoring, recording personal data, and a complete characterization of the patient could be considered as an interference in private life. This could lead to an attitude of rejection from the user. These issues are compiled in [25]. In this work, it is explained that variables, like age, gender, and previous experiences with telemedicine can modulate its level of acceptance. So, after this review, we can conclude that, in order to develop a complete DM1 management system, it is necessary for a BAN to collect data, which should include glycemia and insulin, but also other kind of relevant features, like heart rate, physical activity, or slept hours. Moreover, a smartphone is a cornerstone to play the role of a Body Gateway, and also could provide enhanced information thanks to some software applications. Finally, IoT and Cloud Computing will allow for remote management, and the entire system must provide auto-adjustment, decision support, and advice to the patient, when considering machine learning algorithms or deep learning procedures.