## 3 Results  
For our experiments, we took into account two weeks where people slept with a smart device and monitored their sleep conditions in their daily life and as they had been doing it since before. Later, another two weeks while people have the sensor network with the proposed algorithm and their smart device for sleep monitoring in their room. We have separated the sample into men (50%) and women (50%). This is done to see if there is any difference in the network effect between genders. Indeed, in Figure 16, we observe the percentage improvement in sleep quality in the upper graph for men and the lower graph for women. We obtain that the increase in sleep quality for men is around 9%, and for women, it is around 15%. This result is curious, but it may be because women follow the algorithm's recommendations more closely, guided by the alerts in the sensors. This different result between men and women is interesting because, possibly, women are more strict in following the algorithm's indications at the end of the day. This leads us to think that the slope of sleep rhythm improvement in women may be caused by the implementation of the device measures. For example, if the person has several stops during the night (detected by the motion sensor), the algorithm routes the recommendations towards lower fluid intake. Another example is based on the fact that if the air quality and temperature sensors show unsuitable conditions for rest, the algorithm's recommendation may be to close some windows at night. This also happens with the abrupt change of levels in the noise sensor. We added the Table 4 with the questionnaire that was applied to the users before performing the experiments to detect primary diseases or basic health conditions. We have chosen a sample of 50 healthy people, non-smokers who do not have heart or lung conditions to avoid bias in the experiment. People who have answered \"yes\" to any of the questions in Table 4 have been discarded from the sample. Figures 17 and 18 show examples of actual testing in a user's dream application. Scenario A shows the person's sleep behavior without the sensor network in the person's room. Scenario B shows an example of the person's sleep performance with the sensor network in the person's room. We can observe the improvement of the person's sleep quality with the sensor network, even in the number of hours of sleep. \begin{table}
\begin{tabular}{l} \hline \hline
**Question** \\ \hline Are you diabetic? \\ Has any close relative (mother father, grandparents, or siblings) died suddenly before age 40 from a heart problem? \\ Do you have any heart diseases? \\ Do you have high blood pressure? \\ How often do you do physical exercise? \\ Have you felt dizzy when you exercise or play sports? \\ Have you ever had chest pain during exercise? \\ Have your nails or lips turned purple or bluish while exercising? \\ Have you felt that you cannot breathe after exercising? \\ Are you a smoker? \\ Do you have asthma? \\ Do you have exhaustion (tired) or lack of energy? \\ Does it present oxygenation above 90\%? \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Core questions on cardiovascular and respiratory conditions. Figure 16: Percentage of sleep improvement with the sensor network for men and for women. Figure 17: Example of a sleep improvement application with the sensor network for a user. Screenshots of the cell phone corresponds to no sensor network. One of the most accessible parameters to measure is the Heart Rate which, during sleep, is directly related to its quality. Since we are measuring the heart rate, it is beneficial to determine if there are any problems related to the heart and if the sensor network helps to maintain calm when sleeping. One of the most common sleep disorders in the cardiac field is sleep apnea. Thanks to smart sensors, it is easier to detect if the person presents some of their most frequent symptoms and if there is a difference when they sleep, monitored by the network of sensors implemented in the bedroom.