## 4 Results
The five metrics with the highest chi-square values (HRV, T, S, TST, and REMS) were normalized to a range of 0 to 1 using the MinMax algorithm (see Equation (2)) and subsequently converted to four-level categorical variables. The purpose of this step is to find association patterns using the FP-growth frequent pattern mining algorithm [41] among the five variables that had the best performance in the chi-square test. The process of transforming quantitative variables into qualitative variables is called discretization, and it is necessary to apply algorithms that detect categorical associations. For this paper, the FP-growth frequent pattern algorithm identifies when categories of different metrics occur together in a group of participants in the experiment. The minimum and maximum values for each metric are chosen from the values collected with and without the sensor network to ensure they are comparable. Finally, to assign a categorical level to each value of each variable, the following process is followed: if the value is equal to or less than 0.25, it is assigned the low level; if the value is greater than 0.25 and less than or equal to 0.50, it is assigned the medium-low level; if the value is greater than 0.50 and less than or equal to 0.75, it is assigned the medium-high level; and if the value is greater than 0.75, it is assigned the high level. \[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{2}\]  
To search for patterns in the form of rules with an antecedent and consequent, the FP-growth frequent pattern algorithm was used. FP-growth frequent pattern identifies the most frequent occurrences among the categorical levels of performance of the sleep metrics measured in the experiment. Significant association rules were identified using a minimum support of 15% and a confidence of 60% across the HRV, T, S, TST, and REMS metrics. A support of 15% means that at least 15% of the people involved in the experiment have the found pattern. A confidence of 60% means that at least 60% of the people who have the antecedent of the rule also have the consequent. The following patterns were found with and without the use of the sensor network:  
* Without the use of the sensor network, 63.3% of the people who presented high-level temperature had medium-low snoring, equivalent to 31 people. On the other hand, with the use of the sensor network, 61.1% of the people who presented medium-high temperature had medium-low snoring, equivalent to 22 people. This pair of patterns may indicate that people with medium-low snoring lowered the temperature of their environment following the algorithm's recommendations. * Without the use of the sensor network, 61.7% of the people who presented low HRV had medium-low REM sleep, equivalent to 29 people. On the other hand, with the use of the sensor network, 62.9% of the people who presented high HRV had high REM sleep, equivalent to 22 people. It was also observed that with the sensor network, 68.2% of the people with medium-low HRV had medium-high REM sleep, equivalent to 15 people. These three patterns may indicate that people with low levels of HRV and REM sleep improved their HRV and REM metrics by following the algorithm's recommendations. * Without the use of the sensor network, 61.7% of the people who presented low HRV had medium-low total sleep, equivalent to 29 people. This pattern may indicate that low HRV negatively impacts people's total hours of sleep. * Without the use of the sensor network, 62.5% of the people who presented medium-high temperature had medium-low total sleep, equivalent to 25 people. On the other hand, with the use of the sensor network, a pattern with the same metrics in a different order appeared: 61.3% of the people who had high total sleep presented medium-low temperature, equivalent to 19 people. This pair of patterns may indicate that by lowering the room temperature, following the algorithm's recommendations, people increased their total hours of sleep.