# Pattern Recognition in Older Adults' Activities of Daily Living
## 3 Materials and Methods
### Pattern Recognition
For example, this first type of message can be visible with the phrase _At 03:00 AM, the usual location has been changed from the \"Bedroom\" to the \"Bathroom\"_. This means that the presence in the \"Bedroom\" has become a pattern at that hour of the day; moreover, the \"Bathroom\" is also trending on that specific day. Outliers were detected in your patient today

LOW:

* At 03:00 AM, the usual location has been changed from the 'Bedroom' to the 'Bathroom'
* At 08:00 AM, the usual location has been changed from the 'Bedroom' to the 'Living Room'
* At 10:00 AM, the usual location has been changed from the 'Kitchen' to the 'Living Room'
* At 12:00 PM, the usual location has been increased by 'Kitchen' at the expense of 'None I Outside'
* At 02:00 PM, the usual location has been increased by 'Living Room' at the expense of 'Bathroom'

MEDIUM:

* At 03:00 PM, the usual location has been changed from the 'Living Room' to the 'None I Outside'

HIGH:

* At 11:00 AM, the usual location has been changed from the 'Kitchen' to the 'None I Outside'

This is an automatic email, please do not reply

The second message corresponds to the fact that a compartment is well defined but not significantly so. So, when there is a very high probability of that same division, it can generate a message indicating that the likelihood of that compartment has been increased even more. This second type of message will generally always appear at the \"low\" level of outliers, as it is rare for there to be a situation in which there is a time when there is not at least one well-defined compartment at that time, which at least makes it possible to analyze which the probability of frequenting that space is excellent. For example, in the generated email, we can see a message of this type: \"At 12:00 PM, the usual location has been increased by 'Kitchen' to the 'Living Room\". In this way, the email manages to be quite descriptive of what it detected, helping whoever is reading what is happening that day. Figure 10:2 Example of emails sent if outliers are detected. Figure 10:3 Number of outliers detected during the experimentation phase. ## 5 Conclusions

Pattern recognition was successfully achieved in all compartments through a series of analyses. The identification of movement patterns was confirmed through the different analysis, accumulated data, percentiles, probability, and the outlier detection mechanism. Even though the dataset may contain irregularities in the records, the model demonstrated the ability to adapt to the identified patterns. The model's ability to adapt and recognize the identified patterns highlights its robustness and reliability. The accumulated data were crucial in establishing movement patterns within the compartments. By analyzing the cumulative data over time, we could discern consistent trends and understand how activities unfolded within each area. Percentiles provided further insight into the distribution of values within the dataset. This allowed for a more nuanced understanding of the variations in movement patterns and helped identify activities that deviated from the norm. The probability analysis significantly determined the likelihood of specific events or actions occurring within the compartments. By assigning probabilities to various scenarios, we could quantify the occurrence of particular behaviors and validate the identified patterns. Furthermore, the outlier detection mechanism was another validation tool for the identified movement patterns. The mechanism ensured that any irregularities or unexpected behaviors were accounted for and adequately addressed by detecting and flagging anomalies or outliers. Identifying outliers and studying patterns are distinct processes, but they complement each other in ensuring the correct interpretation of the information. We can validate the identified patterns and comprehensively understand the data by analyzing both aspects. Limitations of the method occur when the number of beacons increases and in the data processing step.