## 6 Conclusions  
In this paper, we proposed a solution for identifying the frequent behavioral patterns part of the daily routines of a person by considering, as relevant features, the sequence in which the activities are performed as well as the time interval and the duration corresponding to each activity in the sequence. Genetic algorithms are used to identify the sequence of activities that occur on the vast majority of monitored days, and a method based on the standard deviation is used to calculate the time interval and duration corresponding to each activity in the sequence. To avoid the premature convergence of the genetic algorithm and to maintain a better balance between exploration and exploitation, a strategy based on the dynamic application of crossover and mutation operators has been used. The selection of crossover and mutation operators that were applied in the dynamic selection strategy was made taking into consideration the encoding strategy of an individual that we used as well as the advantages that these operators offer as reported in the specialized literature. The approach has been tested on a dataset of ten patients with age between 75 and 80 who suffer from cardiovascular diseases. For each person, a different search space was created based on the duration intervals of the daily activities and the transition probabilities of the activities, on which the genetic algorithm was applied to extract the daily routine corresponding to that person. To determine how the performance of the genetic algorithm is influenced by the variations of the control parameters, we have performed a sensitive analysis. We have also compared our approach with other state-of-the-art approaches, to assess its performance in terms of the accuracy of the results. The obtained results demonstrate that our approach can provide good results even when working with smaller amounts of data, unlike existing classifier-based approaches that require large amounts of annotated data to achieve good results. In future work, we plan to implement a distributed version that runs several genetic algorithm instances in parallel, aggregates the best solution provided by each instance, and outputs the best solution out of this set. This may improve the accuracy of detecting the routine of the person. Moreover, to better capture the behavior of a person in a certain context, we intend to encode in the individual representation contextual information as well \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coefficients** & **Standard Error** & \(p\)**-Value** \\ \hline Intercept & 0.70711862 & 0.010992243 & \(3.52\times 10^{-59}\) \\ Size of population & 0.00311164 & 0.000179602 & \(1.19\times 10^{-25}\) \\ Number of generations & 0.00089019 & \(9.69964\times 10^{-5}\) & \(3.19\times 10^{-13}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression analysis table. as additional information regarding the location in which the activities are performed or the frequency of the activities. Finally, we will look into newer and promising metaheuristics such as Whale or Harris hawks optimization algorithms that may improve the results of daily routine detection. Conceptualization, V.R.C. and C.B.P. ; methodology, V.R.C. and T.C. ; software, D.D. ; validation, C.B.P., D.D. and V.R.C. ; formal analysis, C.B.P. ; investigation, I.A. and D.D. ; writing--original draft preparation, V.R.C., T.C. and C.B.P. ; writing--review and editing, I.A. and I.S. ; visualization, I.A. and T.C. ; Supervision, I.S. ; funding acquisition, T.C. and I.A. All authors have read and agreed to the published version of the manuscript. This research was funded by UEFISCDI Romania and the European Union AAL Programme with co-funding from the Horizon 2020 research and innovation programme, grant number AAL264/2021 engAGE, AAL159/2020 H2 HCare, and AAL162/2020 ReMember-Me. Not applicable. Data Availability Statement: Data sharing is not applicable to this article.