# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 3 Outdoor Activity Recognition System
### POI Identification Mechanism  
In this section, we are going to introduce the developed POI identification system. This module gets as input the daily points of the user that the used mobile application has been able to gather. The application collects location information every 1 min, therefore, in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\begin{tabular}{c} **POI/Clusters** \\ **Detection** \\ \end{tabular} } & \begin{tabular}{c} **Semantic Location** \\ **(SL) or Meaningful** \\ **Location** \\ \end{tabular} & \begin{tabular}{c} **POI + SL in** \\ **Chain** \\ \end{tabular} & \begin{tabular}{c} **Indoor** \\ **Location** \\ **Prediction** \\ \end{tabular} & \begin{tabular}{c} **Indoor** \\ **Location** \\ **Estimation** \\ \end{tabular} & \
\begin{tabular}{c} **User** \\ **Activity** \\ **Prediction** \\ \end{tabular} \\ \hline [PERSON] et al. [36] & No & No & No & No & Yes, but with accelerometers, compasses, and gyroscopes & No \\ \hline [PERSON] et al. [37] & No & No & No & No &
\begin{tabular}{c} Yes, they \\ associate \\ meaningful \\ places to \\ coordinates \\ through \\ ontologies \\ \end{tabular} & No \\ \hline [PERSON] et al. [38] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [39] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. (2018) & No & No & No & No & Yes & No \\ \hline [PERSON] et al. (2019) & No & No & No & No & Yes & No \\ \hline
[41] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [42] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [43] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [44] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [45] & No & No & No & No & Yes & No \\ \hline [PERSON] [40] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. (2010) [47] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. (2011) [46] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. (2011) [48] & No & No & No & No &
\begin{tabular}{c} Yes, through \\ RFID \\ technology. \\ \end{tabular} & Yes, but only to detect falls \\ \hline [PERSON] et al. [51] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [52] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [53] & No & No & No & No & Yes, with BLE 5.1 & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._a single day we would have to process 1440 sequential GPS coordinates (60 min \(\times\) 24 h). Once this module receives the daily location data, it pre-processes the raw data to handle any GPS inaccuracies and time intervals without any data (the smartphone was switched off or without internet connection for various days). Then, we take advantage of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [54] clustering algorithm, adapting it to this specific task. Finally, we start the creation of a location-based daily activity timeline for the user. This module has gone through two iterations that we are going to explain in the following sections in order to clarify the reasoning behind this decision, giving some insight about how and why we have implemented the proposed changes in the second iteration.