# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Typology** & **Advantages** & **Disadvantages** & **Accuracy** \\ \hline Visual-sensor & \begin{tabular}{c} Ease of use, ease of analysis from images, \\ data reliability, alternative to multiple \\ sensory devices, \\ \end{tabular} & \begin{tabular}{c} Privacy, sensitive to environmental \\ conditions, higher cost, increased \\ processing power, longer processing time \\ \end{tabular} & 99\% \\ \hline Non-visual sensors & \begin{tabular}{c} Detection of any information about \\ behavior, no privacy issues, lower cost, less \\ processing power, lower power \\ consumption, less processing time \\ \end{tabular} & \begin{tabular}{c} Need for a large set of sensors, data \\ reliability, system vulnerability due to \\ sensor malfunction, lower accuracy values \\ \end{tabular} & 70–80\% \\ \hline Multimodal sensor & \
\begin{tabular}{c} Suitable for the collection of data of \\ different nature, lightweight devices, lower \\ power consumption, less processing time \\ \end{tabular} &
\begin{tabular}{c} Need for multiple sensors, acceptance \\ issues, need to wear sensors, efficient \\ fusion algorithms \\ \end{tabular} & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of HAR technologies. Figure 15: HAR system. intervals, and inferences to validate hypotheses. Statistical data operate on smaller volumes of data than those used by ML models. The accuracy of statistical models is lower than that of ML models due to the impossibility of creating complex relationships between data. Being complementary, the tendency is to make the two systems coexist. In machine learning, the federated learning model represents an innovative approach within HAR systems [71]. It represents a machine learning technique that allows the formation of predictive models by training an algorithm on multiple decentralized edge devices or servers that contain local data samples without exchanging them. Furthermore, it has the peculiarity of using decentralized private data without centralized collection. Federated learning is a machine learning technique in which the model is created without data sharing between users. The model can be downloaded by each user with their mobile device and updated with their local data. Local updates are then aggregated into the global model through an iterative process. This technology helps overcome all issues associated with privacy, data security, and access authorization. A peculiarity of this technology is the possibility of using different signal transmissions for the same type of activity. The need for frequent communication between nodes during the process requires sufficient computing power and memory as well as high-bandwidth connections to exchange model parameters. To improve localization prediction subject to uncertainties due to the variables at play, [PERSON] et al. [72] implemented an accurate compensation mechanism on a combined PDR and BLE positioning system. The hidden Markov model was used for recognition of activities. The approach by correcting the involuntary acceleration of the body and the distortion of the magnetic sensor accurately determines the speed and position. Taking into account the characteristics of positioning systems and the performance needs of a HAR system, it follows that integrated monitoring, localization, and tracking system should have the following characteristics: low power consumption, high accuracy, sufficient coverage, easy scalability, short return time, less computation, low cost, noncomplex infrastructure, and insensitivity to obstacles. The solution must have the right compromise about the design needs; there is currently no technology that meets all the characteristics mentioned.