# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Related Work
The Kalman filter algorithm then fuses the data collected by the sensors; the data are sent to the CNN for classification. The authors used the Keras model to design the CNN architecture trained on the wireless sensor data mining (WISDM) dataset. Thirty-six people collected the data through the smartphone with a sampling of 20 values for every person. The activity recognized were upstairs, downstairs, sitting, standing, and walking. The neural network was composed of two convolutional layers, two ReLu layers, two pooling layers, and two fully connected layers. In order to detect the exact position of the people, the system was integrated with an ultrasound network made by beacons and mobile nodes. The beacons are placed at the corners of a square \(4\times 4\times 3\) m\({}^{3}\) at the top of the ceiling while the patient wears the tag. The localization system is based on an RF synchronization between transmitter and receiver, an ultrasonic chirp signal for measurement of the distance, and the multilateration process to compute the distances. Distances are calculated by measuring the time of flight, the time elapsed from the transmitter to the receiver. The communication system uses the ANT protocol (wireless communication protocol of ANT wireless), which is not affected by interference in the transmission between different devices. The authors created a script to configure and establish the communication on the same Wi-Fi network between the server on which the patient's coordinates are recorded and the system board that represents the client to which the information on the exact position of the subject is to be transferred. The results showed that the accuracy of the recognition of the assets was greater than 99%. In contrast, the accuracy of the positioning was of the order of cm in consideration of the accuracy of the measurement that was equal to about 1.7 mm, which we can consider an accurate value as it is greater than the limit value obtainable with the Cramer-Rao formula (CRB) [90]. Table 5 shows a comparison of the systems analyzed. From the analysis of the solutions, we can observe no standard solution was adopted by the designers. Combining IPS with HAR improved prediction accuracy. The mainly applied technology was Wi-Fi with the use of inertial sensors. In addition, for the prediction of activities, the use of ML models that allow obtaining high values of accuracy was preferred. Figure 16 shows an integrated HAR and IPS system. Figure 16: HAR/IPS system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Adopted System** & **Technology** & **Technique** & **Accuracy** \\ \hline [PERSON] [72] & Inertial sensors of smartphone & PDR-BLE & EPBCM/HMM & 99\% \\ \hline [PERSON] [78] & Cameras and smart home sensors & Visual/Wi-Fi & Unsupervised model & 77\% \\ \hline [PERSON] [79] & Inertial sensors of smartphone & Fingerprints & ConVLSTM & 84\% \\ \hline [PERSON] [81] & RFID & Wi-Fi & KNN & Not declared \\ \hline [PERSON] [82] & UHF/RFID Landmark & Wi-Fi & KNN & 32 cm error \\ \hline [PERSON] [83] & Inertial sensors of smartphone & PDR & KNN & 99\% \\ \hline [PERSON] [84] & Inertial sensor & Wi-Fi fingerprints & C1D & \begin{tabular}{c} 88\% recognition, \\ 95\% localization \\ \end{tabular} \\ \hline [PERSON] [86] & Inertial–physiological sensor (ECG) & Bluetooth & DT/SVM/ANN & \
\begin{tabular}{c} 0.924–0.994 DT \\ 0.995–0.999 SVM \\ 0.839–0.917 ANM \\ \end{tabular} \\ \hline [PERSON] [87] & Anchors/mobile device & Wi-Fi & DT & 99\% \\ \hline [PERSON] [88] & MEMS/ultrasound & Wi-Fi & CNN &
\begin{tabular}{c} 99\% recognition \\ 1 cm localization \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of HAR-IPS systems.