# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Related Work
Although there are numerous applications in which the Wi-Fi CSI has been studied for human activity detection, there is a lack of evidence that the joint study of activity recognition and internal localization has been addressed. They proposed a novel one-dimensional convolutional neural network (C1D) based on ResNet [85], comprising two branches, one for recognizing activities and the other for internal localization. To verify the validity of the model, they used the IEEE 802.11n protocol with two USRPs (universal software radio peripherals) and with a dataset appropriately prepared for a potential human-computer interaction application. The dataset contained six hand gestures: hand up, hand down, left hand, right hand, hand circle, and hand cross. The test was carried out by a volunteer who repeated these activities 15 times in each 16 different locations, realizing 1394 samples (after excluding the invalid data). This produced different Wi-Fi fingerprints when performing the same activity but at different locations and activities in one location. The system contains two sets of personal computers and USRPs, which work as Wi-Fi transmitters and receivers. Lastly, an Ettus clock synchronizes the two sets. The experimental results confirmed the model's validity by reporting accuracy values of 88.13% on the recognition of the activity and 95.68% on indoor localization. [PERSON] et al. [86] proposed a well-organized work in which they designed a system with body information, vital signs, and the user's internal position aggregated to improve the recognition of activities. The proposed model integrates wearable sensors capable of monitoring cardiac activity (electrocardiogram, ECG), body posture, and acceleration of the lumbar area and an environmental localization network capable of estimating the user's position. The localization network is implemented in such a way as to locate both range-free and range based. From an architectural point of view, the system consists of a hardware layer, a communication layer, and a data processing module. The hardware component consists of three sensors: an STMicroelectronics iNEMO-M1 inertial sensor, a Zephyr Bioharness (BH3) Bluetooth chest stray that can monitor the ECG, and ZigBee wireless location sensors. The network configuration consists of a ZigBee coordinator (ZC), a data logger (DL), a wearable mobile node (MN) equipped with an omnidirectional antenna, and a set of ZigBee anchors (ZAs). The ZAs are installed in a fixed position in the experimental site on the walls and inside the furniture to detect the location within the rooms accurately and is equipped with 60\({}^{\circ}\) sectoral antennas to improve the SNR ratio of RSS signals. Each ZA calculates the user's position as a function of the RSS signal exchanged between the nodes and transmitted this value to the DL. The communication is based on the ZigBee module for localization and the Bluetooth module for ECG. As far as the processing system is concerned, a PC collects all the data through four modules. One module for capturing ECG data, one to collect data from inertial sensors, two to collect RSS data from DL, and another to calculate the user's position. Three supervised machine learning algorithms were used for the classification: decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). Two classification models were created for each algorithm, one containing information about the user's location to assess whether the user could improve accuracy and the other without location information. Two techniques were used to validate the models: fivefold cross-validation (5 CFV) and LOSO. The training dataset was created using nine subjects, while the test dataset included an unknown subject to testing how good the model is in the presence of new data.