# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Related Work
The model was ultimately tested in a realistic environment with a total of 3279 samples taken from 10 users to recognize the following activities: sitting to work at the PC, sitting to watch TV, lying on the couch, sitting in the kitchen, and sitting in the bathroom. The results showed that the three models, while presenting different accuracy values, all showed clear improvements with the information on the location. The DT went from 0.924 to 0.999, the SVM went from 0.995 to 0.999, and the NN went from 0.839 to 0.917. [PERSON] et al. [87] developed an interesting system based on wireless sensor networks (WSNs) to supervise patients within a nursing home. It provides two main features:  
* Monitoring of the patient's status. Various information is collected on the patient's status concerning standing, walking, supine, and prone activities. * Localization and tracking of patients. The exact knowledge of the patients allows a quick intervention of the assistants in case of need. The architecture of the system consists of the following elements: a personal localization and personal system (PLTS) module, a personal monitor system (PMS), and a network architecture (NA) composed of fixed nodes (AN) and mobile devices (MN) mounted on patients to provide information to be sent to an automated central controller. The PLTS module uses a localization algorithm based on the intensity of the received signal (RSS). The PMS uses a decision tree-based classifier (DT) to recognize acquired movements correctly. The PLTS module, to improve localization and tracking, uses particle filtering to \"smooth down\" the fluctuation of the RSSI sample. The personal monitoring algorithm is based on hierarchical DT, where the classification output is perfected at each tree level; general classifications are carried out in the highest levels with low-effort threshold decisions. In contrast, more detailed classifications are carried out in the lower levels. The algorithm uses signals from a biaxial accelerometer linked to the waist to classify the movement. A window-by-window classification scheme has been adopted in which the movement is classified by analyzing the data collected in a 1 s window. Once the two components of the acceleration have been separated, the acceleration signal is examined to see if the patient is in a state of movement or rest. Since the energy spent during movement is greater than the rest activity, the tests are carried out for a fixed threshold value that the authors have placed equal to 0.2 in their tests. The authors used the acceleration value to detect a possible fall, starting from the consideration that if the acceleration takes a value more significant than the normal one equal to 1 g, this value can indicate a critical event such as fall detection. Experimental values showed that the accuracy of the localization system was about 2-3 m with a node arrangement still approximately equal to 0.15 knots/m\({}^{2}\). As for the accuracy of the movements, the values were almost all close to 100. [PERSON] et al. [88] designed an innovative home care system for the elderly and pathological conditions. The system provides services to assist an integrated system for older adults. They developed the system architecture on an IoT platform capable of guaranteeing three functions:  
* Data collection connected to the patient activity such as inertial, physiological, environmental, and localization data;
* Design of a convolutional neural network for activity recognition;
* Identification of the exact elderly position. In order to verify the classification level of the AI model, the authors applied virtual reality technology [89]. The system's core was the STM32L475 microcontroller IoT node, which embeds a network of sensors such as an accelerometer, gyroscope, magnetometer, temperature sensor, and proximity sensor, which belongs to the MCU based on the core Arm Cortex-M4. It also integrates wireless connectivity such as Wi-Fi, NFC, BLE, and sub-GHz bands.