# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Intelligent Metasurfaces
Exciting applications in which the presence of intelligent surfaces is fundamental are the creation of holograms and the creation of so-called invisibility cloaks, thanks to their ultrathin structure properties, programmability, and intelligence. The growing need for wireless information transfer has stimulated research to identify solutions that can implement the efficiency of such networks. For example, metasurface-assisted wireless communications improve performance. Two solutions are possible: one in which radio signals, which are emitted by the transmitter and dispersed in space, can be retrieved and directed toward the desired user improving the SNR ratio, and the other in which additional information is encoded by the intelligent surfaces and transferred to users. Three new architectures have been identified:  
_Non-modulated metasurface backscatter communication (NMMBC)_ systems function as conventional wireless systems in which there is an RF vector to transport information and a mixer in which the modulation and demodulation of the signal are carried out. The presence of the intelligent metasurface can be considered an extension of antenna arrays in conventional systems; it serves, in practice, to increase the number of information channels. _Modulated metasurface backscatter communication (MMBC)_ systems can be applied to overcome the problems related to the possible interception of information distributed in space without radiation directions, typical of the previous architecture. In these systems, the intelligent metasurface that carries the information directly modulates the radio signal coming from RF, simultaneously playing the role of mixer and antenna. _Ambient modulated metasurface backscatter communication (AMMBC)_ systems have no need for dedicated RF sources or a new frequency spectrum because the emitter can be an environmental RF source of the type of TV tower, Bluetooth, cellular base stations, or Wi-Fi. The metasurface plays three roles: mixer, antenna, and energy collection collector. Ithas the advantage over traditional ambient backscatter systems, ensuring secure multiuser communications and higher transmission speeds. Intelligent surfaces have also proven efficient for intelligent sensing by enabling the connection between the physical and digital worlds. Three techniques are possible:  
* Nearly digital-computing-free intelligent sensing. A reprogrammable deep imager represents a usable model in which the metasurface is trained with data that can be obtained from the principal components analysis (PCA). With this method, it is possible to obtain the radiation patterns as required by machine learning at the physical level. The intelligent metasurface, in this case, performs a physical calculation function by producing the PCA characteristics from the input of the raw data as in an analog calculation. Therefore, the detection is carried out almost without digital processing. * Hybrid-computing-based intelligent sensing. An approach can be adopted on the basis of applying an analog preprocessing phase of high-dimensional data with an intelligent surface at the physical level, followed by a digital postprocessing phase carried out with deep neural networks at the digital level. On the basis of this criterion, [PERSON] et al. [77] proposed using smart EM cameras by integrating ANNs into intelligent metasurfaces to detect the movements of the hands and breathing of the subjects. The intelligent EM camera detects people in real time in a scene with high spatiotemporal resolution, and checks the EM wave fields toward the points of interest to better recognize the points of the body. * Hybrid-computing-based intelligent integrated sensing. Data acquisition at the physical layer and postprocessing of data at the digital level are integrated as a whole and are learned simultaneously. Two measurement networks have been designed: a measurement network called m-ANN and a reconstruction network called r-ANN, jointly optimized to extract information in a variational autoencoder.