## 3 Healthcare
### Human Activity Monitoring  
#### 3.1.1 Introduction  
Recent years have shown an increased interest in the detection of human activity in both indoor and outdoor environments, such as healthcare [24; 25; 26; 27], search and rescue through localisation and tracking [28; 29; 30; 31], intrusion detection, and others. Existing techniques for human activity detection rely on ambient sensors, wearables and cameras that require, primarily, onerous deployment overheads. Moreover, they can raise concerns regarding privacy, including physical invasion of the environment. Researchers are continuously developing non-invasive, easily deployable, and privacy-preserving systems for detecting large-scale body movement, using two or more radio frequency (RF) transceivers as shown in Figure 3. This is motivated from the early use of radars to determine the distance, angle and/or velocity of an object, using radio waves. This principle is widely used by the military to detect and identify threats, such as drug smuggling, terrorist attacks, espionage, etc. [32]. The advancements in signal processing techniques have made radar detection and identification more robust while opening this field for further use cases, such as human activity classification. A moving target relative to a radar sensor induces a frequency shift in the echo as a result of the well-known Doppler effect. The distinctive characteristics of the observed micro-Doppler effect of an object or a process is called the micro-Doppler signature. Ultimately, a unique micro-Doppler signature for walking and running is the periodic motion of arms and legs, producing sidebands for the main Doppler frequency used for the detection and classification of the respective activity [33].  
Since future 6G mobile networks are expected to thrive on even denser RF environments than the current 5G systems, here, we present a case study on human activity detection [34], using ambient RF waves [35; 36]. The non-invasive and contactless human activity detection based on channel state information (CSI) utilising Wi-Fi devices has been studied extensively to detect daily living routines [37; 38]. This includes the use of off-the-shelf wireless devices and network interface cards operating at 2.4 GHz to extract CSI information, depicting movement and activity. Similarly, other studies used radar-based systems to track occupancy through the use of frequency modulated continuous-wave (FMCW) and orthogonal frequency division multiplexing (OFDM) techniques [39].  
The case study to be presented differs from those previously presented in the literature since the earlier work used off-the-shelf wireless devices that present numerous technical limitations. For instance, the Wi-Fi transmitter sends 56 subcarriers; however, a regular off-the-shelf NIC only reports 30 subcarriers, losing nearly 46% of the information [40]. Additionally, the transmitter power is not flexible and does not change according to a particular human activity. The presence of random noise also impairs the phase information retrieved through these small wireless devices [19]. In this case study, software-defined radios, specifically, universal software radio peripherals (USRPs), are used to transmit and receive \(N\) number of OFDM subcarriers, as compared to earlier work, where only a limited number of subcarriers were available. The system developed for this use case also allowed modification to the transceiver's power level and its operating frequency.  
Moreover, it enabled the use of custom-designed antennas and more control on the number of subcarriers employed in real-time. The case study shows that the results obtained using USRP based wireless sensing for daily living activities are more accurate than those, owing to off-the-shelf wireless devices.