## 2 Wireless Communication Technologies
#### Smart Wearable Devices
Second-generation devices encompass a range of options, including invasive microneedles, injectable devices, and compact alternatives such as skin patches, tattoos, tooth-mounted sensors, and contact lenses. These advancements offer diverse solutions for monitoring and enhancing various aspects of human health and well-being [69]. A group of second-generation devices utilise biofluids and employ biorecognition components to convert analytes into measurable signals. These devices are typically laboratory-based and include examples such as the Freestyle Libre for glucose monitoring and sweat-based patches for Gx detection. Numerous wearable biochemical and biophysical sensor devices have been utilised for wellness applications, including the detection of COVID-19 [70]. In [71], a novel nanostructured smart tactile sensing device was constructed using 3D printing with an integrated electronic component. A tactile sensing device was improved using neural networks (NN) on a digital unit. Various telehealth devices are employed to identify an illness of an individual. By utilising four essential bio signals such as ECG, EMG, EEG, and PPG, machine learning and wearable technology have transformed medical monitoring and HMI. When machine learning is combined with ECG, it can be used to accurately detect diseases, classify heartbeats, and provide real-time cardiac monitoring. This improves cardiac care and helps identify disorders like stroke risk and sleep apnea. EMG-driven machine learning has revolutionised HMI applications and helped diagnose neuromuscular illnesses. Examples of these applications include virtual-reality interfaces and prosthetics. Machine learning-powered EEG helps with cognitive state analysis and brain-computer interface development, which is beneficial in neurology and psychology. PPG, on the other hand, facilitates real-time health monitoring and the identification of cardiovascular disorders due to its non-invasive nature and improved data processing. Therefore, machine learning has revolutionised wearable and HMI applications and helped in diagnosis [19; 22; 72]. Researchers integrated a contact lens device based on a multifunctional ultrathin MoS2 transistor, as shown in Figure 2b [73]. In [74], a hard-structure sensor, along with circuit chips, was incorporated into the lens-type substrate and contact-tear fluid method via a microfluidic-based sensing channel. An ultrathin-type sensor was fixed onto the lenses and came into contact with the tear fluid, which provided detection without interfering with blinking. Soft electronic devices offer an excellent interface with the human body's skin, enabling real-time monitoring of vital signals [75]. Researchers investigated smart glasses and head-mounted computers capable of displaying information [76]. They employed nanomaterials with mechanical structures and stretchable polymers to develop electronic devices that are both flexible and stretchable, enabling seamless integration with living organisms. These devices are capable of detecting parameters such as temperature, ion concentrations, and electrocardiogram (ECG or EKG) signals, in vivo or in vitro [77]. Several transdermal patches were used for pharmaceutical and chemical agents through the human body's skin [78]. A single-walled carbon nanotube (SWCNT) was designed to monitor glucose oxidase-Nafion composites for sensing [79]. Recently, hybrid devices based on metal, dielectric, and metallic polymers were proposed, and their performance was simulated for various frequency bands to design wearable and flexible devices. A group of researchers recently investigated a self-powered device that can easily harvest ambient energy to run a sensor, removing the need for external power [80]. Piezo-electric nano-generators (PENGs) and tribo-electric nano-generators (TENGs) have predominantly been employed in the development of self-powered sensor devices.