###### Abstract
EMG-driven machine learning has transformed human-machine interaction (HMI) applications, such as virtual reality interfaces and prosthetics, while also aiding in the diagnosis of neuromuscular diseases. EEG, powered by machine learning, facilitates brain-computer interface development and cognitive state analysis, benefiting psychology and neurology. Meanwhile, PPG, with its non-invasive nature and enhanced signal processing, supports the diagnosis of cardiovascular conditions and real-time health monitoring [19; 22]. With the development of biodegradable materials, there is now a great chance to revolutionise healthcare technologies using sensors that will naturally break down after use. With the recent development of superhydrophobic wearable sensors, it is now evident that wearable devices with superhydrophobic functions have significant potential for diverse applications [23]. MXene ink (transition metal carbides, nitrides, and carbonitrides) has penetrated such diverse fields of research as wonder materials. The most attractive features of their applications show great promise in energy storage conversion, harvesting, and sensing [24]. E-skins with multimodal sensing capabilities have been extensively reviewed, highlighting their potential for diverse applications, including health monitoring, intelligent prosthetics, HMIs, and robotic systems. Further, these multimodal e-skin systems are classified into three modes, as follows: (1) integration of multiple physical sensors, (2) integration of physical and electrophysiological sensors, and (3) integration of physical and chemical sensors. These systems primarily mimic human skin functions by detecting physical stimuli like force, strain, vibration, temperature, and humidity while also integrating additional sensing modalities, such as ultra-violet (UV) light sensors, to create next-generation smart skins [5]. These advancements have shed light on the future direction of next-generation wearable sensor devices. This evolving trend encompasses various areas, including the applications of sixth generation (6G) radio frequency (RF) in sensing, imaging, and healthcare. These applications encompass a broad spectrum, ranging from wireless networks employed in neural implants and drug delivery systems to guaranteeing safety concerns related to exposure to microwaves. The exploration of these avenues paves the way for innovative developments in wearable technology [25]. In wearable applications, specific absorption rate (SAR) measures the rate at which the human body absorbs energy from electromagnetic fields (EMF), with regulatory limits set to prevent harmful exposure. In the U.S., the Federal Communications Commission (FCC) has set the SAR limit at 1.6 W /kg, averaging over 1 g of tissue for the head, and 4.0 W/kg, averaging over 10 g for extremities. The International Commission on Non-Ionizing Radiation Protection (ICNIRP) has slightly different guidelines, with 2.0 W/kg for head exposure in the EU, Japan, and China. In overall wearable and flexible sensor devices, accurate SAR measurement and adherence to these regulatory limits are essential for ensuring user safety, particularly with newer high-frequency applications. Updating safety guidelines to account for prolonged exposure and new frequency ranges is increasingly important as these devices become more integrated into daily life. To minimise the effects of microwave wave radiation, various methods such as reducing direct exposure, maintaining distance from radiation sources, using protective layers like shielding, and period structures can be used [25; 26]. In WBAN, additive manufacturing (AM) methods have undergone significant advancements over the past few decades, catering to diverse applications such as medical devices and orthopaedic equipment [27]. These advancements have opened new possibilities for the manufacturing and utilisation of wearable sensor devices within the WBAN domain using various inks or liquids.