## 2 LIG-Based Wearable Systems for Multifunctional and Intelligent Health Care
### Machine Learning-Based Multi-Modal Electrochemical Analytical System  
The detection of multiple biomolecules is crucial in various fields including disease diagnosis, environmental monitoring, and food safety. However, it is challenging to use a single material such as LIG to accurately detect multiple biomolecules in a mixture.  
To address this challenge, [PERSON] et al. proposed an electrochemical analytical wearable system that integrates LIG-based devices with machine learning algorithms. As shown in Figure 5, this system enables the multiplexed detection of tyrosine and uric acid in both sweat and saliva. The LIG-based electrochemical sensor was realized by irradiating a commercial CO\({}_{2}\) laser system. The three-electrode sensor consisted of a working electrode (eMoS\({}_{x}\)-LIG), a counter electrode (LIG), and a reference electrode (LIG). The working electrode was made functional through the addition of MoS\({}_{x}\) through electrodeposition, which led to a significant improvement in the sensor's performance with a 3-fold increase in the electrochemically active specific surface area (ECSA) and a 1.5-fold increase in the heterogeneous electron transfer rate when compared to the bare LIG-based electrode. In order to fulfill the need for extensive electrochemical data in machine learning, a specialized sensor bracket was created to simultaneously gather signals from eight LIG-based sensors.  
Figure 4: Human motion-driven self-powered sensing system. (**a–c**) Schematic illustration and optical images of the LIG-based self-powered, wireless, wearable sensing platform, with the energy harvested by TENGs from kinetic human motion to charge MSCAs as the sustained power supply for powering on-skin sensors, signal processing units, and wireless data transmission components. (**d**) Photo of the stretchable LIG-based top electrode with island-bridge layout in the TENG. (**e**) Photo of the stretchable LIG-based MSCAs attached on a human wrist. (**f**) Schematic illustration of the laser inducing process. (**g**) Photo of the LIG-based TENG shoe sole incorporated into a flip-flop. (**h**) Circuit diagram of the LIG-based TENG shoe sole. Reproduced with permission from [15]. Copyright 2022 AIP Publishing.  
To test the multi-modal system, artificial saliva and artificial sweat with different concentrations of tyrosine and uric acid were prepared. The acquired data were used to train the algorithm for automatic baseline subtraction with integrated feature extraction. Due to the multi-modal and multi-peak analysis, the algorithm could reduce the limit of detection (LOD) of the system by two orders of magnitude. Furthermore, with the help of the algorithm, the system could quantify each kind of biomolecule in a mixture and realize multiplexed detection using a single sensor. Moreover, on-body sweat analysis showed that compared to a single sensor, the multi-modal system combined with machine learning could accurately quantify the analyte concentration in the mixture.  
The combination of wearable electrochemical monitoring and machine learning makes it possible for the LIG-based wearable system to accurately collect and analyze real-time concentrations of diverse biochemical components, which holds great potential for applications in health care, environmental detection, and food safety. In addition to the highly sensitive information collection and high-performance intelligent information analysis (diagnostic function), if the wearable system can also achieve a therapeutic function, it will give it the basic capability of a \"portable doctor\". Next, we introduce the possibility of wearable therapy with LIG by using the implementation of LIG-based soft actuators as an example.