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Huang et al. Soft Sci 2024;4:40 https://dx.doi.org/10.20517/ss.2024.37 Page 13 of 35

Table 1. Some machine learning algorithms for healthcare monitoring systems
Textile Sensor Function Machine learning models Ref.
Fabric Pressure sensor Biometric gait recognition SVM [187]
Muscle pants Stretch sensor Human motion recognition Random forest, neural network, SVM [188]
Chest band Stretch sensor Talking detection Random forest, neural network, linear discriminant [189]
analysis
Fabric Gas sensor Gas sensing identification Machine learning-enabled principal component analysis [190]
Vest Triboelectric sensor Sitting position recognition Random forest [191]
Cuff Triboelectric Sensor Cardiovascular monitoring Neural network [192]
Wristband, socks Pressure sensor Respiration and gait CNN [193]
recognition
Garment Polymer optical fiber Activity classification KNN classifier [194]
sensor
Knee covers, Strain sensor Human motion recognition 1D CNN [195]
sleeves
SVM: Support vector machines; CNN: convolutional neural networks; KNN: K-nearest neighbors.

Communication module
The function of the communication module is to realize the transmission and communication of health
data. Communication modules can be categorized into two types based on transmission mode: wired and
wireless. Wired communication modules, such as Ethernet, are ideal for scenarios demanding stable and
high-speed data transfer. On the other hand, wireless communication modules, including Bluetooth, Wi-Fi,
near field communication (NFC), and radio frequency identification, are suited for situations that call for
mobility and flexibility in data transmission. Textile antennas, designed for the transmission and reception
of radio waves, have garnered considerable research attention. To meet specific design requirements, the
antenna’s geometry and dimensions are developed using electromagnetic simulation software to predict the
performance of the antenna . Subsequently, the conductive material is applied to the substrate material
[196]
using printing technology (such as screen printing or inkjet printing) or coating technology to create the
[197]
antenna .

Display module
To present visual health management and analysis, the collected monitoring data is transmitted to an
external display device, such as a smartphone or computer interface. In recent years, preliminary display
fabrics have been realized. There are two approaches to fabricating textile displays. The first approach
involves directly manufacturing thin-film light-emitting diodes (LEDs) onto the fabrics. For optimal LED
performance, it is essential that the underlying substrate is extremely smooth. Consequently, a critical step is
applying a polymer buffer layer to the textile’s uneven surface through methods such as heat pressing or
UV-induced photopolymerization [198,199] . Alternatively, light-emitting fibers can be created and then woven
directly into the fabric to serve as displays [200,201] . The advancement of light-emitting fabrics for real-time
health status display is anticipated to drive significant progress in health monitoring system development.

In summary, the design of all modules must consider compatibility, size, shape, and flexibility to facilitate
their integration with textiles. The existing textile-based health monitoring systems still combine rigid and
flexible components. Numerous challenges remain in the development of a fully textile-based health
monitoring system.

APPLICATIONS OF TEXTILE ELECTRONICS IN HEALTH MONITORING
In health monitoring systems, textile electronics play an essential role, necessitating one or more such
components. These fundamental components can be categorized into four types based on their