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Xi et al. Soft Sci 2023;3:26 https://dx.doi.org/10.20517/ss.2023.13 Page 7 of 34

Literature coding
The selected articles underwent comprehensive analysis to extract pertinent data encompassing the types,
functions, and applications of self-powered wearable IoT sensors. This information was then organized and
categorized into specific topics and categories. Thematic analysis methods were employed to identify the
recurring themes, patterns, and distinguishing features across studies. The findings were presented in a clear
and concise manner, aligning them with the research question and objectives. The synthesized data were
interpreted within the broader context of the review, facilitating a comprehensive understanding of the
subject matter. The discussion delved into key insights, emerging trends, and research gaps in the literature.
Critical analysis of connections and contradictions within the data provided a balanced and nuanced
evaluation of the existing knowledge. The findings were documented in a clear and concise manner. The
literature review section provided an overview of the current state of research on self-powered wearable IoT
sensors as human-machine interfaces, including applications, energy harvesting mechanisms, performance
evaluation methods, and key findings. This comprehensive analysis of the literature enabled the synthesis of
existing knowledge and identification of research gaps in the field.

RESULTS
Our review identified a range of self-powered wearable IoT sensors that can be used as human-machine
interfaces, including those that utilize energy harvesting, wireless communication, and data processing
technologies. The following themes emerged from our analysis.

Materials for self-powered wearable IoT sensors play a key role in their function as human-machine
interfaces. These materials not only need to have the characteristics of being able to convert external energy
into electrical energy but also need to be safe, comfortable, and reliable when in contact with the human
body. Therefore, proper material selection can significantly improve the performance and user experience
of self-powered wearable IoT sensors. Some commonly used materials include transparent conductive
etc.
materials, flexible substrate materials, energy conversion materials, These materials can not only
provide a stable energy source for self-powered sensors but also make the sensors softer, more transparent,
and more comfortable for wider applications, such as healthcare, sports and fitness, and virtual reality
scenarios.

The working modes of self-powered wearable IoT sensors as human-machine interfaces mainly include
physical sensing, chemical sensing, and hybrid sensing. Physical sensing uses physical effects to convert
etc.
external energy (such as pressure, heat, light, ) into electrical energy, such as piezoelectric materials and
pyroelectric materials; chemical sensing uses chemical reactions to generate electrical energy, such as biofuel
cells and microfuel batteries; hybrid sensing is the combination of multiple energy conversion modes, such
as combining solar panels and piezoelectric materials. The combination of these working modes can
effectively improve the energy utilization efficiency and service life of the sensor so that it can be applied in
a wider range of application scenarios. At the same time, these working modes also provide more flexible
and innovative ideas for the design and manufacture of sensors, which will help promote the development
of wearable IoT sensor technology.

The technology used in self-powered wearable IoT sensors as human-machine interfaces includes a variety
of advanced technical means, such as TENGs, PENGs, thermoelectric nanogenerators, biofuel cells, solar
etc.
cells, machine study, These technical means can convert external energy into electrical energy so that
the sensor does not need an external power supply, thereby improving the energy utilization efficiency and
service life of the sensor. Among them, machine learning can improve the accuracy and reliability of sensors
through the analysis and learning of sensor data, making it more widely used in human-machine interfaces.