# Applications of Smart Helmet in Applied Sciences: A Systematic Review
## 5 Conclusions  
A systematic review was conducted to investigate the current status and trends of smart helmet studies published in the past 11 years (2009-2020). The review provided answers to five RQs, as follows.  
1. The number of studies on smart helmets has increased rapidly since 2018. In the last three years (2018-2020), these studies accounted for 74.8% of the total number of studies. Therefore, it can be said that the research on smart helmets has been receiving considerable attention, and it is expected to be expanded further in the future.
2. Studies on smart helmets can be divided into two categories (i.e., motorcyclists and workers) depending on the user. Among these, the application of smart helmets to motorcyclists accounts for 79%. Because helmets are necessary items that should be worn by motorcyclists, smart helmet studies have attracted relatively more attention in this field. Most smart helmets for motorcyclists provide functions for starting a motorcycle and rescue requests in emergency situations. To date, research on smart helmets for workers has been inadequate.
3. Among the various sensors used for developing smart helmets, those related to human health monitoring have been used the most, and the last three years (2018-2020) have seen the highest increase. Among them, sensors for detecting accidents, helmet wearing status, and alcohol were frequently used, accounting for 32%, 30%, and 27%, respectively. Note that the use of environmental monitoring sensors for harmful gas detection, image information acquisition, and temperature measurement, has rapidly increased since 2019.
* Currently, the type of microcontroller primarily used for developing smart helmets is a single-board MCU, such as the Arduino board, which accounts for more than 50% of all microcontroller types. Although single-board computers such as the Raspberry Pi or smartphones are also being used to develop smart helmets, this number has not been so far.
* To date, approximately 85% of smart helmets have been developed using wireless communication technologies, such as RF, Bluetooth, WiFi, and Zigbee. Among them, the use of RF and Bluetooth has been increasing rapidly since 2015, and they are the most commonly used technologies today, accounting for 38% and 34%, respectively.  
Considering the expandability and functionality of smart helmets, they can be widely used not only for various sporting events but also for military purposes. However, until now, few cases of such applications have been reported in academic papers. Academic studies on smart helmets are needed to expand their application to more diverse fields, including sports and military services. Therefore, the findings of this study are expected to benefit researchers and industry professionals in various fields by offering detailed and systematic analysis results of smart helmet technologies to the community, thereby bridging the gap between academia and industry. In addition, it can help practitioners recognize the potential applications of smart helmets and understand the requirements and challenges of smart helmet products in various fields.  
Conceptualization, Y.C.; methodology, Y.C.; software, Y.C.; validation, Y.K.; formal analysis, Y.K.; investigation, Y.C.; resources, Y.C.; data curation, Y.K.; writing--original draft preparation, Y.C. and Y.K.; writing--review and editing, Y.C.; visualization, Y.K.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2021R1A2C1011216).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.