## 1 Introduction  
Electric scooters (e-scooters) have recently gained popularity as a personal means of transportation due to their affordability and ease of use. Additionally, their eco-friendly nature results in low carbon emissions, making them a potential alternative to bicycles and traditional scooters [1, 2]. Thanks to these advantages, the e-scooter market has rapidly expanded and now covers 626 cities across 53 countries [3]. According to a report by Nimble app Genie [4], the global e-scooter market size reached $70.26 billion in 2022, and it is projected to grow at a compound annual growth rate of approximately 9.9% from 2023 to 2030 [5, 6]. With the increase in e-scooter usage, related accidents have also surged. According to a report from the Korea Road Traffic Authority, e-scooter-related accidents increased by an average of 96.2% annually from 2017 to 2021, while overall traffic accidents decreased by approximately 1.6% during the same period [7]. [PERSON] et al. [8] reported that head injury rates for e-scooter users were more than twice as high as those for cyclists, with the primary cause of injuries being falls, leading to head and facial injury rates ranging from 20% to 58%. [PERSON] et al. [9] conducted dynamic simulations to determine impact forces generated during e-scooter falls and reported a significant correlation between the speed and the impact force on the head. They found that the impact forces during falls were great enough to cause fractures. E-scooter users are vulnerable to injuries due to the lack of protective gear, highlighting the importance of protective equipment like helmets. Wearable sensor-based protective devices have been used to detect falls or crashes and prevent injuries. [PERSON] et al. [10] developed an algorithm that detected human falls by calculating vertical angles and triangular features using 6-axis inertial sensor data. Their algorithm achieved 100% accuracy with an average lead time (the time difference between the fall and the detection) of over 300 mson their own dataset and showed 90.3% accuracy using the Sisfall [11] public dataset. [PERSON] et al. [12] developed a fall-detection algorithm by calculating vertical angles and vertical speeds based on inertial sensor data. The algorithm demonstrated 98.3% accuracy with an average lead time of over 311 ms on their dataset, and it was tested on the KFall [13] and FARSEEING [14] real fall datasets, reporting accuracies of 99.5% and 71.4%, respectively. [PERSON] et al. [15] developed a threshold-based fall-from-height detection algorithm aimed at detecting fall incidents on construction sites. By calculating vertical angles using inertial sensor data and deriving vertical speed, the algorithm achieved 100% accuracy with an average lead time of 240 ms. [PERSON] et al. [16] detected motorcycle falls using inertial sensors mounted on the vehicle. By applying thresholds to acceleration and angular velocity, they determined fall moments and integrated the system with airbags and other safety devices. Their algorithm was tested with stuntmen simulating fall scenarios at speeds up to 80 km/h, achieving a lead time of over 200 ms. Recent advances in computer science have led to the integration of AI with wearable sensor research. [PERSON] et al. [17] developed a fall-detection algorithm using a conv-LSTM model, achieving 99.32% accuracy with an average lead time of 403 ms on the KFall public dataset. The algorithm was further validated with the additional data collected from elderly individuals, demonstrating 99.84% accuracy. [PERSON] et al. [18] developed TinyFallNet, a lightweight fall-detection algorithm by tuning the parameters of the ResNet model. The model achieved 98.0% accuracy with an average lead time of over 539 ms and was reduced to 716 kB, achieving approximately a 36% reduction in size. [PERSON] et al. [19] proposed an algorithm to detect e-scooter falls using onboard inertial sensors.