@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00241883,
 author = {Binti, Mohd Zaidi Ain Musyira and Jadram, Narumon and Nishikawa, Yuri and Sugaya, Midori and Binti, Mohd Zaidi Ain Musyira and Jadram, Narumon and Yuri, Nishikawa and Midori, Sugaya},
 book = {Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform},
 month = {Dec},
 note = {Recently, the demand for personal low-speed mobility has increased, particularly due to the increasing number of elderly and disabled individuals. However, passengers sometimes experience discomfort when using these vehicles in outdoor environments. Therefore, this study aims to create a comfort assessment tool by analyzing and visualizing passenger comfort during outdoor rides on a map. To analyze comfort, we collected Heart Rate Variability (HRV) data from participants riding an electric wheelchair, selecting the pNN20 HRV index as it provides the most reliable measure for comfort comparison. To visualize comfort on a map, we proposed two methods. The first method is the Comfort Threshold Map (CTM), which visualizes comfort based on a threshold value of pNN20. The second method is the Comfort Level Map (CLM), which visualizes comfort on a map using five different levels based on pNN20 values. The results highlight specific areas of comfort and discomfort. The findings suggest that participants feel more comfortable in locations with a low risk of collision. In the future, we plan to expand the dataset, use our proposed maps to identify factors significantly affecting passenger comfort, and integrate these findings into the maps., Recently, the demand for personal low-speed mobility has increased, particularly due to the increasing number of elderly and disabled individuals. However, passengers sometimes experience discomfort when using these vehicles in outdoor environments. Therefore, this study aims to create a comfort assessment tool by analyzing and visualizing passenger comfort during outdoor rides on a map. To analyze comfort, we collected Heart Rate Variability (HRV) data from participants riding an electric wheelchair, selecting the pNN20 HRV index as it provides the most reliable measure for comfort comparison. To visualize comfort on a map, we proposed two methods. The first method is the Comfort Threshold Map (CTM), which visualizes comfort based on a threshold value of pNN20. The second method is the Comfort Level Map (CLM), which visualizes comfort on a map using five different levels based on pNN20 values. The results highlight specific areas of comfort and discomfort. The findings suggest that participants feel more comfortable in locations with a low risk of collision. In the future, we plan to expand the dataset, use our proposed maps to identify factors significantly affecting passenger comfort, and integrate these findings into the maps.},
 pages = {77--78},
 publisher = {情報処理学会},
 title = {Analysis and Visualization of Passenger Comfort During Low-Speed Mobility Rides in Outdoor Environments Using Heart Rate Variability Index},
 volume = {2024},
 year = {2024}
}