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Two-Step k-means Clustering Based Information Entropy for Detecting Environmental Barriers Using Wearable Sensor
1Department of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Suji-gu, Yongin-si 16890, Korea.
International Journal of Environmental Research and Public Health
|January 21, 2022
Summary
Identifying walking environmental barriers is crucial for pedestrian safety. This study introduces an information entropy method using wearable sensors to efficiently detect these barriers, improving walkability management.
Area of Science:
- Urban planning
- Human-computer interaction
- Biomechanics
Background:
- Effective management of the walking environment is essential for transportation and pedestrian experience.
- Current methods for identifying environmental barriers are labor-intensive and time-consuming.
- Walkability is significantly impacted by environmental barriers.
Purpose of the Study:
- To develop an efficient method for identifying environmental barriers in walking environments.
- To utilize information entropy and behavioral data for barrier detection.
- To enhance the continuous monitoring of walkability.
Main Methods:
- Collected gait data from 64 participants using wearable sensors.
- Classified gait patterns into seven types using two-step k-means clustering.
- Calculated information entropy based on gait probability distributions at different locations.
Main Results:
- Information entropy values demonstrated a strong correlation with the presence or absence of environmental barriers.
- The developed method successfully identified environmental barriers.
- The approach facilitates continuous monitoring of walking environments.
Conclusions:
- The information entropy-based method offers an efficient alternative to traditional barrier identification techniques.
- This approach can significantly contribute to improving pedestrian safety and experience.
- Continuous monitoring of environmental barriers can be achieved through this novel method.
