NDLI: Two-Step k-means Clustering Based Information Entropy for Detecting Environmental Barriers Using Wearable Sensor

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Two-Step k-means Clustering Based Information Entropy for Detecting Environmental Barriers Using Wearable Sensor

Content Provider	MDPI
Author	Lee, Bogyeong Kim, Hyunsoo
Copyright Year	2022
Description	Walking is the most basic means of transportation. Therefore, continuous management of the walking environment is very important. In particular, the identification of environmental barriers that can impede walkability is the first step in improving the pedestrian experience. Current practices for identifying environmental barriers (e.g., expert investigation and survey) are time-consuming and require additional human resources. Hence, we have developed a method to identify environmental barriers based on information entropy considering that every individual behaves differently in the presence of external stimuli. The behavioral data of the gait process were recorded for 64 participants using a wearable sensor. Additionally, the data were classified into seven gait types using two-step k-means clustering. It was observed that the classified gaits create a probability distribution for each location to calculate information entropy. The values of calculated information entropy showed a high correlation in the presence or absence of environmental barriers. The results obtained facilitated the continuous monitoring of environmental barriers generated in a walking environment.
Starting Page	704
e-ISSN	16604601
DOI	10.3390/ijerph19020704
Journal	International Journal of Environmental Research and Public Health
Issue Number	2
Volume Number	19
Language	English
Publisher	MDPI
Publisher Date	2022-01-08
Access Restriction	Open
Subject Keyword	International Journal of Environmental Research and Public Health Transportation Science and Technology Walkability Environmental Barrier K-means Clustering Inertial Measurement Unit (imu) Information Entropy Wearable Sensor
Content Type	Text
Resource Type	Article

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