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

Abstract

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.

Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait

“…The experimental site was the same as the site of the author's previous study, and Figure 3 presents a graphical summary of the previous study. The experimental site's data collection process and environmental features are presented in detail in previous studies [27]. In this study, the gait data were collected by a commercial wearable sensor (Opal sensor with 1.15 degree of roll/pitch static accuracy, 1.50 degree of heading static accuracy, 2.80 degree of dynamic accuracy) produced by APDM Inc.…”

“…The participants' ages and genders in the first and second experiments are presented respectively in Table 1. lection process and environmental features are presented in detail in previous studies [27].…”

“…Therefore, if we can differentiate between abnormal gaits and normal gaits, we can find the environmental barriers that cause abnormal gaits. Existing wearable-based methods mainly used the intensity of the response to differentiate between abnormal gaits and normal gaits [27]. Although the existing method has shown sufficient potential to detect the hazard, the intensity-based approach to the reaction does not sufficiently consider the various types of human gait.…”

“…This study aims to identify the environmental barrier through the classification of normal gaits and abnormal gaits caused by the environmental barrier using wearable sensor data. To this end, the author labels and applies the experimental data performed in the author's previous study [27] to the cascaded LSTM-based DRNN model [45,49] for training and testing the model. Afterward, the feasibility of the model for new data is checked by applying the data of the experiments conducted at the same site to the built model.…”

Review—Smart Wearable Sensors for Health and Lifestyle Monitoring: Commercial and Emerging Solutions

“…A smart wearable sensor recorded the gait process of 64 participants, revealing differing behavioural responses to environmental barriers. 72 While the study's small sample size and limited data collection areas may impact reliability, smart wearables show potential in assessing environmental barriers, particularly in specific conditions. Sedentary behaviour in urban environments poses cardiovascular risks, addressed by smart wearable IoT prototypes integrated into garments for real-time assessment.…”