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

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.

The Feasibility of Information-Entropy-Based Behavioral Analysis for Detecting Environmental Barriers.

The enhancement of physical activity is highly correlated with the conditions of the built environment. Walking is considered to be a fundamental daily physical activity, which requires an appropriate environment. Therefore, the barriers of the built environment should be identified and addressed. Barriers can act as external stimuli for pedestrians, so pedestrians may diversely respond to them. Based on this consideration, this study examines the feasibility of information-entropy-based behavioral analysis for the detection of environmental barriers. The physical responses of pedestrians were collected using an inertial measurement unit (IMU) sensor in a smartphone. After the acquired data were converted to behavioral probability distributions, the information entropy of each grid cell was calculated. The grid cells whereby the participants indicated that environmental barriers were present yielded relatively high information entropy values. The findings of this study will facilitate the design of more pedestrian-friendly environments and the development of diverse approaches that utilize citizens for monitoring the built environment.

Assessment of Daily Routine Uniformity in a Smart Home Environment Using Hierarchical Clustering.

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.