Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

Authors

Hui R. Ng, University of Nebraska at Omaha
Isidore Sossa, University of Nebraska at Omaha
Yunwoo Nam, University of Nebraska-LincolnFollow
Jong-Hoon Youn, University of Nebraska at OmahaFollow

ORCID IDs

Hui R. Ng https://orcid.org/0000-0002-2359-5014

Date of this Version

2023

Citation

Ng, H.R.; Sossa, I.; Nam, Y.; Youn, J.-H. Machine Learning Approach for Automated Detection of IrregularWalking Surfaces for Walkability Assessment with Wearable Sensor. Sensors 2023, 23, 193. https://doi.org/10.3390/s23010193

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

Abstract

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians’ physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model’s performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions.