| Title |
Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs |
| ID_Doc |
11262 |
| Authors |
Yu H.; Raychoudhury V.; Saha S.; Edinger J.; Smith R.O.; Gani M.O. |
| Year |
2023 |
| Published |
IEEE Transactions on Artificial Intelligence, 4, 4 |
| DOI |
http://dx.doi.org/10.1109/TAI.2022.3190828 |
| Abstract |
Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE. |
| Author Keywords |
Accessibility; classification; machine learning; smart cities; wheelchair |
