Surface Recognition from Wheelchair-induced Noisy Vibration Data: A Tale of Many Cities

Abstract

Despite the active legislation in many countries supporting the accessibility of public spaces by mobility-impaired users, the reality is far from ideal. Wheelchair users often struggle to navigate the built environment let alone the natural areas. While barriers to wheeled mobility can be caused by broken/uneven surfaces, steep slopes, and unfavorable weather conditions, the effects of many such factors and others are not properly investigated. In this paper, we aim to classify various built and natural surfaces through their characteristic vibration patterns using different deep learning algorithms. The surface vibration data is collected from various cities in Europe (including Paris (FR), Mannheim (DE), Dresden (DE), Munich, Nuremberg (DE), and Salzburg (AT)) while a user drives a manual wheelchair attached with three differently oriented smartphones placed at different heights. Extensive experiments show that a Deep Neural Network model classifies surfaces using a denoised dataset with a 98.9% accuracy which is significantly higher than our previous state-of-the-art.