Abstract
Providing accessibility information about sidewalks for people with difficulties with moving is an important social issue. We previously proposed a fully supervised machine learning approach for providing accessibility information by estimating road surface conditions using wheelchair accelerometer data with manually annotated road surface condition labels. However, manually annotating road surface condition labels is expensive and impractical for extensive data. This paper proposes and evaluates a novel method for estimating road surface conditions without human annotation by applying weakly supervised learning. The proposed method only relies on positional information while driving for weak supervision to learn road surface conditions. Our results demonstrate that the proposed method learns detailed and subtle features of road surface conditions, such as the difference in ascending and descending of a slope, the angle of slopes, the exact locations of curbs, and the slight differences of similar pavements. The results demonstrate that the proposed method learns feature representations that are discriminative for a road surface classification task. When the amount of labeled data is 10% or less in a semi-supervised setting, the proposed method outperforms a fully supervised method that uses manually annotated labels to learn feature representations of road surface conditions.
Highlights
Providing accessibility information about sidewalks for people with difficulties with moving, such as older, mobility-impaired, and visually impaired people, is an important social issue
This paper proposes a novel method for evaluating road surface conditions via weakly supervised learning that uses wheelchair acceleration data and its positional information as weak supervision
The network is composed of an input layer (Input), four convolution layers (CONV + Max POOL), fully connected layer (FC), and an output layer (Output)
Summary
Providing accessibility information about sidewalks for people with difficulties with moving, such as older, mobility-impaired, and visually impaired people, is an important social issue. Human activities measured by body-mounted vital sensors are recognized by applying machine learning [12,13] Motivated by this background, we proposed a system that evaluates road surface conditions by applying machine learning to wheelchair accelerometer data [14]. This paper proposes and evaluates a novel method for estimating road surface conditions without human annotation by applying weakly supervised learning [15]. The positional information can be automatically collected with acceleration data during wheelchair driving and is semantically related to road surface conditions. This paper proposes a novel method for evaluating road surface conditions via weakly supervised learning that uses wheelchair acceleration data and its positional information as weak supervision. We applied a weak supervision design and visually demonstrate that the proposed method learns subtle and detailed representations of road surface conditions.
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