Abstract
In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.
Highlights
Brain-Machine Interface (BMI) is a digital communication interface capable of connecting cortical activity and an external device, avoiding the participation of peripheral nerves or the muscolar system [1,2]
Given an image under analysis, boundary pixels were selected through Canny method [45] and for each pixel 36 features were evaluated through the proposed adaptive direction tracking filter (ADT) filter (Section 3)
The novelty of the proposed system lies in including a shadow detection module based on an adaptive direction tracking filter to extract target features along the direction of boundaries
Summary
Brain-Machine Interface (BMI) is a digital communication interface capable of connecting cortical activity and an external device, avoiding the participation of peripheral nerves or the muscolar system [1,2]. It is to be noted that, in real environments, during the navigation with wheelchair, subjects can encounter obstacles that can jeoparde their safety. In this context, motivated by the growing demand to develop even more intelligent assistive navigation systems, we propose a BMI prototype able to aid people with neuromotor disabilities and control a wheelchair using motor imaginary and recent advances in boundary objects detection. The overall system includes a novel shadow boundary detection algorithm intended to identify possible obstacles and prevent the user from dangerous situations. The mental control signal and the shadow detection algorithm work contextually, according to a shared control
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