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一种用于电动轮椅控制的智能且低成本的眼动追踪系统。

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control.

机构信息

School of Engineering, University of Maryland, College Park, MD 20742, USA.

Department of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.

出版信息

Sensors (Basel). 2020 Jul 15;20(14):3936. doi: 10.3390/s20143936.

Abstract

In the 34 developed and 156 developing countries, there are ~132 million disabled people who need a wheelchair, constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head. This paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input is images of the user's eye that are processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented, and tested; all of which were based on a benchmark database created by the authors. The first three techniques were automatic, employ correlation, and were variants of template matching, whereas the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e., 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on eight subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin. This work not only empowers any immobile wheelchair user, but also provides low-cost tools for the organization assisting wheelchair users.

摘要

在 34 个发达国家和 156 个发展中国家中,有大约 1.32 亿残疾人需要轮椅,占世界人口的 1.86%。此外,还有数百万人患有与运动障碍相关的疾病,这些疾病导致四肢甚至头部无法产生受控运动。本文提出了一种通过使用眼控电动轮椅帮助运动障碍患者恢复有效和轻松运动能力的系统,而无需依赖他人。系统的输入是用户眼睛的图像,这些图像经过处理以估计注视方向,然后相应地移动轮椅。为了实现这一目标,开发、实现和测试了四个特定于用户的方法;所有方法都基于作者创建的基准数据库。前三种技术是自动的,采用相关方法,是模板匹配的变体,而最后一种则使用卷积神经网络 (CNN)。计算了不同的指标来定量评估每个算法在准确性和延迟方面的性能,并进行了总体比较。CNN 表现出最好的性能(即 99.3%的分类准确率),因此它是注视估计器的首选模型,该模型控制轮椅运动。该系统在 8 名受试者中进行了仔细评估,在户外和室内不同光照条件下的准确率达到 99%。这需要修改电动轮椅以适应注视估计算法输出的预测。如果测量距离低于定义良好的安全裕度,轮椅控制可以绕过注视估计器做出的任何决定,并借助一系列接近传感器立即停止其运动。这项工作不仅使任何无法移动的轮椅使用者都能受益,还为帮助轮椅使用者的组织提供了低成本的工具。

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