A Visual Feedback Supported Intelligent Assistive Technique for Amyotrophic Lateral Sclerosis Patients
Zihao Wang, Aojie Zhang, Xinyue Xia, Sizhe Zhang, Haitao Li, Jiaqi Wang, Shuo Gao
Beihang University
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摘要与影响
Among diverse intelligent assistive systems developed for amyotrophic lateral sclerosis (ALS) patients, headwear eye tracking based ones trigger broad interests due to their merits such as noninvasive, cost effective, and high operation freedom. However, with headwear eye trackers, patients are easy to feel tired during human–machine interactivities (HMIs), and the operation accuracy is not satisfied compared with its counterparts. To address these two issues, herein, a visual feedback technique is developed which allows users to recognize machine's vision by positioning a laser spot to the user watched object, according to the location information interpreted from user's eye movement. Through the visual feedback technique, users not only obtain real‐time feedback, but also can fine‐tune the laser spot to the desired location before performing further operations. Experimental results demonstrate that the presented work can successfully reduce user's fatigue and boost operation accuracy by 25.1% and 27.6%, respectively, therefore, advancing the field of intelligent assistive technologies.
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学术脉络
学科主题
计算机 / AIGaze Tracking and Assistive Technology
EEG and Brain-Computer Interfaces · Assistive Technology in Communication and Mobility
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