Open AccessA Visual Feedback Supported Intelligent Assistive Technique for Amyotrophic Lateral Sclerosis Patients
Author(s) -
Wang Zihao,
Zhang Aojie,
Xia Xinyue,
Zhang Sizhe,
Li Haitao,
Wang Jiaqi,
Gao Shuo
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202100097
Subject(s) - computer science , amyotrophic lateral sclerosis , human–computer interaction , eye tracking , computer vision , bittorrent tracker , assistive technology , controller (irrigation) , visual feedback , artificial intelligence , object (grammar) , simulation , medicine , disease , pathology , agronomy , biology
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.