{"title":"Development of an intention estimation model based on gaze and face information for electric wheelchair operation support","authors":"Sho Higa, Koji Yamada, Shihoko Kamisato","doi":"10.1002/ecj.12367","DOIUrl":null,"url":null,"abstract":"<p>In recent years, various user interfaces have been developed to meet the diverse needs of physically disabled persons. In this paper, we developed a method to identify gazing and facial movements based on gaze time and eye/face information and developed an electric wheelchair that can be operated with the user's natural gazing and facial movements. This intention estimation model is composed of 1DCNN and LSTM layers. First, 1DCNN is used to extract features from gaze and face information, and then the extracted features are input to LSTM to estimate the user's intentions regarding movement. The evaluation experiments suggest that the combination of gaze and face information improves the estimation accuracy and contributes to the classification. Furthermore, it was confirmed that adding a convolutional filter layer to the LSTM layer improved the accuracy.</p>","PeriodicalId":50539,"journal":{"name":"Electronics and Communications in Japan","volume":"105 3","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics and Communications in Japan","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ecj.12367","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, various user interfaces have been developed to meet the diverse needs of physically disabled persons. In this paper, we developed a method to identify gazing and facial movements based on gaze time and eye/face information and developed an electric wheelchair that can be operated with the user's natural gazing and facial movements. This intention estimation model is composed of 1DCNN and LSTM layers. First, 1DCNN is used to extract features from gaze and face information, and then the extracted features are input to LSTM to estimate the user's intentions regarding movement. The evaluation experiments suggest that the combination of gaze and face information improves the estimation accuracy and contributes to the classification. Furthermore, it was confirmed that adding a convolutional filter layer to the LSTM layer improved the accuracy.
期刊介绍：
Electronics and Communications in Japan (ECJ) publishes papers translated from the Transactions of the Institute of Electrical Engineers of Japan 12 times per year as an official journal of the Institute of Electrical Engineers of Japan (IEEJ). ECJ aims to provide world-class researches in highly diverse and sophisticated areas of Electrical and Electronic Engineering as well as in related disciplines with emphasis on electronic circuits, controls and communications. ECJ focuses on the following fields:
- Electronic theory and circuits,
- Control theory,
- Communications,
- Cryptography,
- Biomedical fields,
- Surveillance,
- Robotics,
- Sensors and actuators,
- Micromachines,
- Image analysis and signal analysis,
- New materials.
For works related to the science, technology, and applications of electric power, please refer to the sister journal Electrical Engineering in Japan (EEJ).
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