ArticleFrontiers in neurorobotics2021
Face-Computer Interface (FCI): Intent Recognition Based on Facial Electromyography (fEMG) and Online Human-Computer Interface With Audiovisual Feedback.
Article in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Using principles of motor control to analyze performance of human machine interfaces.Scientific reports · 2023Article
- Facial electromyogram-based facial gesture recognition for hands-free control of an AR/VR environment: optimal gesture set selection and validation of feasibility as an assistive technology.Biomedical engineering letters · 2023Article
- Using Principles of Motor Control to Analyze Performance of Human Machine Interfaces.Research square · 2023Article
- Crosstalk in Facial EMG and Its Reduction Using ICA.Sensors (Basel, Switzerland) · 2023Article
- Human-machine interface-based wheelchair control using piezoelectric sensors based on face and tongue movements.Heliyon · 2022Article
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Authors and funding
6 authors.
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Abstract
Patients who have lost limb control ability, such as upper limb amputation and high paraplegia, are usually unable to take care of themselves. Establishing a natural, stable, and comfortable human-computer interface (HCI) for controlling rehabilitation assistance robots and other controllable equipments will solve a lot of their troubles. In this study, a complete limbs-free face-computer interface (FCI) framework based on facial electromyography (fEMG) including offline analysis and online control of mechanical equipments was proposed. Six facial movements related to eyebrows, eyes, and mouth were used in this FCI. In the offline stage, 12 models, eight types of features, and three different feature combination methods for model inputing were studied and compared in detail. In the online stage, four well-designed sessions were introduced to control a robotic arm to complete drinking water task in three ways (by touch screen, by fEMG with and without audio feedback) for verification and performance comparison of proposed FCI framework. Three features and one model with an average offline recognition accuracy of 95.3%, a maximum of 98.8%, and a minimum of 91.4% were selected for use in online scenarios. In contrast, the way with audio feedback performed better than that without audio feedback. All subjects completed the drinking task in a few minutes with FCI. The average and smallest time difference between touch screen and fEMG under audio feedback were only 1.24 and 0.37 min, respectively.
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