Hands-Free Human-Computer Interface Based on Facial Myoelectric Pattern Recognition

Conclusion A facial movement-machine interface was developed in this study in order to help users with limited hand function manipulate electronic devices. Facial movements were detected using four EMG sensors, and five movement patterns were classified using myoelectric pattern recognition algorithms. The results from 10 able-bodied subjects show that facial movements can be detected and classified at high accuracies. The pattern-based continuous mapping between facial movements and cursor actions achieved high performance in both a typing task and a drawing task. Ethics Statement This study was approved by the Committee for the Protection of Human Subjects (CPHS) of The University of Texas Health Science Center at Houston and TIRR Memorial Hermann (Houston, TX, USA). All procedures of the study were performed in accordance with the Declaration of Helsinki. The subject gave written and informed consent before the experimental procedures. Author Contributions ZL experimental design, subject recruitment, data collection, data analysis, and first draft of the manuscript. PZ conception, study design, data analysis, supervision, and manuscript revision. Both authors approved the final manuscript. Funding This study was supported by Mission Connect, a program of TIRR Foundation (Grant Number: 018–116). Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constru...
Source: Frontiers in Neurology - Category: Neurology Source Type: research