DocumentCode :
2950014
Title :
Automatic user identification by using forearm biometrics
Author :
Cannan, James ; Huosheng Hu
Author_Institution :
Comput. Sci. & Electron. Eng. Dept., Univ. of Essex, Colchester, UK
fYear :
2013
fDate :
9-12 July 2013
Firstpage :
710
Lastpage :
715
Abstract :
Electromyography (EMG) based human machine muscle interfaces hold great potential for interfacing the complexity of our body, with a multitude of electronic devices. However, the lack of compensationary methods for adapting systems from one user to another, prevents us achieving easy to use devices. This paper presents a method for enhancing EMG usability, which is based on biometrically identifying a user, so that previous training data can be automatically retrieved. This minimizes the need for small groups of people to repeatedly re-train a system over a short to medium time frame. Experiments were performed to test how EMG, circumference, as well as a combination of both, can be used as a biometric for identifying 4 users, in small group sizes of 4, 10 and 19. The results show average identification accuracies across all 11 gestures of 55.32%, 75.44% and 90.32%, for groups of 19,10 and 4 subjects respectively, while attaining the best single gesture identification accuracies of 60.04%, 82.8% and 100%.
Keywords :
biometrics (access control); electromyography; gesture recognition; human computer interaction; EMG based human machine muscle interfaces; EMG usability; automatic user identification; electromyography based human machine muscle interfaces; electronic devices; forearm biometrics; gesture identification accuracies; medium time frame; short time frame; Accuracy; Biomedical monitoring; Biometrics (access control); Electrodes; Electromyography; Muscles; Training; Biometric; Bionics; Circumference; Electromyography (EMG); Human Machine Interfaces (HMI);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Intelligent Mechatronics (AIM), 2013 IEEE/ASME International Conference on
Conference_Location :
Wollongong, NSW
ISSN :
2159-6247
Print_ISBN :
978-1-4673-5319-9
Type :
conf
DOI :
10.1109/AIM.2013.6584176
Filename :
6584176
Link To Document :
بازگشت