Signer-independent classification of American sign language word signs using surface EMG
Cassandra Derr, Ferat Sahin · 2017
The field of Sign Language Recognition (SLR) has become an increasingly popular research topic. The goal of this study is an SLR system that will be capable of identifying a subset of 50 of the most common American Sign Language (ASL) word signs using surface electromyography and accelerometer data for multiple signers. All data was collected from deaf, fluent ASL users. A windowing approach is used with different time domain features for feature extraction. The samples are divided into one and two-handed signs, each of which are used to train a Support Vector Machine classifier. Samples from all but one subject are used to train the classifiers. The classifiers are then tested on both data held out from the subjects used for training and the subject that was left out. The resulting system had an average accuracy of 59.96% for trained subjects and 33.66% for the subject left out. To compare this approach to others, 40-word and 7-word sign sets are trained and tested using this method. The proposed system performed comparably with literature for the 40-word set, and better for the 7-word set.