Title
Thumbs Up, Thumbs Down: Non-Verbal Human-Robot Interaction Through Real-Time Emg Classification Via Inductive And Supervised Transductive Transfer Learning
Abstract
In this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of'thumbs up','thumbs down', and'relax'in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a '20 Questions' game.
Year
DOI
Venue
2020
10.1007/s12652-020-01852-z
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
Keywords
DocType
Volume
Gesture classification, Human-robot interaction, Electromyography, Machine learning, Transfer learning, Inductive transfer learning, Supervised transductive transfer Learning, Myo armband, Pepper robot
Journal
11
Issue
ISSN
Citations 
12
1868-5137
1
PageRank 
References 
Authors
0.40
0
5
Name
Order
Citations
PageRank
Jhonatan Kobylarz110.40
Jordan J. Bird294.05
Diego R. Faria39514.96
Eduardo Parente Ribeiro4145.42
Anikó Ekart556462.28