Title
Rock-Paper-Scissors Prediction Experiments Using Muscle Activations
Abstract
Human motion prediction is becoming more and more important issue in the filed of wearable robots or biorobotics. This paper provides an initial experimental result for human motion prediction. In detail, the prediction method for ternary choice among rock-paper-scissors is presented using temporal patterns of muscle activations (Electromyography, in short EMG) controlling hand motion of subject. Initial burst part of EMG is prior to the onset of actual movement by dozens to hundreds milliseconds. Using this property, the proposed method makes the ternary choice prediction among rock-paper-scissors as soon as 10% motion variation of any finger is detected. It is shown experimentally that the success rate of the proposed prediction method is over 95%. © 2012 IEEE.
Publication Date
12-1-2012
Publication Title
IEEE International Conference on Intelligent Robots and Systems
Number of Pages
5133-5134
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/IROS.2012.6386264
Copyright Status
Unknown
Socpus ID
84872345497 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84872345497
STARS Citation
Jang, Giho; Choi, Youngjin; and Qu, Zhihua, "Rock-Paper-Scissors Prediction Experiments Using Muscle Activations" (2012). Scopus Export 2010-2014. 3989.
https://stars.library.ucf.edu/scopus2010/3989