Rock-Paper-Scissors Prediction Experiments using Muscle Activations
- Authors
- Jang, Giho; Choi, Youngjin; Qu, Zhihua
- Issue Date
- Oct-2012
- Publisher
- IEEE
- Citation
- 2012 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), pp 5133 - 5134
- Pages
- 2
- Indexed
- SCIE
SCOPUS
- Journal Title
- 2012 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
- Start Page
- 5133
- End Page
- 5134
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/36310
- DOI
- 10.1109/IROS.2012.6386264
- ISSN
- 2153-0858
2153-0866
- 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%.
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