Genetic algorithm-based motion estimation method using orientations and EMGs for robot controls

Jeongsook Chae, Yong Jin, Yunsick Sung, Kyungeun Cho

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%.

Original languageEnglish
Article number183
JournalSensors
Volume18
Issue number1
DOIs
StatePublished - 11 Jan 2018

Keywords

  • Bayesian probability
  • EMG
  • Genetic algorithm
  • Motion estimation
  • Myo device
  • Orientation
  • Weight

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