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Article

Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls

Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea
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Author to whom correspondence should be addressed.
Sensors 2018, 18(1), 183; https://doi.org/10.3390/s18010183
Received: 15 November 2017 / Revised: 31 December 2017 / Accepted: 5 January 2018 / Published: 11 January 2018
(This article belongs to the Special Issue Internet of Things and Ubiquitous Sensing)
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%. View Full-Text
Keywords: motion estimation; Bayesian probability; Myo device; orientation; EMG; genetic algorithm; weight motion estimation; Bayesian probability; Myo device; orientation; EMG; genetic algorithm; weight
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MDPI and ACS Style

Chae, J.; Jin, Y.; Sung, Y.; Cho, K. Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls. Sensors 2018, 18, 183. https://doi.org/10.3390/s18010183

AMA Style

Chae J, Jin Y, Sung Y, Cho K. Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls. Sensors. 2018; 18(1):183. https://doi.org/10.3390/s18010183

Chicago/Turabian Style

Chae, Jeongsook, Yong Jin, Yunsick Sung, and Kyungeun Cho. 2018. "Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls" Sensors 18, no. 1: 183. https://doi.org/10.3390/s18010183

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