Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea.
Sensors (Basel). 2018 Jan 11;18(1):183. doi: 10.3390/s18010183.
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%.
随着智能设备的发展，对交互式可穿戴设备的需求正在迅速增加。为了准确地将可穿戴设备用于远程机器人控制，应有效地分析和利用有限的数据。例如，通过测量其方向并基于这些方向数据计算贝叶斯概率，可以估计名为 Myo 设备的可穿戴设备的运动。由于 Myo 设备可以测量各种类型的数据，因此可以通过利用这些附加类型的数据来提高其运动估计的准确性。本文提出了一种基于加权贝叶斯概率和同时测量的数据（方向和肌电图（EMG））的运动估计方法。将估计的最可能运动视为最终的估计运动。因此，与仅使用单一类型数据的传统方法相比，可以提高识别精度。在我们的实验中，七个对象执行五个预定义的动作。当通过传统方法测量方向时，运动估计误差的总和为 37.3％；同样，当仅使用 EMG 数据时，所提出的方法的运动估计误差也为 37.3％。所提出的组合方法的误差为 25％。因此，该方法将运动估计误差减少了 12％。
