Effects of Data Augmentation on the Nine-Axis IMU-Based Orientation Estimation Accuracy of a Recurrent Neural Network.

The nine-axis inertial and measurement unit (IMU)-based three-dimensional (3D) orientation estimation is a fundamental part of inertial motion capture. Recently, owing to the successful utilization of deep learning in various applications, orientation estimation neural networks (NNs) trained on large datasets, including nine-axis IMU signals and reference orientation data, have been developed. During the training process, the limited amount of training data is a critical issue in the development of powerful networks. Data augmentation, which increases the amount of training data, is a key approach for addressing the data shortage problem and thus for improving the estimation performance. However, to the best of our knowledge, no studies have been conducted to analyze the effects of data augmentation techniques on estimation performance in orientation estimation networks using IMU sensors. This paper selects three data augmentation techniques for IMU-based orientation estimation NNs, i.e., augmentation by virtual rotation, bias addition, and noise addition (which are hereafter referred to as rotation, bias, and noise, respectively). Then, this paper analyzes the effects of these augmentation techniques on estimation accuracy in recurrent neural networks, for a total of seven combinations (i.e., rotation only, bias only, noise only, rotation and bias, rotation and noise, and rotation and bias and noise). The evaluation results show that, among a total of seven augmentation cases, four cases including 'rotation' (i.e., rotation only, rotation and bias, rotation and noise, and rotation and bias and noise) occupy the top four. Therefore, it may be concluded that the augmentation effect of rotation is overwhelming compared to those of bias and noise. By applying rotation augmentation, the performance of the NN can be significantly improved. The analysis of the effect of the data augmentation techniques presented in this paper may provide insights for developing robust IMU-based orientation estimation networks.

Antibacterial Activities of Prenylated Isoflavones from Maclura tricuspidata against Fish Pathogenic Streptococcus: Their Structure-Activity Relationships and Extraction Optimization.

Streptococcus zoonotic bacteria cause serious problems in aquaculture with clinical effects on humans. A structure-antibacterial activity relationships analysis of 22 isoflavones isolated from M. tricuspidata (leaves, ripe fruits, and unripe fruits) against S. iniae revealed that prenylation of the isoflavone skeleton was an important key for their antibacterial activities (minimum inhibitory concentrations: 1.95-500 µg/mL). Through principal component analysis, characteristic prenylated isoflavones such as 6,8-diprenlygenistein (4) were identified as pivotal compounds that largely determine each part's antibacterial activities. M. tiricuspidata ripe fruits (MTF), which showed the highest antibacterial activity among the parts tested, were optimized for high antibacterial activity and low cytotoxicity on fathead minnow cells using Box-Behnken design. Optimized extraction conditions were deduced to be 50%/80 °C/7.5 h for ethanol concentration/extraction temperature/time, and OE-MTF showed contents of 6,8-diprenlygenistein (4), 2.09% with a MIC of 40 µg/mL. These results suggest that OE-MTF and its active isoflavones have promising potential as eco-friendly antibacterial agents against streptococcosis in aquaculture.