Charge Transport through Single-Molecule Junctions with σ-Delocalized Systems.

Single-molecule junctions, formed by a single molecule bridging a gap between two metal electrodes, are attracting attention as basic models of ultrasmall electronic devices. Although charge transport through π-conjugated molecules with π-delocalized system has been widely studied for a number of molecular junctions, there has been almost no research on charge transport through molecular junctions with a σ-delocalized orbital system. Compounds with hexa-selenium-substituted benzene form a σ-delocalized orbital system on the periphery of the benzene ring. In this study, we fabricated single-molecule junctions with the σ-delocalized orbital systems arising from lone-pair interactions of selenium atoms and clarified their electronic properties using the break-junction method. The single-molecule junctions with the σ-orbital systems show efficient charge transport properties and can be one of the alternatives to those with conventional π-orbital systems as minute electronic conductors.

Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography.

Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window and z-score normalization that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed method achieved 77.7% accuracy, an improvement of 21.5% compared to the non-normalization (56.2%). Furthermore, when using a model trained by other people's data for application without calibration, the proposed method achieved 63.1% accuracy, an improvement of 8.8% compared to the z-score (54.4%). These results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved.

Verification of Normalization Method to Improve Usability and Versatility among Users of Applications that Predict Continuous Motion Using Electromyography.

In applications using electromyography (EMG), it is important to ensure high performance for all users (versatility among users) and to enable use without prior preparation (usability). Some of the current applications that use EMG normalize the signal through methods based on the measured maximum absolute value of EMG (maEMG), such as dynamic contraction (DC). However, usability is low when using DC because the reference value must be measured first every time the application is used. Further, the versatility among users is low because of the nonlinearity of EMG and the fact that maEMG varies among users. This study aimed to improve usability and versatility among users for continuous classification tasks using EMG. To this end, we developed a normalization method using sliding-window and z-score normalization techniques. The results reveal that the proposed method exhibits higher usability and versatility among users than DC. The proposed method does not require any calibration time, suggesting improved usability, while yielding the same classification accuracy as DC (57% for three target tasks) for a model trained using a subject's own data. Further, for a model trained with other users' data, the proposed method yields a classification accuracy of 53%, which is 18% higher than that of DC (35%), suggesting versatility among users. These results demonstrate that the proposed normalization method improves usability and versatility for users of practical applications that use EMG and perform continuous classification, such as prosthetic hands.

Developing a Method to Control an Arm-Assist-Suit by Predicting Arm-Trajectory Using Electromyography.

Many researchers have developed assist-suits to support repetitive and strenuous physical labor, but existing suits show unsatisfactory responsiveness and restrict arm motions. Therefore, we propose a method for an arm-assist-suit that synchronizes arm motions by using electromyography (EMG) to predict arm trajectory. EMG is used to measure and record electrical signals while muscles are active. Further, predicted arm-joint motions and estimated arm-joint angles are used for arm trajectory predictions. In this study, we attempted the prediction of elbow-joint motions and the timing of motion changes. Two subjects executed twelve types of elbow-joint movements that had four start and endpoints. We measured seven muscle types with EMG points on the right arm(hand, elbow, and shoulder) a motion capture system, respectively. After processing these data, we applied a multiclass logistic regression, which is a machine-learning technique, to predict elbow-joint motions, namely, rest, flexion, and extension. The precision in elbow joint motion prediction shows a difference between the two subjects for the three motions analyzed. Additionally, the rest prediction accuracy is lower than both flexion and extension for each subject. The prediction of elbow-joint motion change timing does not correlate with the elbow-joint motion predictions, with the timing prediction precision being very low and thus, causing some difficulties. To overcome these difficulties, and improve precision in future work, we plan to apply an independent component analysis to eliminate noise and add or change features.Clinical Relevance- This study aims to establish a benchmark for future research on the improvement of responsiveness and range-of-motion of arm-assist-suits.