Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.

Jump locomotion is the basic movement of human. However, no thorough research on the recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor information fusion and machine learning to recognize the human jump phase, which is crucial to the development of exoskeleton that assists jumping. The method of information fusion for sensors including sEMG, IMU, and footswitch sensor is studied. The footswitch signals are filtered by median filter. A processing method of synthesizing Euler angles into phase angle is proposed, which is beneficial to data integration. The jump locomotion is creatively segmented into five phases. The onset and offset of active segment are detected by sample entropy of sEMG and standard deviation of acceleration signal. The features are extracted from analysis windows using multi-sensor information fusion, and the dimension of feature matrix is selected. By comparing the performances of state-of-the-art machine learning classifiers, feature subsets of sEMG, IMU, and footswitch signals are selected from time domain features in a series of analysis window parameters. The average recognition accuracy of sEMG and IMU is 91.76% and 97.68%, respectively. When using the combination of sEMG, IMU, and footswitch signals, the average accuracy is 98.70%, which outperforms the combination of sEMG and IMU (97.97%, p < 0.01). Graphical Abstract The sub-phases of human locomotion are recognized based on multi-sensor information fusion and machine learning method. The feature data of the sub-phases is visualized in 3-dimensional space. The predicted states and the true states in a complete jump are compared along the time axis.

Hexagonal Zr3X (X = Al, Ga, In) Metals: High Dynamic Stability, Nodal Loop, and Perfect Nodal Surface States.

In recent years, topological semimetals/metals, including nodal point, nodal line, and nodal surface semimetals/metals, have been studied extensively because of their potential applications in spintronics and quantum computers. In this study, we predict a family of materials, Zr3X (X = Al, Ga, In), hosting the nodal loop and nodal surface states in the absence of spin-orbit coupling. Remarkably, the energy variation of the nodal loop and nodal surface states in Zr3X are very small, and these topological signatures lie very close to the Fermi level. When the effect of spin-orbit coupling is considered, the nodal loop and nodal surface states exhibit small energy gaps (<25 and 35 meV, respectively) that are suitable observables that reflect the spin-orbit coupling response of these topological signatures and can be detected in experiments. Moreover, these compounds are dynamically stable, and they consequently form potential material platforms to study nodal loop and nodal surface semimetals.

Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning.

Background and Objective The lower limb activity of recognition of the elderly, the weak, the disabled and the sick is an irreplaceable role in the caring of daily life. The main purpose of this study is to assess the feasibility of using the surface electromyography (sEMG) signal and inertial measurement units (IMUs) data to determine the optimal fusion features and classifier for the study of daily ambulation mode recognition. Methods We have carried out several steps of experiments to obtain and test the optimal combination of the sEMG data and the body motion data at the feature level and the most suitable machine learning classification algorithm. Firstly, the sEMG and IMUs signals of eighteen participants performing four different ambulatory activities have recorded using wearable sensors. Secondly, several features of the sEMG sensors and IMU data were extracted and tested by the Markov Random Field based Fisher-Markov feature selector. Finally, four ML classifiers with several feature combinations were estimated with sensitivity, precision and recognition accurate rate of ambulatory activity classification. Results The results of this work showed that all selected features were significantly statistical difference in four ambulation modes. The principal component analysis was used to reduce the dimension of selected sEMG features and IMU features to form a fusion feature input support vector machine classifier, which could predict ambulatory activities with good classification performance. Conclusions It is concluded that the results demonstrate the feasibility of the ML classification model, which could provide a more novel way to guarantee the recognition rate and effectiveness of monitor daily ambulatory activity.