Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction.

Objective.Recent technological advances show the feasibility of fusing surface electromyography (sEMG) signals and movement data to predict lower limb ambulation intentions. However, since the invasive fusion of different signals is a major impediment to improving predictive performance, searching for a non-invasive (NI) fusion mechanism for lower limb ambulation pattern recognition based on different modal features is crucial.Approach. We propose an end-to-end sequence prediction model with NI dual attention temporal convolutional networks (NIDA-TCNs) as a core to elegantly address the essential deficiencies of traditional decision models with heterogeneous signal fusion. Notably, the NIDA-TCN is a weighted fusion of sEMG and inertial measurement units with time-dependent effective hidden information in the temporal and channel dimensions using TCN and self-attentive mechanisms. The new model can better discriminate between walking, jumping, downstairs, and upstairs four lower limb activities of daily living.Main results. The results of this study show that the NIDA-TCN models produce predictions that significantly outperform both frame-wise and TCN models in terms of accuracy, sensitivity, precision, F1 score, and stability. Particularly, the NIDA-TCN with sequence decision fusion (NIDA-TCN-SDF) models, have maximum accuracy and stability increments of 3.37% and 4.95% relative to the frame-wise model, respectively, without manual feature-encoding and complex model parameters.Significance. It is concluded that the results demonstrate the validity and feasibility of the NIDA-TCN-SDF models to ensure the prediction of daily lower limb ambulation activities, paving the way to the development of fused heterogeneous signal decoding with better prediction performance.

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.

Distributed cooperative output regulation of heterogeneous linear multi-agent systems based on event- and self-triggered control with undirected topology.

In this note, the cooperative output regulation problem is considered for a kind of heterogeneous linear multi-agent systems under undirected communication topological graphs. Our approach consists of two techniques, the design of distributed dynamic observer for exosystem under the event-triggered and self-triggered control mechanisms. Firstly, a distributed event-triggered control protocol is designed, which proves that multi-agent system can achieve stability and Zeno behavior is excluded under this protocol. Secondly, a self-triggered control protocol is designed to achieve the cooperative output regulation problem of heterogeneous multi-agent system. Moreover, by designing parameter in the self-triggered mechanism, the Zeno behavior does not exist. Finally, the theoretical results are verified by a simulation of output regulation problem under two triggered mechanisms.