A Novel Method for Cross-Subject Human Activity Recognition with Wearable Sensors

Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.

E3GCAPS: Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection

Cognitive state detection using electroencephalogram(EEG)signals for various tasks has attracted significant research attention.However,it is difficult to further improve the performance of crosssubject cognitive state detection.Further,most of the existing deep learning models will degrade significantly when limited training samples are given,and the feature hierarchical relationships are ignored.To address the above challenges,we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection,termed as Efficient EEG-based Multi-Capsule Framework(E3GCAPS).Specifically,we use a selfexpression module to capture the potential connections between samples,which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG.In addition,considering the strong correlation between cognitive states and brain function connection mode,the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data,in which multichannel 1D data greatly improving the training efficiency while preserving the model performance.The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset(FAAD)and the SJTU Emotion EEG Dataset(SEED).Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.

On educational system of legal translation course in colleges of China

With the broadening of economic and commercial communication between China and the outside world, the market of legal and contract document translation job is blooming harshly in both China and abroad. Accompanying this tendency, the professionalizing forensic development of translation is booming up as well. The request for higher education institutions to establish the education system of forensic translation is shining in human＇s eyes, and the cultivating system for the future, the world and profession is highly regarded. So comprehensive profession-cultivating, socialization and economy-serving should be the direction of the education of foreign languages department, related foreign forensic document translation and professional direction of public foreign language teaching as well are the aims of this reform of education. So the aim of this new education system should be the establishing the system of professional translation, forensic translation, cross-subject education and the cultivation of multi-professionals.

A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022

The rapid serial visual presentation(RSVP)paradigm has garnered considerable attention in brain–computer interface(BCI)systems.Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models.In this study,we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022.We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection.The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks.We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.

Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021

Electroencephalogram(EEG)data depict various emotional states and reflect brain activity.There has been increasing interest in EEG emotion recognition in brain-computer interface systems(BCIs).In the World Robot Contest(WRC),the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition.Three types of emotions(happy,sad,and neutral)are modeled using EEG signals.In this study,5 methods employed by different teams are compared.The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition,whereas deep learning methods perform better in online cross-subject decoding.