Multi-sensor fusion strategies have been widely applied in Human Activity Recognition (HAR) in Body Sensor Networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this paper, Dezert-Smarandache Theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the Kernel Density Estimation (KDE) based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of Basic Belief Assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to deal with the conflict between the obtained BBAs, Proportional Conflict Redistribution-6 (PCR6) is applied to fuse the acquired BBAs. Moreover, the missing data of the involved sensors are addressed as ignorance in the framework of the DSmT without manual interpolation or intervention. Experimental studies on two real-world activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) were conducted, and the results showed the superiority of our proposed method over some state-of-the-art approaches proposed in the literature.

In the original belief function (BF) theory, a precise-valued belief structure has been widely used to represent uncertain information. However, this mentioned belief structure is difficult to effectively measure the specific hesitant situation, especially when decision makers have a set of possible values for the belief assignments of focal elements. In order to model the hesitant nature of the behavior of people to make a decision under uncertainty, we propose a hesitant fuzzy belief structure (HFBS) that is based on the BF theory and the recent hesitant fuzzy set theory. We also present the novel rule of combination of HFBS that is used and evaluated in a wearable human activity recognition (HAR) system coupled with an extreme learning machine. The evaluation of this new HFBS approach is done from two benchmark datasets. We clearly show its effectiveness and its superiority compared to various methods used classically for the wearable HAR.

In recent years, wearable sensor-based human activity recognition (HAR) is becoming more and more attractive, especially in health monitoring and sports management. However, in order to obtain high-quality HAR, it is often necessary to get sufficient labeled activity data, which is very difficult, time-consuming, and costly in a natural environment. To tackle this problem, multisource domain adaptation (DA) is a promising method that aims to learn enough multisource prior knowledge from labeled activity data, and then transfer this learned knowledge to the target unlabeled dataset. Thus, this article presents a novel multisource weighted DA with evidential reasoning (w-MSDAER) for HAR, which can effectively utilize complementary knowledge between multiple sources. Specifically, we first use the strategy of distribution alignment to learn local domain-invariant classifiers based on multisource domains. And then the reliabilities of these derived classifiers are comprehensively evaluated according to the belief function based technique for order preference by similarity to ideal solution (BF-TOPSIS). Finally, the discounting fusion method is used to fuse the local classification results. Comprehensive experiments are conducted on two open-source datasets, and the results show that the proposed w-MSDAER significantly outperforms other state-of-art methods.

Decoding human visual representations from brain activity data is a challenging but arguably essential task with an understanding of the real world and the human visual system. However, decoding semantically similar visual representations from brain recordings is difficult, especially for electroencephalography (EEG), which has excellent temporal resolution but suffers from spatial precision. Prevailing methods mainly focus on matching brain activity data with corresponding stimuli-responses using contrastive learning. They rely on massive and high-quality paired data and omit semantically aligned modalities distributed in distinct regions of the latent space. This paper proposes a novel Multimodal Bidirectional Cycle Consistency (MB2C) framework for learning robust visual neural representations. Specifically, we utilize dual-GAN to generate modality-related features and inversely translate back to the corresponding semantic latent space to close the modality gap and guarantee that embeddings from different modalities with similar semantics are in the same region of representation space. We perform zero-shot tasks on the ThingsEEG dataset. Additionally, we conduct EEG classification and image reconstruction on both the ThingsEEG and EEGCVPR40 datasets, achieving state-of-the-art performance compared to other baselines.

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain–computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

Wearable human activity recognition has raised great interest among researchers in the field of Body Sensor Networks (BSNs). However, the classification of human complex activities, especially those similar activities such as walking upstairs and downstairs, is very challenging for the design of efficient activity recognition models. Considering that the hierarchical data structure is helpful to improve the performance of classifiers in effectively detecting the inter-class differences, here, we propose a novel hierarchical activity recognition method based on Belief Functions (BFs) theory. Specifically, in the training stage, we first use Long-Short Term Model (LSTM) to classify the predefined activities and then determine the similarities between each pair of activities through a confusion matrix. Based on this, the activities can be organized into a tree-based hierarchical structure through hierarchical clustering and then the Extreme Learning Machine (ELM) model for each nonleaf node in hierarchical structure can be trained, respectively. In the testing stage, we also propose a novel fast tree-based hierarchical combination rule to fuse the outputs of all trained ELMs. Finally, the proposed hierarchical activity recognition is evaluated by using two widely used UCI datasets: Smartphone and mHealth. The experimental results show that the proposed method is more accurate than the state-of-art methods.