Identifying mild cognitive impairment (MCI) is vital for Alzheimer’s disease prevention. As neurodegenerative diseases progress, synchronous activity in electroencephalography (EEG) - indicating functional connectivity - changes due to neural system deterioration. Thus, developing geometric learning to decode the functional brain structure is essential. Techniques such as graph neural networks and Riemannian manifolds show potential in analyzing non-Euclidean data. However, existing approaches neglect to combine synchronous activity with temporal dependence and still remain insufficient for MCI detection. This paper proposes the Brain Network sequence-driven Manifold-based Transformer (BNMTrans) to identify MCI patterns from EEG data. BNMTrans leverages its strengths by extracting features from sequential brain networks through the self-attention mechanism, guided by the geometric correlations within the Riemannian manifold. By integrating long-term temporal dynamics and structural relationships within manifold space based on functional connectivity, this approach outperforms others in EEG feature comparisons and state-of-the-art evaluations based on clinical data from 89 subjects (46 MCI, 43 healthy controls) at a local hospital. Our work has significance for both MCI clinical management and technical progression in the EEG field.

Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing Manifold Attention and Confidence Stratification (MACS) to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. The MACS framework's effectiveness stems from these features: 1) The Augmentor generates various EEG-represented brain variants to enrich the data space; 2) The Switcher enhances the feature space for trusted samples and reduces overfitting on incorrectly labeled samples; 3) The Encoder uses the Riemannian manifold and Euclidean metrics to capture spatiotemporal variations and dynamic synchronization in EEG; 4) The Projector, equipped with dual heads, monitors consistency across multiple brain variants and ensures diagnostic accuracy; 5) The Stratifier adaptively stratifies learned samples by confidence levels throughout the training process; 6) Forward and backpropagation in MACS are constrained by confidence stratification to stabilize the learning system amid unreliable annotations. Our subject-independent experiments, conducted on both neurocognitive and movement disorders using cross-center corpora, have demonstrated superior performance compared to existing related algorithms. This work not only improves EEG-based diagnostics for cross-center and small-setting brain diseases but also offers insights into extending MACS techniques to other data analyses, tackling data heterogeneity and annotation unreliability in multimedia and multimodal content understanding. We have released our code here: https://github.com/ICI-BCI/EEG-MACS.

Recently, depression recognition through EEG has gained significant attention. However, two challenges have not been properly addressed in prior automated depression recognition and classification studies: (1) EEG data lacks an explicit topological structure. (2) Capturing spatio-temporal features of EEG signals is difficult. In this paper, we propose Multi-scale Adaptive Spatial-Temporal Graph Convolutional Network (MAST-GCN) for mining latent topological structure among EEG channels and capturing discriminative spatio-temporal features. First, we integrate Adaptive Graph Convolution (AGC) that merges the inherent graph construction method with a data-driven graph reconstruction method. The model uses attention mechanism to learn an adaptive topological structure and semantic information from different layers and classes. Second, we propose Multi-Scale Time Convolutional Layer (MS-TCL), which captures long-term dependence from EEG data. Since Graph Convolution is weak for aggregating the spatio-temporal information, we have implemented a 3D Graph Convolution (G3D) to directly capture the spatio-temporal dependencies by reconstructing the spatio-temporal graph. The experimental results demonstrate that MAST-GCN consistently outperforms state-of-the-art methods on two datasets. Furthermore, we use the gradient-based saliency maps for interpretability analysis, discovering the active brain regions and important electrode pairs related to depression.

Human gait refers to a daily motion that represents not only mobility but can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker’s emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech, and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. Finally, we discuss possible future developments of efficient and effective gait-based emotion recognition using state-of-the-art techniques in intelligent computation and big data.

The diagnosis of cognitive impairment (CI), here referred to as mild cognitive impairment (MCI) and probable Alzheimer’s disease (AD), is complicated in practice. Early AD diagnosis using electroencephalography (EEG) has attracted attention due to EEG’s advantages in data accessibility. Because of limited, sparse, and ambiguous labels, which are commonly encountered in the EEG-based diagnosis of CI, it is desirable to develop a learning framework to effectively capture CI-related representations beyond fully supervised learning. Therefore, this work explored the possibility of weakly-supervised learning in identifying MCI, AD, and normal aging patterns based on incompletely reliable labels. To address the problem, we proposed a framework containing a dual-contrastive learning structure and a multi-level temporal-spectral EEG encoder, which transformed EEG signals into embeddings and automatically updated the ambiguous labels through intra-subject and cross-subject contrastive learning. We verified the method’s performance based on 54 subjects (18 in each group). Our findings provide new insights into the accurate inference of refractory CI diseases based on non-ideal data sources.

Person re-identification (Re-ID) via gait features within 3D skeleton sequences is a newly-emerging topic with several advantages. Existing solutions either rely on hand-crafted descriptors or supervised gait representation learning. This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID. Specifically, we first create self-supervision by learning to reconstruct unlabeled skeleton sequences reversely, which involves richer high-level semantics to obtain better gait representations. Other pretext tasks are also explored to further improve self-supervised learning. Second, inspired by the fact that motion's continuity endows adjacent skeletons in one skeleton sequence and temporally consecutive skeleton sequences with higher correlations (referred as locality in 3D skeleton data), we propose a locality-aware attention mechanism and a locality-aware contrastive learning scheme, which aim to preserve locality-awareness on intra-sequence level and inter-sequence level respectively during self-supervised learning. Last, with context vectors learned by our locality-aware attention mechanism and contrastive learning scheme, a novel feature named Constrastive Attention-based Gait Encodings (CAGEs) is designed to represent gait effectively. Empirical evaluations show that our approach significantly outperforms skeleton-based counterparts by 15-40 percent Rank-1 accuracy, and it even achieves superior performance to numerous multi-modal methods with extra RGB or depth information. Our codes are available at https://github.com/Kali-Hac/Locality-Awareness-SGE.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

Action recognition is essential for many human-centered applications in the Internet of Things (IoT). Especially, in the Internet of Medical Things (IoMT), action recognition shows great importance in surgical assistance, patient monitoring, etc. Recently, 3-D skeleton sequence-based action recognition draws broad attention. It is a challenging task that needs effective modeling on intraframe skeleton representations and interframe temporal dynamics. Standard long short-term memory (LSTM)-based models are widely used for sequence modeling due to its long-term memory, yet they are unable to fully model the relationship between different body joints or persons to extract crucial co-occurrence features from different levels. To handle this shortcoming, we propose an attention-based multilevel co-occurrence graph convolutional LSTM (AMCGC-LSTM). By integrating graph convolutional networks (GCNs) into LSTM, the proposed model is capable of leveraging body structural information from skeletons and strengthening the multilevel co-occurrence (MC) feature learning. Specifically, we first design the spatial attention module for feature enhancement of key joints from skeleton inputs. Second, we design MC memory units coupled with GCN to automatically model the spatial relationship between joints, and simultaneously capture the co-occurrence features from different joints, persons, and frames. Finally, we construct aggregated features of MCs (AFMCs) from MC memory units to better represent the intraframe action context encoding, and leverage a concurrent LSTM (Co-LSTM) to further model their temporal dynamics for action recognition. Our model significantly outperforms mainstream methods on NTU RGB+D 60/120 data set, mutual action subset of NTU RGB+D 60/120 data set, and Northewestern-UCLA data set.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

Meige's syndrome, one type of the segmental cranial dystonia, is characterized by blepharospasm and oromandibular dystonia and can be involved with involuntary movement of lower facial muscles, mouth, pharyngeal or cervical muscles. The exact pathophysiology is still obscure. Clinically, there are no curative drugs and the therapeutic effects of botulinum toxin injection is limited. Repetitive transcranial magnetic stimulation (rTMS), one of the noninvasive techniques of brain stimulation, can be able to induce lasting changes of cortical excitability and remodeling of brain networks. In this study, we first reported the beneficial and long-lasting effect of cerebellar low-frequency rTMS on the patient with Meige's syndrome, which seems to offer the alternative therapeutic method for the disease.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

With the outbreak of COVID-19, people’s demand for using the Internet of Medical Things (IoMT) for physical health monitoring has increased dramatically. The considerable amount of data requires stable, reliable, and real-time transmission, which has become an urgent problem to be solved. This paper constructs a health monitoring-enabled IoMT network which is composed of several users carrying wearable devices and a coordinator. One of the important problems for the proposed network is the unstable and inefficient transmission of data packets caused by node congestion and link breakage in the routing process. Based on these, we propose a Q -learning-based dynamic routing selection (QDRS) algorithm. First, a mathematical model of path optimization and a solution named Global Routing selection with high Credibility and Stability (GRCS) is proposed to select the optimal path globally. However, during the data transmission through the optimal path, the node and link status may change, causing packet loss or retransmission. This is a problem not considered by standard routing algorithms. Therefore, this paper proposes a local link dynamic adjustment scheme based on GRCS, using the Q -learning algorithm to select the optimal next-hop node for each intermediate forwarding node. If the selected node is not the same as the original path, the chosen node replaces the downstream node in the original path and so corrects the optimal path in time. This paper considers the congestion state, remaining energy, and mobility of the node when selecting the path and considers the network state changes during packet transmission, which is the most significant innovation of this paper. The simulation results show that compared with other similar algorithms, the proposed algorithm can significantly improve the packet forwarding rate without seriously affecting the network energy consumption and delay.

As an emerging communication platform, opportunistic Cloud of Things (CoT) is promising for clients to exchange messages through opportunistic contacts in cloud computing-enabled Internet of Things. Recently, numerous socially aware schemes have been put forward, leveraging users’ social attributes and contact history to predict future contacts with the purpose of improving message forwarding efficiency and network throughput. However, individual privacy is generally overlooked in the prediction process and transmission stage of opportunistic CoT. In this paper, we construct a privacy-preserving message forwarding framework for opportunistic CoT to guarantee individual privacy and improve transmission efficiency. We first set up a two-layer architecture of a cloud server to improve communication efficiency for terminal clients. By integrating a security-based mobility prediction algorithm with a routing decision process, our scheme can effectively protect individual privacy. We integrate an attribute-based cryptographic algorithm with a message delivery process to enable our scheme to resist attacks, such as Sybil attack, drop for profit, and data tampered attack. Compared with some existing solutions, our scheme improves network security significantly at the cost of slightly increased communication overhead.