[This corrects the article DOI: 10.1371/journal.pcbi.1013667.].

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

Wearable activity recognition is crucial for ubiquitous computing, enhancing human-machine interaction, medical monitoring, and personalized services. As wearable devices collect user activity data that often contain personal privacy information, federated learning (FL) is increasingly applied to protect user data privacy. However, in real-world scenarios, users' data are commonly exhibit heterogeneity, manifesting as non-independent and identically distributed (non-IID) characteristics, which presents challenges for FL methods. Traditional FL client selection approaches with heterogeneous data can cause global model drift, reducing the accuracy of activity recognition models. In this paper, we propose Data Contribution-Based Federated Learning (DCBFL) method, an adaptive FL training approach by selecting clients to counter the problem caused by heterogeneous data. Specifically, we first utilize a conditional generator on the server to construct an auxiliary dataset, which is used to train an auxiliary model as a benchmark to measure the degree of heterogeneity in each client's data. Furthermore, we reasonably differentiate the data contributions of clients based on the degree of data heterogeneity and select suitable clients for FL training, effectively utilizing heterogeneous data information, mitigating global model drift. The comprehensive experiments are conducted on five public activity recognition datasets under non-IID conditions in this work. The experimental results show that DCBFL outperforms existing baseline methods, showcasing superior performance.

The rapid development of spatial transcriptomics (ST) has made it possible to effectively integrate gene expression and spatial information of cells and accurately identify spatial domains. A large number of deep learning (DL)-based methods have been proposed to perform spatial domain identification and achieved impressive results. However, these methods have some limitations. First, these methods rely on a fixed similarity metric and cannot fully utilize neighborhood information. Second, they cannot efficiently and adaptively integrate key information when fusing and reconstructing gene expression using purely additive methods. Finally, these methods ignore key nonlinear features and introduce noise during clustering. To address these limitations, we propose a novel DL model SpaMWGDA based on multi-view weighted fused graph convolutional network (GCN) and data augmentation. By modeling spatial information using different similarity metrics, the model is able to successfully capture comprehensive neighborhood information of the spot features. By combining data augmentation and contrastive learning, SpaMWGDA is able to learn key gene expressions. SpaMWGDA uses a multi-view GCN encoder to model the similarities between spatial information and gene features, and uses a view-level attention mechanism for weighted fusion to adaptively learn the dependencies between them and learn the key features of each view. Experimental results not only demonstrate that SpaMWGDA outperforms competing methods in spatial domain identification and trajectory inference but also show the ability of SpaMWGDA to analyse tissue structure and function. The source code for SpaMWGDA is available at https://github.com/nathanyl/SpaMWGDA .

Federated learning (FL) has been an effective way to train a machine learning model distributedly, holding local data without exchanging them. However, due to the inaccessibility of local data, FL with label noise would be more challenging. Most existing methods assume only open-set or closed-set noise and correspondingly propose filtering or correction solutions, ignoring that label noise can be mixed in real-world scenarios. In this article, we propose a novel FL method to discriminate the type of noise and make the FL mixed noise-robust, named FedMIN. FedMIN employs a composite framework that captures local-global differences in multiparticipant distributions to model generalized noise patterns. By determining adaptive thresholds for identifying mixed label noise in each client and assigning appropriate weights during model aggregation, FedMIN enhances the performance of the global model. Furthermore, FedMIN incorporates a loss alignment mechanism using local and global Gaussian mixture models (GMMs) to mitigate the risk of revealing samplewise loss. Extensive experiments are conducted on several public datasets, which include the simulated FL testbeds, i.e., CIFAR-10, CIFAR-100, and SVHN, and the real-world ones, i.e., Camelyon17 and multiorgan nuclei challenge (MoNuSAC). Compared to FL benchmarks, FedMIN improves model accuracy by up to 9.9% due to its superior noise estimation capabilities.

Collaborative crowdsensing (CCS) requires the recruited team to collaborate closely to complete sensing tasks with high quality of service (QoS). The team recruitment of CCS is mainly influenced by the subjective willingness of participants and the objective trust evaluation of the sensing platform; that is, the higher the subjective mutual willingness to work together and the objective mutual trust among participants, the more efficiency with which the CCS tasks will be achieved. However, the existing research lacks comprehensive consideration of mutual willingness and mutual trust among recruited participants. This results in poor QoS. To address this problem, we propose a novel team recruitment method for CCS that jointly considers the willingness and trust to recruit optimal teams. First, we build a graph convolutional network‐based willingness‐trust network (GCN‐WTN) model for CCS to obtain mutual willingness and trust among participants more accurately. Second, we propose a willingness and trust‐based team recruitment (WT‐TR) method to recruit the optimal teams for CCS. This method introduces the consensus and similarity constraints into the willingness and trust networks to better meet the collaboration needs of CCS. Finally, we implement a recruitment simulation platform for CCS to simulate the team recruitment process and validate the effectiveness of our proposed method. The experimental results show that the teams recruited by the proposed method can significantly improve QoS for CCS.

With the aid of graph convolution neural network and transformer model, human action recognition has achieved significant performance based on skeleton data. However, the majority of existing works rarely focus on identifying fine-grained motion information (i.e., “read”, “write”, etc.). Furthermore, they tend to explore correlations between joints and bones ignoring the angular information. Consequently, the recognition accuracy for fine-grained actions with most models is still less desired. To address this issue, we first attempt to bring angular information as a complement to familiar joint and bone information, while learning the potential dependencies of the three kinds of information using graph neural networks. Based on this, we propose a self-attention-enhanced graph neural network (SAE-GNN), which consists of a kernel-unified graph convolution (KUGC) module and an enhanced attention graph convolution (EAGC) module. The KUGC module is devised to effectively extract rich features in the skeleton information. The EAGC consisting of a multi-scale enhanced graph convolution block and a multi-headed self-attention block is designed to learn the potential high-level semantic information in the features. Besides, we introduce contrastive learning in the two blocks to enhance feature representation by maximizing their mutual information. We conduct extensive experiments on four publicly available datasets, and results show that our model outperforms state-of-the-art methods in recognizing fine-grained actions.

Wearable-based human activity recognition (HAR) is commonly employed in real-world scenarios such as health monitoring, auxiliary diagnosis, etc. As implementing activity recognition is a daunting challenge in an open dynamic environment, incremental learning has become a common method to adapt to variable behavior patterns of users and create dynamic modeling in activity recognition. However, catastrophic forgetting is a significant challenge with incremental learning. This is contrary to our expectations of identifying new activity classes while remembering existing ones. To address this problem, we propose a knowledge importance-based class incremental learning method called KiCi and construct an incremental learning model based on the framework of self-iterative knowledge distillation for dynamic activity recognition. To eliminate the prediction bias of the teacher model on the old knowledge, we utilize the trained weights of previous incremental steps generated by the teacher model as the prior knowledge to obtain knowledge importance. Then use it to make the student model have a reasonable trade-off between old and new knowledge and mitigate catastrophic forgetting by avoiding negative transfer. We conduct extensive experiments on four public HAR datasets and our method consistently outperforms the existing state-of-the-art methods by a large margin.

Surface electromyography (sEMG) is a kind of bioelectric signal of the human body, containing a wealth of action intentions. Among various gesture recognition solutions, sEMG-based solutions show irreplaceable advantages by directly sensing and parsing human muscle activities and converting them into interactive commands. However, sEMG is sensitive to many factors related to users, and there are individual differences among different users. The gesture recognition model trained based on the data of existing users has poor recognition accuracy on the data of new users directly. Excitingly, transfer learning breaks through the independently identical distribution (I.I.D.) assumption of data in different domains, so it shows great potential for cross-user sEMG gesture recognition. Therefore, we propose a Multi-Source Integration based Transfer Learning (MSITL) method to explore cross-user gesture recognition in this paper. MSITL is composed of two main parts, the Source Model Construction Strategy (SMCS) and the Target Model Adaptation Strategy (TMAS). SMCS is a layered integration model. The first layer builds a model for each user. The second layer integrates multiple models through simple majority voting. TMAS is mainly divided into three steps. The first step is to use the target domain data to evaluate the source domain model and obtain the evaluation score of the individual classifier; The second step is to fine-tune the individual classifiers under the guidance of the evaluation scores; The third step is to integrate the adjusted model. Detailed experiments are conducted on benchmark sEMG gesture recognition datasets, including NinaPro (i.e., DB1) and CapgMyo (i.e., DB-a, DB-b, and DB-c). The proposed method achieves significant improvements in performance compared with current state-of-the-art methods.

Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of new sensors in a dynamic environment is a significant challenge. In this paper, we propose a novel feature incremental learning method, namely the Feature Incremental Random Forest (FIRF), to improve the performance of an existing model with a small amount of data on newly appeared features. It consists of two important components - 1) a mutual information based diversity generation strategy (MIDGS) and 2) a feature incremental tree growing mechanism (FITGM). MIDGS enhances the internal diversity of random forests, while FITGM improves the accuracy of individual decision trees. To evaluate the performance of FIRF, we conduct extensive experiments on three well-known public datasets for activity recognition. Experimental results demonstrate that FIRF is significantly more accurate and efficient compared with other state-of-the-art methods. It has the potential to allow the dynamic exploitation of new sensors in changing environments.

Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging due to two main technical problems: 1) the size of data labeled by doctors is small, and 2) the available data tends to be highly imbalanced (the vast majority tend to be from normal subjects with only a small fraction from subjects with cognitive disorder). In order to effectively deal with these challenges to infer cognitive wellness from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The proposed MOCA first uses the random oversampling iterative random forest based feature selection method to reduce the feature space dimensionality and avoid overfitting, and then adds a bias in the optimization problem of weighted extreme learning machine to achieve good generalization ability in handling imbalanced small-sampling dataset. Experimental results on two real-world datasets including SVD and stroke patients show that MOCA can effectively reduce the rate of misdiagnosis and significantly outperform state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the health-care infrastructure.

Falls threaten the health and life of the elders heavily because they lead to injuries or even death. Therefore, a reliable monitoring and alarm mechanism is desperately in need to guarantee the quality of elders' life. In this paper, we propose a multistage machine learning method to perform fall detection and solve the false alarm and missing alarm problem in traditional fall detection methods. Our proposed method consists of three stages: 1) threshold filtering, 2) ELM classifier, and 3) orientation-based filtering. Our method utilizes a high-precision triaxial accelerometer to collect the relevant information. After filtered by our three-stage method, the signal can be determined whether it is a fall or not. Experimental results demonstrate that: different from the traditional state-of-art methods with a single machine learning classifier, our method can greatly reduce the missing alarm and false alarm rate on the premise of high accuracy for all detection.

Cerebral small vessel disease (SVD) is common in the elderly and is associated with loss of functional independence, institutionalization, and death. In this paper, we propose a coarse-to-fine feature selection method for accurate SVD detection and timely implementation of interventions. The proposed method first uses an Iterative Random Forest based Feature Selection (IRFFS) method to obtain the most representative features from a feature set that includes gait, balance, and agility performance features extracted from 17 predefined clinical actions. The method then uses the Feature Incremental Extreme Learning Machine (FIELM) model to further verify the discriminant ability of each kind of selected features. Our results demonstrate that the proposed method can effectively select the most significant features for SVD detection, which include gait and agility performance features. Our method achieves up to 91.44% classification accuracy, outperforming other state-of-the-art feature selection methods. Our findings also verify clinical observations indicating that the fine motor pattern features of upper and lower limbs are helpful for high-accuracy SVD detection.