Ubiquitous wireless sensing has facilitated a variety of intelligent applications, yet its widespread deployment is constrained by manual calibration of the node locations, whereas existing localization methods generally depend on multiple fixed anchors for device-free topology estimation or require dedicated transceivers for device-based schemes, which limit accuracy and practical scalability. In this paper, we proposeNodeLoc, a universal two-stage node localization framework that integrates local topology estimation with global calibration through robot-assisted alignment, achieving multi-node localization with only one fixed anchor and without requiring any receivers on the robot. Specifically, the framework first establishes coarse-grained relative topology through inter-node signal measurements, and then employs the robot’s trajectories to refine global positions. Furthermore, to address inherent challenges of node localization (i.e., the non-uniqueness of local topology and the ambiguity in global position mapping), we design an optimized localization algorithm by exploring the geometric constraints and trajectory calibration of multiple receivers. A prototype system based on the proposed framework is implemented and evaluated in real-world indoor environments. Experimental results demonstrate that the system achieves 80% localization errors within 0.61mand orientation errors within 18.7°, while providing higher accuracy and significantly improved robustness and scalability compared to state-of-the-art approaches.

Multidevice cooperative wireless sensing offers a promising solution for human motion recognition, owing to its superior privacy preservation and robustness. In the sensing process, devices continuously extract features from channel echoes and transmit them to a fusion center for motion recognition over successive time slots. The intertwined sub-processes of sensing and communication jointly determine recognition accuracy, yet simultaneously compete for limited radio resources. Moreover, the dynamic nature of practical environments further complicates this interplay due to the presence of moving interference sources and time-varying number of cellular users sharing the available bandwidth. Therefore, it is of paramount importance to jointly optimize sensing and communication resource allocation among devices and across time slots, while meticulously accounting for the impacts of dynamic environment to maximize recognition accuracy. In this paper, we propose a task-oriented integrated sensing and communication (ISAC) system for multidevice cooperative wireless motion recognition in dynamic environments. Specifically, we formulate a joint sensing and communication resource allocation problem to maximize recognition accuracy, represented by a discriminant gain metric that explicitly accounts for both sensing quality and communication constraints. Since this problem is a fractional program, we transform the original sum-of-ratios objective function into an equivalently subtractive form that facilities the development of a two-step iterative offline optimization (TSIO) algorithm to achieve the benchmark performance. Furthermore, to effectively cope with dynamic environmental influences, we further design a multi-agent reinforcement learning (MARL)-based online optimization (MRLO) scheme, which predicts environmental conditions at the subsequent time slot and adaptively optimizes resource allocation. Extensive numerical results illustrate that the proposed algorithm significantly enhances the recognition accuracy with dynamic environment influences, compared to existing benchmark algorithms. It is also observed from results that the sensing performance primarily drives recognition accuracy when energy is limited, whereas communication performance becomes the dominant factor under bandwidth constraints.

Traditional liquid detection approaches are often time-intensive and invasive, typically requiring the opening of containers for examination. While recent initiatives have proposed several innovative solutions, including camera-based and vibration sensor-based techniques, these approaches still face limitations in terms of convenience. The development of radio frequency (RF) technology, particularly millimeter-wave (mmWave) radar, offers a promising solution for non-invasive and contactless liquid detection. In particular, during the past few years, a number of radar-based sensing systems have been developed to detect or identify liquids. However, little work has been done on the simultaneous detection of multiple liquids. To fill this gap, we design a novel composite reflection model, which overcomes the detection challenges due to composite interference and environmental reflections, by utilizing the consistency and uniqueness of the reflection signals from multiple liquid targets. Based on the proposed model, we develop a system namedMultiScanner, which is able to detect different types of liquids in multi-target scenarios, exhibiting high location independence without the need for extensive data training. Extensive experiments validate the effectiveness ofMultiScanner, achieving up to 95.91% accuracy in detecting 10 hazardous-normal liquid combinations in 2-target scenarios. Moreover, even in more complex 5-target scenarios, an detection accuracy of 86.49% can be obtained. To the best of our knowledge, this is the first study that uses RF signals for multi-liquid detection.

This study introduces a novel approach for preventing overexertion in home fitness through fine-grained detection of respiratory parameters. To overcome the robustness limitation associated with using a composite signal for wireless sensing, we introduce an optimization-based signal separation model. This model effectively disentangles composite signals into static and dynamic components, while preserving the intricate details of target movements or activities. Specifically, by constructing a reference signal derived from the dominant static component, we eliminate time-varying phase shifts and leverage the invariant property of the dynamic component’s amplitude for precise separation. A system calledFinerSenseis developed, which is able to accurately and robustly detect fine-grained respiratory parameters such as respiration rate, depth, and inhalation-to-exhalation ratio with accuracy rates exceeding 97%, 95%, and 91%, respectively. Extensive experiments show that the developed system outperforms state-of-the-art baselines significantly, empowering users to optimize exercise intensity and duration while mitigating the risk of overexertion. We believe that this work is able to facilitate the seamless transition of wireless sensing systems from laboratory prototypes to practical and user-friendly applications.

During the past few years, with the advent of large-scale pre-trained language models (PLMs), there has been a significant advancement in cross-domain text classification with limited labeled samples. However, most existing approaches still face the problem of excessive computation overhead. While some non-pretrained language models can reduce the computation overhead, the performance could sharply drop off. To resolve few-shot learning problems on resource-limited devices with satisfactory performance, we propose a prototype rectification framework, ProtoRectifier, based on pre-trained model distillation and episodic meta-learning strategy. Specifically, a representation refactor based on DistilBERT is developed to mine text semantics. Meanwhile, a novel prototype rectification approach (i.e., Mean Shift Rectification) is put forward by making full use of the pseudo labeled query samples, so that the prototype of each category can be updated during the meta-training phase without introducing additional time overhead. Experiments on multiple real-world datasets demonstrate that ProtoRectifier outperforms the state-of-the-art baselines, not only achieving high cross-domain classification accuracy but also reducing the computation overhead significantly.

During the last decade, there have been lots of efforts on wireless sensing using Wi-Fi signals, which can be divided into two categories, i.e., the pattern-based approach and the model-based approach. Recently, more and more attention has been paid on the model-based approach, mainly due to its superiority of no need for collecting a large dataset or retraining the model for new environments. However, existing models are mainly designed for Line-of-Sight (LoS) scenarios, which are not applicable to Non-Line-of-Sight (NLoS) scenarios, such as through-wall sensing. To bridge this gap, we put forward a through-wall wireless sensing model to reveal the sensing mechanism of Wi-Fi signals in NLoS scenarios. In particular, arefraction-awareFresnel zone model is developed by taking into account both the reflection propagation and the refraction propagation of Wi-Fi signals. For the first time, we discover that the geometric distribution of Fresnel zones becomes uneven, due to the difference in dielectric constants between the air and the wall. Specifically, some areas become denser and other areas become sparser, leading to thesqueeze effectandstretch effectof Fresnel zones. Inspired by the insight, we further put forward a new metric namedcompression-ratioto quantify the through-wall sensing capability of Wi-Fi signals. Meanwhile, a set of algorithms are developed to guide the deployment of Wi-Fi sensing systems. To validate the proposed model, we implement a through-wall respiration sensing prototype system. Experiments show that the respiration detection performance varies significantly when the user locates in different areas. Specifically, for two sensing locations (one in the compression area and the other in the expansion area) symmetrically distributed on both sides of the transceivers’ connection line, the difference in mean absolute errors (MAE) can exceed 3 times.

As a non-intrusive sensing approach, the gait-based human identification technique attracts extensive attention. For the gait-based human identification technique, the unique gait feature is captured and extracted. Owing to the strong environment robustness and good privacy protection, the radar, especially the single-input multiple-output (SIMO) Doppler radar, is proposed as a promising way to capture the gait feature. However, the existing SIMO Doppler radar-based methods require the person to walk along a straight-line trajectory, which hinders their practical application. In this paper, we propose a gait-based human identification system for the weakly constrained trajectory, called CovertEye. In CovertEye, the person can be identified, when he/she walks along variable directions. To this end, we propose a trajectory segmentation algorithm to divide the trajectory into many straight-line trajectory segments. Based on the trajectory segments, we design the gait-based human identification method. In particular, we propose a normalization method to eliminate the differences in the direction of movement and the length among trajectory segments. The normalized signal spectrogram is exploited for the deep learning based feature extraction and human identification. We develop a prototype of the CovertEye system. The extensive experimental results demonstrate that our proposed system can achieve the identification accuracy of 82:4%.

The proliferation of multimodal big data in cities provides unprecedented opportunities for modeling and forecasting urban problems, such as crime prediction and house price prediction, through data-driven approaches. A fundamental and critical issue in modeling and forecasting urban problems lies in identifying suitable spatial analysis units, also known as city region partition. Existing works rely on subjective domain knowledge for static partitions, which is general and universal for all tasks. In fact, different tasks may need different city region partitions. To address this issue, we propose JLPR , a task-oriented framework for J oint L earning of region P artition and R epresentation. To make partitions fit tasks, JLPR integrates the region partition into the representation model training and learns region partitions using the supervision signal from the downstream task. We evaluate the framework on two prediction tasks (i.e., crime prediction and housing price prediction) in Chicago. Experiments show that JLPR consistently outperforms state-of-the-art partitioning methods in both tasks, which achieves above 25% and 70% performance improvements in terms of mean absolute error for crime prediction and house price prediction tasks, respectively. Additionally, we meticulously undertake three visualization case studies, which yield profound and illuminating findings from diverse perspectives, demonstrating the remarkable effectiveness and superiority of our approach.

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs.

It is commonly agreed that a recommender system should use not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., incidental information) to deal with the problem of data sparsity and cold start. The knowledge graph (KG), due to its expressive structural and semantic representation capabilities, has been increasingly used for capturing auxiliary information for recommender systems, such as the recent development of graph neural network (GNN) based models for KG-aware recommendation. Nevertheless, these models have the shortcoming of insufficient node interactions or improper node weights during information propagation, which limits the performance of recommender systems. To address this issue, we propose a Multi-dimension Interaction based attentional Knowledge Graph Neural Network (MI-KGNN) for enhanced KG-aware recommendation. MI-KGNN characterizes similarities between users and items through information propagation and aggregation in knowledge graphs. As such, it can optimize the updating direction of node representation by fully exploring multi-dimension interactions among nodes during information propagation. In addition, MI-KGNN introduces a dual attention mechanism, which allows users and items to jointly determine the weight of neighbor nodes. As a result, MI-KGNN can effectively capture and represent both structural (i.e., the topology of interactions) and semantic information (i.e., the weight of interactions) in the knowledge graph. Experimental results show that the proposed model significantly outperforms baseline methods for top-K recommendation. Specifically, the recall rate is increased by 5.78%, 6.66%, and 3.22% on three public datasets, compared with the best performance of existing methods.

Traditional path-finding studies basically focus on planning the path with the shortest travel distance or the least travel time over city road networks. In recent years, with the increasing needs of diverse routing services in smart cities, the bi-criteria optimum path-finding problem (i.e., minimizing path distance and optimizing extra cost or utility according to users’ preference) has drawn wide attention. For instance, in addition to distance, the previous studies further find routes with more scenery (utility) or less crime risk (cost). However, existing works are scenario-oriented which optimize specific cost or utility, ignoring that the routing planner should be universal to deal with both cost and utility in different real-life scenarios. To fill this gap, this paper proposes a generic bi-criteria optimum path-finding framework (cuRL) based on deep reinforcement learning (DRL). Specifically, we design a novel state representation and reward function for the DRL model ofcuRLto overcome the challenges that 1) the cost and utility should be optimized with minimal path distance in a unified manner; 2) the diverse distributions of cost and utility in various scenarios should be well-addressed. Then, a transition preprocessing method is proposed to enable the efficient training of DRL and avoid detours. Finally, simulations are performed to verify the effectiveness ofcuRL, where two criteria (i.e., solar radiation and crime risk) are modelled based on the real-world data in downtown New York. Comparing with a set of baseline algorithms, the evaluation results demonstrate the priority of the proposed framework for its generality.

Recently, dynamic graph representation learning has attracted more and more attention from both academic and industrial communities due to its capabilities of capturing different real-world phenomena. For a dynamic graph represented as a sequence of timestamped events, there are two kinds of evolutionary essences: temporal and repetitive information. At present, the temporal information of interactions (e.g., timestamps) have been deeply explored. However, as another vital nature of dynamic graphs, the repetitive information of interactions between two nodes is neglected, which may lead to inaccurate node representation. To address this issue, we propose a novel continuous-time dynamic graph representation learning model, which consists of a node-level-memory based module, a historical high-order neighborhood based vertical aggregation module and a repetitive-topological information based horizontal aggregation module. In particular, to characterize the evolving pattern of the repetitive information of interactions between a pair of nodes, we put forward a repetitive-interaction based attention mechanism to integrate the two key attributes (i.e., the content and the number of interactions) of repetitive interactions at different moments, based on the insight that the repetitive behaviors of nodes are widespread and essential. We conduct extensive experiments including future link prediction tasks (for transductive and inductive learning) and dynamic node classification task, and results on three real-life dynamic graph datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines, for both observed nodes and new ones.

In recent years, various intelligent activity recognition systems have been developed based on radio frequency signals such as radar, Wi-Fi, and radio frequency identification (RFID). When only one target is present, these systems can often provide high accuracy in recognizing different activities. However, such activity identification systems often fail to work due to signal interference when multiple targets coexist. To address this problem, we propose a multitarget gesture recognition system, named SoDar, based on a commercial single-input multi-output (SIMO) dual-channel Doppler radar. First, we employ endpoint detection, low-pass filtering, and discrete wavelet transform for data preprocessing. Then, we design a multitarget signal separation algorithm by maximizing the signal-to-noise ratio, and further refine the obtained signal based on principle component analysis. Afterward, we put forward a two-stage feature extraction method to extract both static and dynamic features from each separated signal. Finally, a classification model is trained to recognize the gestures of multiple targets. To verify the performance of SoDar, we selected nine different combinations of six gestures for two targets and collected more than 8000 data samples. Experimental results showed that the accuracy of two-target gesture recognition is above 90%.

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose anautomaticQTSanomalydetectionframework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to achieve a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3 percent accuracy, TCQSA outperforms two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. Additionally, the effectiveness of the attention mechanisms is quantitatively and qualitatively demonstrated.

Indoor localization serves as the basis of sensing and understanding human behaviors and further providing personalized services in many scenarios, such as retail stores, warehouses and libraries. However, existing indoor localization technologies cannot fulfill the requirement of such scenarios due to incapable of identifying different persons, severe object occlusion when there are multiple persons, or privacy concerns. On the basis of wide deployment of RFID tags in such scenarios, in this paper we develop a RFID-based localization system, i.e., ShopSense, which is not only able to accurately localize multiple people simultaneously but also differentiate them even when there are a lot of obstacles in the environment. Extensive experiments demonstrate that ShopSense can locate the shopping cart at a median tracking error of 20 cm and can locate the customer’s location with a median tracking error of 25 cm.

App popularity prediction is a significant task in mobile service development, which predicts an app's future popularity based on its current behaviors. It provides benefits from app development to targeted investment. Popularity is affected by two factors, i.e., internal ones like reviews and external ones like interaction among apps. However, most related studies only explore internal factors but neglect external ones. In fact, external factor plays an important role in popularity prediction modelling since it is the promoting and/or inhibiting influence resulted by app interaction. The app interaction has two major characteristics, i.e., interactivity and dynamicity, which brings challenges to app popularity prediction due to two reasons: 1) interactivity—it is hard to evaluate the existence and influence intensity of interactions; 2) dynamicity—the nature of interaction influence, e.g., promoting or inhibiting, and its intensity on popularity change with time. In this paper, we propose DeePOP, a popularity prediction model that innovatively leverages time-varying hierarchical interactions. First, we propose Hierarchical Interaction Graph, which is first studied in this work, to organically characterize the relationship and influence among apps. Second, DeePOP integrates internal factors and time-varying hierarchical interactions as inputs to build the prediction model. It develops multi-level modules based on Recurrent Neural Network with attention mechanism and generates multi-step time series predictions by fusing the outputs of modules. Experiments on a real-world dataset show that DeePOP outperforms state-of-the-art methods in prediction accuracy, effectively reducing the Root Mean Square Error (RMSE) to 0.088.

With increasing expansion and urbanization of cities, the gap is constantly widening between the current provision of urban public toilets and the fast-growing toileting demand. Building new ones becomes a promising way to alleviate such issue. Nevertheless, where to build them in a city is challenging. Different from other location selection (e.g., commercial sites) problems, the selection of public toilet locations is hard to quantify and evaluate. On one hand, the toileting demand that determines whether the new public toilet is needed cannot be measured accurately. On the other hand, the modeling of the toileting demand is also complicated, being influenced by multiple factors, e.g., human mobility, human activity, and geographical characteristics. In this article, we propose a novel data-driven framework named ToiletBuilder to address it, which consists of three components, i.e., region identification, region representation, and region classification. Specifically, region identification obtains many reachable regions with the reasonable size. Region representation extracts city-specific features from multiple urban data to characterize location selection influencing factors for each region. A deep embedding model is further applied to learn a high-order and concise semantic representation. By labeling some regions with the true positive label (i.e., having public toilets served in these regions) in advance, region classification trains a positive-unlabeled (PU) learning model from these samples to identify unlabeled positive ones. Finally, we conduct extensive experiments based on four real-world data sets including road network, river network, taxi trajectory, and POI data, in the city of Chongqing, China. Results demonstrate the effectiveness of our proposed approach.

Gesture recognition is key to enabling natural human-computer interactions. Existing approaches based on wireless sensing focus on accurate identification of arm gesture types. It remains a challenge to recognize and profile the details of arm gestures for precise interaction control. In addition, current approaches have strict positioning requirements between radars and users, making them difficult for real-world deployment. In this article, we adopt the multisensor approach and present gesture-radar-a dual Doppler radar-based gesture recognition and profiling system, which can capture subtle arm gestures with less positioning or environmental dependence. Gesture-radar uses two vertically placed Doppler radars to collect complementary sensing data of gestures, based on which cross-analysis can be performed for gesture recognition and profiling. Specifically, we first propose a two-stage classification model and enhance the signal proximity matching method by applying constraint functions to the DTW algorithm, aiming to improve the accuracy of gesture type recognition. Afterward, we establish and analyze unique features from the time-frequency spectrogram, which can be used to characterize in-depth gesture details, e.g., the angle or range of an arm movement. Experimental results show that gesture-radar achieves up to 93.5% average accuracy for gesture type recognition, and over 80% precision for profiling gesture details. This proves that the proposed approach is viable and can work in real-world environments.

Recently, the modeling and representation of multivariate time series has attracted much attention in the field of machine learning and data mining, due to its wide application potentials in biomedicine, finance, industry and so on. During the last decade, deep learning has achieved great success in many tasks. However, a large number of labeled data samples are needed to train a satisfactory model which has a huge amount of parameters, especially in cases that the inputs are multivariate time series (i.e., multi-dimension) and have complex relationships with the outputs. We propose a Multi-level attention-based prototype Network (MapNet) to model multivariate time series. Specifically, we first encode the time series based on deep learning and calculate the prototype for each class. Afterwards, we propose a multi-level attention mechanism to further optimize the prototype, including a short-term encoder as well as a long-term encoder. Experiments based on two public datasets demonstrate that MapNet outperforms state-of-the-art baseline models and is more applicable for few-shot dataset.

With the recent surge of smart wearable devices, it is possible to obtain the physiological and behavioral data of human beings in a more convenient and non-invasive manner. Based on such data, researchers have developed a variety of systems or applications to recognize and understand human behaviors, including both physical activities (e.g., gestures) and mental states (e.g., emotions). Specifically, it has been proved that different emotions can cause different changes in physiological parameters. However, other factors, such as activities, may also impact one’s physiological parameters. To accurately recognize emotions, we need not only explore the physiological data but also the behavioral data. To this end, we propose an adaptive emotion recognition system by exploring a sensor-enriched wearable smart watch. First, an activity identification method is developed to distinguish different activity scenes (e.g., sitting, walking, and running) by using the accelerometer sensor. Based on the identified activity scenes, an adaptive emotion recognition method is proposed by leveraging multi-mode sensory data (including blood volume pulse, electrodermal activity, and skin temperature). Specifically, we extract fine-grained features to characterize different emotions. Finally, the adaptive user emotion recognition model is constructed and verified by experiments. An accuracy of 74.3% for 30 participants demonstrates that the proposed system can recognize human emotions effectively.

Massive and redundant vehicle trajectory data are continuously sent to the data center via vehicle-mounted GPS devices, causing a number of sustainable issues, such as storage, communication, and computation. Online trajectory compression becomes a promising way to alleviate these issues. In this paper, we present an online trajectory compression framework running under the mobile environment. The framework consists of two phases, i.e., online trajectory mapping and trajectory compression. In the phase of online trajectory mapping, we develop a light-weighted yet efficient map matcher, namely, Spatial-Directional Matching (SD-Matching), to align the noisy and sparse GPS points upon the underlying road network, which fully explores the usage of vehicle heading direction collected from the GPS trajectory data. In the phase of online trajectory compression, we propose a novel compressor based on the heading change at intersections, namely, Heading Change Compression (HCC), aiming at finding a concise and compact trajectory representation. Finally, we conduct experiments to evaluate the effectiveness and efficiency of the proposed framework using real-world datasets in the city of Beijing, China. We further deploy the system in the real world in the city of Chongqing, China. The experimental results demonstrate that: 1) the SD-Matching algorithm achieves a higher mean accuracy but consumes less time than the state-of-the-art algorithm, namely, Spatial-Temporal Matching (ST-Matching) and 2) the HCC algorithm also outperforms baselines in trading-off compression ratio and computation time.

This paper puts forward a LSTM and CNN based assemble neural network framework to distinguish different types of arrhythmias by integrating stacked bidirectional long shot-term memory (SB-LSTM) network and two-dimensional convolutional neural network (TD-CNN). Particularly, SB-LSTM is used to mine the long-term dependencies contained in electrocardiogram (ECG) from two directions to model the overall variation trends of ECG, while TD-CNN aims at extracting local information of ECG to characterize the local features of ECG. Moreover, we design an ensemble empirical mode decomposition (EEMD) based signal decomposition layer and a support vector machine based intermediate result fusion layer, by which ECG can be analyzed more effectively, and the final classification results can be more accurate and robust. Experimental results on public INCART arrhythmia database show that our model surpasses three state-of-the-art methods, and obtains 99.1% of accuracy, 99.3% of sensitivity and 98.5% of specificity.

Electrocardiogram (ECG) signal based arrhythmias classification is an important task in healthcare field. Based on domain knowledge and observation results from large scale data, we find that accurately classifying different types of arrhythmias relies on three key characteristics of ECG: overall variation trends, local variation features and their relative location. However, these key factors are not yet well studied by existing methods. To tackle this problem, we design an attention-based hybrid LSTM-CNN model which is comprised of a stacked bidirectional LSTM (SB-LSTM) and a two-dimensional CNN (TD-CNN). Specifically, SB-LSTM and TD-CNN are utilized to extract the overall variation trends and local features of ECG, respectively. Furthermore, we add a trend attention gate (TAG) to SB-LSTM, meanwhile, add a feature attention mechanism (FAM) and a location attention mechanism (LAM) to TD-CNN. Thus, the effects of important trends and features at key locations in ECG can be enhanced, which is conducive to obtaining a better understanding of the fluctuation pattern of ECG. Experimental results on the MIT-BIH arrhythmias dataset indicate that our model outperforms three state-of-the-art methods, and achieve 99.3% of accuracy, 99.6% of sensitivity and 98.1% of specificity, respectively.

User behaviors in online social networks convey not only literal information but also one’s emotional attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this article, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model (MERM) to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor, and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%.

Hypertension is a common cardiovascular disease, which will lead to severe complications without timely treatment. Accurate hypertension identification is essential to preventing the condition deteriorated. However, the state of art hypertension identification methods only extract features from very few aspects, and hence have limited identification accuracy. Furthermore, they only can judge whether the subjects are hypertensive or not, more meaningful information (such as, why the subjects suffer from hypertension) that can help doctors to improve their diagnosis level are absent. In this paper, we propose a class association rules-based method to identify hypertension. Particularly, its key idea is to utilize the relationship existing in multi-dimensional features to characterize hypertension pattern more effectively, in order to improve the identification performance. In addition, it can also generate a set of class association rules (CARs), which can reflect the subjects' physiological status and are proved to be useful for doctors to analyze subject's condition deeply. Experiments based on 128 subjects (61 hypertension patients and 67 healthy subjects) shows that our method outperforms the baseline methods and the accuracy, precision and recall reach 85.2%, 85.0%, and 83.6%, respectively. Additionally, a user study based on five clinicians demonstrates the utility of the generated CARs.

The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.

Human behavior recognition is an effective way to realize natural human-computer interactions. Existing wireless sensing enabled gesture recognition technologies require a single person environment or an absolutely fixed position between the device and the user, which is not practical for daily use. In this paper, we present a non-contact radar-based gesture recognition system, named Gesture-Radar, which is able to capture arm gestures with low environmental dependence using a single Doppler radar. Our prototype design of Gesture-Radar is based on the dual channel Doppler information which contains specific Doppler shift and some other information reflected from the user while performing a certain gesture, and concretely we propose a two-stage classification method to identify arm gestures. Experimental result shows while in a single user environment, Gesture-Radar achieves up to 96.4% average classification accuracy for recognizing 5 different kinds of gestures and can work effectively while the distance between the user and the radar is within 3 meters. We also demonstrate that Gesture-Radar can be well adapted to multi-person environments.

This paper proposes CrowdTracker, a novel object tracking system based on mobile crowd sensing (MCS). Different from traditional video-based object tracking approaches, CrowdTracker recruits people to collaboratively take photographs of the object to achieve object movement prediction and tracking. The optimization objective of CrowdTracker is to effectively track the moving object in real time and minimize the cost on user incentives. Specifically, the incentive is determined by the number of workers assigned and the total distance that workers move to complete the task. In order to achieve the objective, we propose the movement prediction (MPRE) model for object movement prediction and two other algorithms for task allocation, namely, T-centric and P-centric. T-centric selects workers in a task-centric way, while P-centric allocates tasks in a peoplecentric manner. By analyzing a large number of historical vehicle trajectories, MPRE builds a model to predict the object's next position. In the predicted regions, CrowdTracker selects workers by utilizing T-centric or P-centric. We evaluate the algorithms over a large-scale real-world dataset. Experimental results indicate that CrowdTracker can effectively track the object with a low incentive cost.

Human computer operations such as writing documents and playing games have become popular in our daily lives. These activities (especially if identified in a non-intrusive manner) can be used to facilitate context-aware services. In this paper, we propose to recognize human computer operations through keystroke sensing with a smartphone. Specifically, we first utilize the microphone embedded in a smartphone to sense the input audio from a computer keyboard. We then identify keystrokes using fingerprint identification techniques. The determined keystrokes are then corrected with a word recognition procedure, which utilizes the relations of adjacent letters in a word. Finally, by fusing both semantic and acoustic features, a classification model is constructed to recognize four typical human computer operations: 1) chatting; 2) coding; 3) writing documents; and 4) playing games. We recruited 15 volunteers to complete these operations, and evaluated the proposed approach from multiple aspects in realistic environments. Experimental results validated the effectiveness of our approach.

Monitoring group mobility and structure is crucial for understanding group activities and social relations. In this paper, we develop algorithms for fine-grained mobility classification and structure recognition of social groups utilizing mobile devices. First, we present a method that recognizes four levels of group mobility, including stationary, strolling, walking, and running. Second, using multiple types of mobile sensors, a novel relative position relationship estimation algorithm is developed to understand different moving group structures. We have conducted real-life experiments in which 12 volunteers moved in different small groups either in an office building or a shopping mall with various speeds and structures. Experimental results show that our approach achieves an accuracy of 99.5 percent in group mobility level classification and about 80 percent in group structure recognition.

Respiration rate plays an important role in human health monitoring. Traditional respiration rate monitoring techniques usually require users to wear some special equipment, which is not convenient for the elderly and the baby. Recently, Wi-Fi based respiration detection technique has attracted much attention due to its device-free and low-deployment-cost. However, most existing studies focus on respiration detection in experimental environments, without considering the impact of people around (it often occurs in our daily life), therefore, if there are several people in the system, their detection will fail. To address this open issue, we propose TinySense, a novel approach that can detect multiple persons' respiration at a time. In particular, we use multiple TX-RX antenna pairs to capture the Wi-Fi Channel State Information (CSI), filter out the data whose time-of-arrival (TOA) is bigger than a truncation threshold and remove subcarriers that are greatly affected by the multi-path effect. As a result, we can obtain the respiration data of each person from the mixed received signal. Experiments demonstrate the effectiveness of our approach on two-user respiration detection.

The rapid development of social networks has woven themselves into people's daily life and become indispensable platforms with superior commercial and scientific values. Both companies and governments have discovered the potential effectiveness of employing social network users for information diffusion. Rather than only selecting a group of users who are interested in target topic, it is more beneficial to choose users with desired emotion preference to help diffuse information under certain emotional expectation. In this paper, we propose an emotional participant selection system that not only considers user's topic preference, but also more importantly takes user's emotional influence into account. Specifically, a dynamic forgetting mechanism is applied to learn user's topic preference, and independent cascade model is leveraged to construct emotional influence. Combining these two features, we develop an algorithm that can accomplish the task for emotional participant selection. Experimental results on a real-world data set validate the effectiveness of our proposed method.

Obstructive sleep apnea (OSA) is regarded as one of the most common sleep-related breathing disorders, which causes various diseases and affects people's daily life severely. Up to now, massive efforts have been devoted to identifying OSA events during sleep based on different signals (e.g., PSG, ECG, nasal airflow and EMG, etc.). However, there still are more or less shortcomings in current studies. In this paper, we propose a novel framework to improve the performance of identifying OSA events. Particularly, the key idea of our framework is to divide each potential event segment (i.e., a data segment that may or may not contain an OSA event) into different phases, from which we further extract fine-grained features to characterize respiratory pattern comprehensively. Concretely, we first automatically locate potential event segments from raw ballistocardiography (BCG) data by identifying arousals. Afterwards, each potential event segment is divided into three phases (i.e., Apnea Phase, Respiratory Effort Phase and Arousal Phase) by an adaptive threshold-based division algorithm. Based on these phases, we further extract and select efficient features that can characterize respiratory pattern from different aspects. Finally, these potential event segments are classified into OSA events or non-OSA events using BP neural network. Experimental results based on a real BCG dataset that contains 3,790 OSA events and 2,556 non-OSA events show that our framework outperforms the baselines and the precision, recall and AUC reach 94.6%, 93.1%, and 0.951, respectively.

Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages Wi-Fi signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding Wi-Fi signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of Wi-Fi. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform- based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.

Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.

It is an important yet challenging task for investors to determine the most suitable type of shop (e.g., restaurant, fashion) for a newly opened store. Traditional ways are predominantly field surveys and empirical estimation, which are not effective as they lack shop-related data. As social media and location-based services (LBS) are becoming more and more pervasive, user-generated data from these platforms are providing rich information not only about individual consumption experiences, but also about shop attributes. In this paper, we investigate the recommendation of shop types for a given location, by leveraging heterogeneous data that are mainly historical user preferences and location context from social media and LBS. Our goal is to select the most suitable shop type, seeking to maximize the number of customers served from a candidate set of types. We propose a novel bias learning matrix factorization method with feature fusion for shop popularity prediction. Features are defined and extracted from two perspectives: location, where features are closely related to location characteristics, and commercial, where features are about the relationships between shops in the neighborhood. Experimental results show that the proposed method outperforms state-of-the-art solutions.

The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called "Douban Events", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.

During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.

The newly emerging event-based social networks (EBSNs) connect online and offline social interactions, offering a great opportunity to understand behaviors in the cyber-physical space. While existing efforts have mainly focused on investigating user behaviors in traditional social network services (SNS), this paper aims to exploit individual behaviors in EBSNs, which remains an unsolved problem. In particular, our method predicts activity attendance by discovering a set of factors that connect the physical and cyber spaces and influence individual's attendance of activities in EBSNs. These factors, including content preference, context (spatial and temporal) and social influence, are extracted using different models and techniques. We further propose a novel Singular Value Decomposition with Multi-Factor Neighborhood (SVD-MFN) algorithm to predict activity attendance by integrating the discovered heterogeneous factors into a single framework, in which these factors are fused through a neighborhood set. Experiments based on real-world data from Douban Events demonstrate that the proposed SVD-MFN algorithm outperforms the state-of-the-art prediction methods.

With the recent surge of location-based social networks (LBSNs), such as Foursquare and Facebook Places, huge digital footprints of people's locations, profiles, and online social connections become accessible to service providers. Unlike social networks (e.g., Flickr, Facebook) that have explicit groups for users to subscribe to or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed. In the meantime, the diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user check-in traces at venues and user/venue attributes, we come out with a novel multimode multi-attribute edge-centric coclustering framework to discover the overlapping and hierarchical communities of LBSNs users. By employing both intermode and intramode features, the proposed framework is not only able to group like-minded users from different social perspectives but also discover communities with explicit profiles indicating the interests of community members. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset.

This paper explores the problem of user experience evaluation, in particular the quantitative evaluation of group user experience, in smart spaces. First, the classification and definition of four different categories of user groups are proposed, and the notion of group user experience is introduced. Second, we analyze the quantitative evaluation of group user experience for different types of user groups and establish an evaluation model for group user experience. Particularly, we employ two quantitative social metrics, user rating and user attention duration, as the main criteria for evaluating user experience. Other social factors, such as group interaction and the diversity of group members, are also taken into account to form a general quantitative evaluation model of group user experience for different user groups. Finally, we evaluate the effectiveness of the proposed model with preliminary experiments in a smart museum.