Spontaneous Echo Contrast (SEC) is a swirling smoke-like echo phenomenon in Transesophageal Echocardiography (TEE) videos caused by slow blood flow and hypercoagulable states. It is a significant indicator for assessing thromboembolic risk. However, current SEC identification requires extensive manual intervention, leading to low accuracy, high costs, and subjectivity. To address these issues, we propose TEENet, an effective clinical detection network for identifying SEC in TEE videos. Specifically, TEENet first generates attention maps for the input clips to highlight important regions and integrates Convolutional Neural Network with the Multi-Head Self-Attention to capture spatiotemporal representations. Furthermore, to enhance the classification performance across different SEC severity grades, we introduce an auxiliary classification module, which simultaneously utilizes the main classification head and auxiliary classification heads. Notably, we constructed a comprehensive dataset of 1106 TEE videos collected during clinical examinations performed at the First Affiliated Hospital of Soochow University from 2018 to 2023, providing a solid foundation for the development and validation of TEENet. Extensive experimental results demonstrate that our proposed network achieves the highest SEC identification accuracy of 92.4$\pm$1.3% compared to other spatiotemporal representation networks such as SlowFastR50 (89.6$\pm$0.7%) and TimeSformer (74.9$\pm$1.8%), which shows strong potential for effective auxiliary diagnosis in clinical practice.

Premature ventricular complexes (PVCs) are irregularities in heart rhythm where the ventricles contract earlier than expected, disrupting the normal cardiac cycle. Identifying the origin of PVCs before surgery is crucial as it can reduce operation duration, lower radiation exposure, and potentially enhance ablation success rates. Current detection methods face limitations in accuracy and data processing, often requiring large datasets and complex interpretations. This study presents PVCsNet, a deep-learning network specifically designed for classifying premature ventricular complexes (PVCs) in ECG images. It incorporates residual structures and attention mechanisms to enhance classification performance. PVCsNet consists of four 3 × 3 convolutional layers as feature extractors, followed by residual connections and attention blocks. This design enables the network to map image features to class probability distributions, enhancing performance even with limited data. Our experimental results demonstrate that using the SE Block with MaxPool and a ratio of 4, PVCsNet achieves an overall accuracy of 94.49%, with high precision in critical categories and a moderate parameter size. We successfully categorize the data into six distinct classes based on their origin locations in the heart: right ventricular outflow tract (RVOT), left ventricular outflow tract (LVOT), papillary muscle (PM), valvular annulus (VA), summit, and His-Purkinje system (HPS). Among these, RVOT is the most common and crucial origin of PVCs. PM and HPS are also significant origins due to their clinical implications. This study demonstrates the potential of PVCsNet in clinical diagnostics, providing promising results in classifying ECG images and contributing to future medical research and diagnosis.

In order to better assist the rehabilitation treatment of patients with musculoskeletal injury, standard rehabilitation actions are needed to guide the musculoskeletal rehabilitation process. With more and more urgent demands, the musculoskeletal rehabilitation evaluation systems have attracted a high degree of attention. Experts have proposed a series of systems based on laser, ultrasound, and image, which can give reasonable recognition and judgment. However, these systems either require specialized and expensive equipment or can be affected by ionizing radiation. How to construct a musculoskeletal rehabilitation evaluation system with low cost, good effect, and little injury is still a great challenge. In this article, we propose MSEva, a musculoskeletal rehabilitation evaluation system based on EMG signals. Specifically, the system uses EMG sensors to collect a large amount of data for five rehabilitation actions. Secondly, MSEva uses Wavelet Transform (WT) to extract the signal features and then puts the processed data into the Long Short-Term Memory (LSTM) network for model training. Finally, the system uses the LSTM model to evaluate the normality of the EMG response of rehabilitation actions. The results show that the average accuracy of MSEva reaches 94.37%, which has important evaluation value in guiding the rehabilitation of musculoskeletal patients.

Recently, federated learning (FL) becomes a emerging research area, and the combination of edge computing and FL is one of the important research contents. However, there are many kinds of edge devices in heterogeneous federated learning, such as personal computers, embedded devices, and the resource-limited devices will reduce the efficiency of FL. In this paper, we propose an efficient linear coded federated learning under multiple stragglers (LCFLMS) to (1) accelerate the training speed and improve the efficiency of heterogeneous FL under multiple stragglers and (2) provide the certain level of privacy protection. We design a client-based multiple stragglers task outsourcing (C-MSTO) algorithm and a server-based multiple stragglers task outsourcing (S-MSTO) algorithm to meet the model calculation acceleration in general environment under multiple stragglers. In the process of outsourcing, the raw data are protected by using linear coding computing (LCC) scheme. Finally, the experimental results demonstrate that LCFLMS reduces the training time by 90.22% when the performance difference between clients in FL system is large.

Popularity prediction is to predict the number of social network users involved in information diffusion. Recently, deep learning methods for popularity prediction advance traditional approaches that rely on hand-crafted features. However, existing approaches ignore the multi-source cascade that consists of multiple sub-cascades with different content but under the same topic. Different from single-source cascade, more cascading information can be observed from multi-source cascade and they are potentially correlated. How to correlate the diverse information and take advantage of them from both temporal and spatial aspects is critical for prediction. To this end, we propose a novel framework, called HEterogeneous Recurrent Integrated Graph Convolutional Neural Network (HERI-GCN). Specifically, we construct a heterogeneous cascade graph to model the multi-source cascade where time intervals are treated as heterogeneous time nodes. Besides, we propose a heterogeneous GCN to learn rich features from the multi-source cascade. RNN is organically integrated into the heterogeneous GCN to overcome the limited learning ability toward temporal and spatial data. We evaluate HERI-GCN through comparative experiments on three datasets. The experimental evaluation shows that HERI-GCN outperforms the state-of-the-art baseline methods.

To improve safety in public transportation, a major issue is how to avoid traffic accidents. To this end, a recent report has demonstrated that more than 90% of accidents in the United States were due to drivers’ abnormal behaviors. Relevant to this observation, many recent studies have proposed to use different sensors to monitor drivers’ behaviors and apply learning algorithms to detect abnormal behaviors. Nevertheless, most existing systems are expensive and inconvenient to be deployed or significantly affected by the environment. In this article, we propose and develop a novel and effective solution, namely, SafeDriving, that collects signals from electromyography (EMG) sensors and then utilizes an effective deep-learning model to detect abnormal behaviors in real time. Specifically, we first utilize a wearable EMG sensor that can be attached to a driver’s forearm to collect a large amount of sensing data from human drivers, for which we define five typical abnormal driving behaviors (i.e., fetching forward, picking up, turning the steering wheel sharply, turning back, and touching sunroof) and label each sample accordingly. Next, using the labeled data, we design and train multiple state-of-the-art classifiers to improve the performance of SafeDriving, e.g., convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The extensive experiments demonstrate that GRU can lead to the best performance with an average accuracy of 93.94%. Based on this observation, we further investigate other important factors, such as the binding area of the sensor, the tightness of binding, the duration of the sample, etc. The proposed SafeDriving system provides an effective approach to reliably assess drivers’ driving behaviors with affordable commodity sensors and be further used in public safety.

Every year, over 4700 traffic fatalities and 75000 crash injuries involve pedestrians in the United States. Effective solutions are urgently needed to prevent vehicle–pedestrian collision accidents. Many driving assistance systems are proposed to address this problem; however, they require additional infrastructures that may result in higher costs and be difficult to deploy on a large scale. In this article, we propose SafePath, which uses the ubiquitous smartphones to avoid vehicle–pedestrian collision. Specifically, SafePath utilizes the smartphones to broadcast the redesigned service set identifier (SSID) messages containing users’ information (e.g., location, direction, etc.) and scan the surroundings via wireless communications. Considering the limited communication range and the possible interference, and obstruction of obstacles, we propose a collaborative mechanism to enhance the transmission capability, hence predicting the collisions in advance effectively. We also design a risk evaluation scheme to calculate the probability of accidents and inform users to take actions against accidents at different levels. We implement SafePath on the Android platform and conduct extensive real-road experiments to evaluate the system performance. The experimental results demonstrate that SafePath can provide twice the transmission range compared with other collision-avoiding systems. Moreover, it also can significantly reduce the probability of vehicle–pedestrian collisions by up to 81.4%, with respect to other compared collision-avoiding systems in our real-road test.

Recently, edge computing (EC) has attracted wide attention as a novel and promising computing mode with high real-time and low-latency characteristics. However, users' privacy and the limited resources have become major concerns in the implementation of EC because edge devices are usually heterogeneous and untrustworthy. Although many related works have protected the user's privacy, they did not take the storage resource limitation of heterogeneous edge devices into consideration and their schemes may cause high communication load. In this paper, we propose PCHEC, a Private Coded computation scheme for Heterogeneous Edge Computing, to protect the user's privacy and minimize the communication load. Specifically, PCHEC first gives a storage allocation scheme to minimize the communication load in EC where the heterogeneous edge devices have different storage limits. Secondly, PCHEC utilizes linear coding to mix the target data with other information for the protection of the user's privacy. To evaluate the efficiency of PCHEC, we make theoretically analysis and conduct extensive simulations. The experiments show PCHEC effectively reduces the communication load by up to 70% compared with other schemes.

Nowadays, the combination of edge computing and artificial intelligence has become a mainstream trend. Based on edge computing and image classification technologies, we design and implement a secure distributed image classification reasoning system for heterogeneous edge computing. The functions of the system consists of two parts: model distributed deployment and image classification reasoning. Firstly, we have designed three distributed deployment schemes for the model deployment on edge devices: random, static and dynamic deployment schemes. Secondly, we have designed three secure distributed image classification reasoning schemes: uncoded, 2-replication and MDS coding reasoning schemes. These reasoning schemes can protect the security of image data in the process of image reasoning and meet the weak security standard. Our system uses edge devices as computing devices, so it has the advantages of low computing cost and saving bandwidth. The experimental results show that our system can protect the security of image data, also has favorable stability and efficiency under the environment of heterogeneous edge computing.

Edge computing is a promising technology that can fulfill the requirements of latency-critical and computation-intensive applications. To further enhance the performance, coded edge computing has emerged because it can optimally utilize edge devices to speed up the computation. In this paper, we tackle a major security issue in coded edge computing: how to verify the correctness of results and identify attackers. Specifically, we propose an efficient verification scheme, namely Decode-and-Compare (DC), by leveraging both the coding redundancy of edge devices and the properties of linear coding itself. To design the DC scheme, we conduct a solid theoretical analysis to show the required coding redundancy, the expected number of decoding operations, and the tradeoff between them. To evaluate the performance of DC, we conduct extensive simulation experiments and the results confirm that the DC scheme can outperform existing solutions, such as homomorphic encryption and computing locally at the user device.

Running is one of the most important workouts to keep our body fit. This paper presents RunnerPal - a runner monitoring and advisory system by harmonizing the rhythms of breathing, heart beating and striding based on smart devices. RunnerPal is a convenient, biofeedback-based, automated music recommendation system, which utilizes Bluetooth headset, Apple Watch and smartphone to obtain body sensed data. To improve the accuracy of the detection, we propose a novel approach to calibrate the result by integrating ambient sensed data with a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the striding and breathing frequencies. RunnerPal uses the sensed data and runner's contextual information to provide dynamic music suggestions to help the user achieve a target heart rate. We perform an empirical study to show the effect of music on heart rate and devise a Proportional Integral Differentiation Controller (PID - Controller) that recommends appropriate music to the user. RunnerPal has been validated by extensive experiments, and experimental results demonstrate that it can help runners achieve a target heart rate and maintain a stable running rhythm for indoor/outdoor running 91.6 percent of the time. In addition, RunnerPal can provide some advice to improve exercise effectiveness for runners.

With the pace of modern life quickening and increasing work stress, people don't have enough time to focus on their health and communicate with family members. The loneliness and chronic diseases (e.g., obesity, depression, diabetes, and dementia) have become more prevalent. In the paper, we propose SmartBuddy, a novel integrated mobile sensing and detecting system for monitoring people's family activities, which can motivate the user to do proper physical exercise and establish good relationships with family members for maintaining their physical and mental health. Specifically, SmartBuddy firstly uses smartphones and Apple Watches built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, etc. Secondly, SmartBuddy can accurately detect family activities including occurrence/duration of meal, cooking, TV viewing, conversations, in an unobtrusive manner based on sensed data. Thirdly, SmartBuddy will propose a personal plan to suggest the user doing some exercise and making continuous progress in the process of communicating with family members. We have fully implemented SmartBuddy on the Android platform and perform testbed experiments. The experimental results demonstrate that SmartBuddy is easy to use, accurate, and appropriate for family activities with the accuracy of 80% and the user satisfaction degree of 84.5%.

Nowadays, with increasing work stress and quick pace of modern life, people generally do not have enough time for exercising, however, they curiously pay much attention to the direct effect of exercising -calorie consumption. In this paper, we investigate several popular calorie consumption monitoring approaches and propose CCMS - a novel Calorie Consumption Monitoring System for exercising with least-squares calibration based on smartphones. Specifically, CCMS uses smartphone built-in sensors to collect the sensed data from accelerometer, barometer and GPS. With the sensed data, CCMS computes the calorie consumption based on the energy consumption formulas of American College of Sports Medicine. We apply an improved Naive Bayesian Classifier, which enables intelligent classification of several exercise types and achieves an average accuracy of 92.6% for determining the exercise types. We adopt the least-squares method to calibrate the result of calorie consumption and find that the method can increase the precision of CCMS up to 6%. We evaluate the performance of CCMS against other popular fitness applications, including Gudong and Jawbone UP3 which is a commercial device. The experimental results demonstrate that CCMS outperforms state-of-the-art calorie consumption monitoring systems in terms of measuring accuracy, with the average accuracy increase of about 5%.

Taking part in family activities plays an important role in establishing good relationships with family members. It can solve the loneliness of elders, which related not only to their physical health, but also to the well-being of the whole family. In the paper, we propose FamilyPal, an effective system for detecting family activities, which can help users establish good relationship with family members. Specifically, FamilyPal firstly uses smartphones built-in sensors, such as GPS, accelerometer, microphone, gyroscope, and Wi-Fi to obtain the motion and location of users, the surrounding voice, etc. Secondly, with the sensed data, we propose an effective method based on Gaussian Mixtures Models (GMM) to detect family activities, including occurrence of meal, cooking, TV viewing, conversations, in an unobtrusive manner. Thirdly, we select appropriate sensors for classification to improve smartphones battery life. FamilyPal has been implemented on the Android platform and evaluation of the system with 10 subjects over one week shows that FamilyPal can accurately classify family activities with the average precision of 71%, the average recall of 73% and the F-measure of 71.99%.

Indoor localization based on smartphone can enhance user's experiences in indoor environments. Although some innovative solutions have been proposed in the past two decades, how to accurately and efficiently localize users in indoor environments is still a challenging problem. Traditional indoor positioning systems based on Wi-Fi fingerprints or dead reckoning suffer from the variation of Wi-Fi signals and the drift of dead reckoning problems, respectively. Crowdsourcing and ambient sensing stimulate new ways to improve existing localization systems' accuracy. Using human social factors to calibrate the accuracy of localization is practical and awarding. In this paper, we propose WAIPO, a collaborative indoor localization system with the fusion of Wi-Fi and magnetic fingerprints, image-matching, and people co-occurrence. Specifically, we could obtain the most likely top-n locations based on Wi-Fi fingerprints. We utilize the statistics of users' historical locations known by image-matching, for which we propose a photo-room matching algorithm, to reduce estimating areas. In order to further improve the accuracy of localization, we propose a co-occurrence and non-co-occurrence detection algorithm to detect users' spatial-temporal co-occurrence and determine users' locations with magnetic calibration. We have fully implemented WAIPO on the Android platform and perform testbed experiments. The experimental results demonstrate that WAIPO achieves an accuracy of 87.3% on average, which outperforms the state-of-the-art indoor localization systems.

Fixed polarity Reed-Muller expressions (FPRMs) are well-suited for many practical applications due to they have many excellent properties. In order to obtain an optimal FPRM with fewest product terms, we propose an efficient minimization algorithm (EMA-FPRMs) for FPRMs. The main idea behind the EMA-FPRMs is that, firstly, the incompletely specified Boolean function is transformed into the zero polarity incompletely specified fixed polarity RM expression (ISFPRM) by using the proposed ISFPRM acquisition algorithm; secondly, the polarity and allocation of don't care terms of ISFPRM is encoded as chromosome; lastly, the optimal FPRM with fewest product terms is obtained by using genetic algorithm (GA), in which the FPRM that corresponds to the given chromosome is obtained by using the proposed chromosome conversion algorithm. The experimental results on MCNC benchmark circuits show that compared with the traditional polarity optimization approach which neglects the don't care terms, the EMA-FPRMs is highly effective in minimizing the number of product terms of FPRMs. Moreover, the EMA-FPRMs is faster than the GA based minimization algorithm which also considers the don't care terms.

Breathing plays an important role in the process of running. A stable and harmonic breathing rhythm can postpone runners' fatigue and help to improve their running performances. This paper presents a method that can detect runner's breathing frequency continuously. We utilize Bluetooth headset and smart phone to obtain sensed data, such as striding frequency and breathing frequency. Due to the interference of ambient noise, the detection will be inaccurate. In order to cope with this problem, we calibrate the detection result by leveraging a physiological model, called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. Our method has been validated by extensive experiments and the experimental results indicate that it can accurately detect the breathing frequency for runners.

Digital Living Network Alliance (DLNA) puts forward an interoperable architecture of home network equipment to implement the simple and seamless interoperability between household appliances, mobile devices and computers, so as to enhance and enrich users' experience. Because DLNA is designed to implement the sharing of multimedia resources between devices in a family environment, it does not support cross-LAN accessing. Currently, the cross-network cooperative work between smart devices has become a hot issue. To overcome this problem, this paper presents CLMRS - a cross-LAN accessing solution of C/S architecture based on DLNA technology. CLMRS can be used to implement cross-network media resources sharing by designing DLNA gateway/router of applicaion-level. We use DLNA gateway as a agent of LAN which is designed to transpond communication messages and to redirect address. To achieve cross-LAN media resources sharing, CLMRS adapts DLNA router as a transfer server to transpond communication data and to manage users access right. We also optimize the transmission path of media Steam to reduce the pressure of DLNA router. In order to protect user privacy, CLMRS sets a family account to limit the access right and proposes an efficient and secure authentication mechanism with anonymity. Our design and theoretical model are validated via implementing an instance of DLNA cross-network communication. Experimental results show that the approach is practical and can protect user privacy effectively.

This paper presents VINCE - a novel visible light sensing design for smartphone-based Near Field Communication (NFC) systems. VINCE encodes information as different brightness levels of smartphone screens, while receivers capture the light signal via light sensors. In contrast to RF technologies, the direction and distance of such a Visible Light Communication (VLC) link can be easily controlled, preserving communication privacy and security. As a result, VINCE can be used in a wide range of NFC applications such as contactless payments and device pairing. We experimentally profile the impact of screen brightness levels and refresh rates of smartphones, and then use the results to guide the design of light intensity encoding scheme of VINCE. We adopt several signal processing techniques and empirically derive a model to deal with the significant variation of received light intensity caused by noises and low screen refresh rates. To improve the communication reliability, VINCE adopts a feedback-based retransmission scheme, and dynamically adjusts the number of encoding brightness levels based on the current light channel condition. We also derive an analytical model that characterizes the relation among the distance, SNR (Signal to Noise Ratio), and BER (Bit Error Rate) of VINCE. Our design and theoretical model are validated via extensive evaluations using a hardware implementation of VINCE on Android smartphones and the Arduino platform.