The dramatic progresses of wireless technologies and wearable devices have significantly promoted the development and popularity of smart home, while digital twin (DT) emerges as a game changer benefiting from its enhanced capabilities of visualization and interaction. The DT is able to build a realtime and continuous visual replica of a physical object or process, and to provide realtime monitoring, anomaly prediction, smart interaction, and lifecycle management. This paper presents a DT model to empower healthcare monitoring in the smart home with the goals of graphical monitoring, healthcare prediction, and intelligent control. High fidelity DT of the house and its equipments is created for visualized monitoring, and two suites of devices are deployed for continuously acquiring the users’ electrocardiograph (ECG) waves and the WiFi signals in the house. Two intelligent algorithms are then developed to perform fall detection from WiFi signals and to screen atrial fibrillation from ECG waves collected by wearable devices. Experimental results well validate the proposed model’s effectiveness for smart home monitoring, and the advantages of the developed smart algorithms for healthcare prediction over counterparts.

Digital Twin Empowered Wireless Healthcare Monitoring for Smart Home

Chaotic encryption mostly has been proposed to protect a single image or batch images of identical size. Typically, it splices these images into a huge one, and later performs batch image encryption. Yet, this solution may not be feasible given arbitrary-size images, which could not be directly connected one by one. To address it, we propose an arbitrary-size encryption scheme via chaos, for efficiently protecting batch/multiple images at one go. Our scheme supports to automatically read size of images, and then perform cross image permutation and diffusion. The cross image permutation is generic, which could output each permutation with equal expectation. We newly propose a bi-direction cross image diffusion, which has superior performance with only 1 round operation to spread the encryption influence to all images. At last, we conduct extensive experiments and security analyses. The experimental results show that our algorithm can mask the statistical information and resist differential attacks, with metrics such as information entropy, correlation coefficients, NPCR, and UACI closely approximating their theoretical values, namely 8, 0, 99.609%, and 33.464%, respectively. Our code is available at [https://github.com/TcSong/CrossImgEncryption]. 

Batch image encryption using cross image permutation and diffusion

A deep residual neural network model for synchronous motor fault diagnostics

Water leakage recognition plays a significant role in ensuring the safety of shield tunnel lining. However, current models cannot meet the engineering requirements because the tunnel environment is complex. In this concern, a one-stage deep learning model is developed for water leakage recognition. First, we design an attention module to reduce background noise interference. Second, an edge refinement algorithm is proposed to refine the mask of water leakage region. Furthermore, a mixed data augmentation is developed to enhance the robustness of model. Experimental results indicate an average precision (AP) is up to 60%, and a recognition speed is 26 frames per second (FPS). This determines that our proposed network is lightweight and has advantages over peer methods.

WLR-Net: An Improved YOLO-V7 With Edge Constraints and Attention Mechanism for Water Leakage Recognition in the Tunnel

Objective. To enhance the accuracy of heart sound classification, this study aims to overcome the limitations of common models which rely on handcrafted feature extraction. These traditional methods may distort or discard crucial pathological information within heart sounds due to their requirement of tedious parameter settings. Approach. We propose a learnable front-end based Efficient Channel Attention Network (ECA-Net) for heart sound classification. This novel approach optimizes the transformation of waveform-to-spectrogram, enabling adaptive feature extraction from heart sound signals without domain knowledge. The features are subsequently fed into an ECA-Net based convolutional recurrent neural network, which emphasizes informative features and suppresses irrelevant information. To address data imbalance, Focal loss is employed in our model. Main results. Using the well-known public PhysioNet challenge 2016 dataset, our method achieved a classification accuracy of 97.77%, outperforming the majority of previous studies and closely rivaling the best model with a difference of just 0.57%. Significance. The learnable front-end facilitates end-to-end training by replacing the conventional heart sound feature extraction module. This provides a novel and efficient approach for heart sound classification research and applications, enhancing the practical utility of end-to-end models in this field.

A learnable front-end based efficient channel attention network for heart sound classification

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

Self-adaptive reconstruction for compressed sensing based ECG acquisition in wireless body area network

The rapid advancements in wearable devices have enabled continuous and real-time monitoring and analysis of physiological parameters. Owing to the benefits of detecting cardiovascular diseases, arrhythmia recognition from the wearable Electrocardiogram (ECG) signals has drawn much attention. In this article, we present a novel method of using an ensemble of multiscale convolutional neural networks, the so-called EMCNet, for classifying the single-lead ECG waves acquired by wearable devices. The EMCNet includes two primary modules. Two component classifiers are first developed, which input the filtered ECG recordings and time-frequency spectrograms, and output the prediction probabilities of corresponding categories. These prediction probabilities are then sent to an improved weight matrix based on soft voting to produce the final classification result. Numerous experiments have been conducted, with results clearly demonstrating the efficacy and benefits of the EMCNet.

EMCNet: Ensemble Multiscale Convolutional Neural Network for Single-Lead ECG Classification in Wearable Devices

K-B2S+: A one-dimensional CNN model for AF detection with short single-lead ECG waves from wearable devices

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Papers

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

Self-adaptive reconstruction for compressed sensing based ECG acquisition in wireless body area network

EMCNet: Ensemble Multiscale Convolutional Neural Network for Single-Lead ECG Classification in Wearable Devices

K-B2S+: A one-dimensional CNN model for AF detection with short single-lead ECG waves from wearable devices