Proper sitting posture is essential for health, as maintaining appropriate torso inclination improves comfort and reduces the risk of cervical and spinal disorders. Despite extensive research on posture detection, fine-grained variations in torso inclination have received limited attention. We propose Torso-Track, a non-intrusive, device-free system for monitoring torso inclination. TorsoTrack leverages wireless respiratory signals to capture posture changes, applies signal restoration to correct abnormal segments, and extracts key features for precise angle estimation. A multi-layer feature extraction network models the relationship between wireless signals and torso inclination, emphasizing critical posture features. To enhance adaptability to new users, TorsoTrack integrates a Reptile-PG meta-learning strategy, improving cross-individual prediction accuracy. Evaluations on 25 participants show that TorsoTrack reliably identifies significant features corresponding to different torso angles, achieving an averageR2of 0.9822, RMSE of 1.6733, and MAE of 0.9977. For previously unseen individuals, the system maintains an averageR2of 0.9033, demonstrating its effectiveness in precise torso inclination assessment.

Motion artifacts (MA) significantly compromise the quality of photoplethysmographic (PPG) signals in wearable devices, hindering the accurate extraction of vital sign parameters. To address the limitations of existing methods in cross-user MA detection and the low accuracy of signal reconstruction, this paper proposes FlexiMA, a two-stage co-optimization framework that operates without reference signals. This framework is designed to tackle two key tasks: MA detection and MA removal. In the detection phase, a task-adaptive diffusion model, IMD-XResNet1D, is developed by integrating meta-learning and diffusion mechanisms, enabling efficient modeling of artifact-related features in new users. In the removal phase, an adversarial variational autoencoder (AVAE) is employed to achieve high-fidelity reconstruction of PPG signals. Experiments conducted on multi-user, multi-scenario datasets demonstrate that FlexiMA achieves 99.68% accuracy in MA detection and maintains a cross-user generalization performance of 99.32%. In the MA removal task, it attains 87.85% waveform structure similarity with an interbeat interval (IBI) error of 4.60 ms, indicating reliable signal reconstruction. These results verify the robustness and adaptability of FlexiMA in dynamic and complex environments, highlighting its strong potential for deployment in wearable applications.

Leather is among the most widely traded textile materials worldwide, and detecting surface defects is crucial to ensuring its quality. Conventional manual inspection of leather surfaces is labor-intensive, time-consuming, and prone to human error. Advances in computer vision have facilitated the development of automated leather surface defect detection. However, vision-based methods remain sensitive to lighting conditions, particularly on high-gloss leather surfaces. In this paper, we introduce Le-Radio, an industrial IoT (IIoT) oriented RF-based system for automated leather defect detection. Le-Radio provides a gloss-robust, ubiquitous, and continuous inspection solution, which is crucial for integrating quality control into smart factory pipelines and advancing the digital transformation of the traditional leather industry. To extract informative representations of leather defects from wireless signals, we employ continuous radio snapshots and refined signal features to differentiate defect patterns. Specifically, we design an imaging algorithm that continuously visualizes defects on moving leather samples using a fixed radar. To enhance Le-Radio’s robustness in diverse environments, we train a transferable model that maintains consistent detection performance across different scenarios. We conduct extensive experiments in three rooms using 120 leather samples to evaluate Le-Radio. Experimental results demonstrate that Le-Radio accurately detects diverse defects in high-gloss leather. Beyond leather inspection, this work establishes Le-Radio as a practical IoT edge sensing node for material surface inspection in the IIoT. By integrating RF-based defect scanning capabilities and a robust radar imaging framework into a unified system, this work establishes RF sensing as a viable and novel paradigm for material surface inspection in the IIoT, particularly in scenarios where optical methods are fundamentally limited.

In recent years, more and more people choose to work out at home or in the office to improve their physique and build muscle. However, the lack of professional guidance makes it difficult for many fitness practitioners to achieve optimal results. Consequently, research on non-contact fitness monitoring using wireless signals has gained attention. Existing studies primarily focus on isolated exercises, while compound exercises, which involve multiple isolated exercises, remain underexplored. This combination introduces new challenges for fitness recognition and monitoring. To this end, we propose M-Fitness, a millimetre-wave radar-based fitness assistant system capable of recognizing and monitoring both isolated and compound exercises. First, we capture fine-grained motion features and design image enhancement algorithms to generate intuitive motion images. Next, we design a novel motion segmentation method for fitness actions. We further formulate compound exercise recognition as a sequential task and develop customized deep learning models that allow users to incorporate new compound exercises. Finally, we perform a comprehensive fitness assessment based on the FITT principle. Experiments on a dataset of over 5,000 movement samples from 18 volunteers demonstrate that M-Fitness achieves 96.5% accuracy for isolated exercise recognition and 92.7% accuracy for compound exercise recognition, exhibiting strong adaptability to diverse environments.

Sitting posture is closely related to our health. Poor sitting posture can cause various diseases and jeopardize our health. Among the current methods for detecting sitting posture, computer vision solutions suffer from privacy leakage and wearable sensor solutions suffer from inconvenience and cost of wearing. In this study, we introduce 3D-Sitpose, which leverages millimeter-wave radar to detect human sitting posture. 3D-Sitpose utilizes wireless signal transmission for non-contact detection, ensuring privacy protection and cost reduction. Firstly, we analyze the impact of variations in human sitting posture on millimeter-wave radar signals, and design sophisticated signal processing methods to refine the collected radar data, yielding clearer point cloud information for volunteers in various sitting postures. Secondly, we develop a two-channel neural network to extract fine-grained features related to volunteers from the point cloud data. Finally, we obtain coordinates for 25 human skeletal points. 3D-Sitpose can instruct users to maintain correct sitting posture based on a set of six key angles. We recruit 20 volunteers from our institute to conduct comprehensive evaluations of 3D-Sitpose. Experiments are conducted in two indoor environments to estimate sitting posture. The results reveal the mean Euclidean distance error for all skeletal point locations is 6.65 cm. This demonstrates that our method is able to estimate various sitting changes in volunteers.

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments.

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users’ active participation, making it impractical to monitor blood alcohol content (BAC) during driving. This article introduces BACFuse, a device-free, contactless, and noninvasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multimodal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse’s performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with an MAE of 2.13 mg/dl, showing promise for future in-car driving management paradigms.

Recent studies have revealed the vulnerability of Deep Neural Network (DNN) models to backdoor attacks. However, existing backdoor attacks arbitrarily set the trigger mask or use a randomly selected trigger, which restricts the effectiveness and robustness of the generated backdoor triggers. In this paper, we propose a novel attention-based mask generation methodology that searches for the optimal trigger shape and location. We also introduce a Quality-of-Experience (QoE) term into the loss function and carefully adjust the transparency value of the trigger in order to make the backdoored samples to be more natural. To further improve the prediction accuracy of the victim model, we propose an alternating retraining algorithm in the backdoor injection process. The victim model is retrained with mixed poisoned datasets in even iterations and with only benign samples in odd iterations. Besides, we launch the backdoor attack under a co-optimized attack framework that alternately optimizes the backdoor trigger and backdoored model to further improve the attack performance. Apart from DNN models, we also extend our proposed attack method against vision transformers. We evaluate our proposed method with extensive experiments on VGG-Flower, CIFAR-10, GTSRB, CIFAR-100, and ImageNette datasets. It is shown that we can increase the attack success rate by as much as 82% over baselines when the poison ratio is low and achieve a high QoE of the backdoored samples. Our proposed backdoor attack framework also showcases robustness against state-of-the-art backdoor defenses.

Due to the widespread use of mobile devices, it is essential to authenticate users on mobile devices to prevent sensitive information leakage. Biometrics-based authentication is prevalent on smart devices to verify the legitimacy of users, but is vulnerable to replay attacks. In this paper, we propose to leverage the distinctive finger tap gesture during unlocking smartphone to establish a secure multi-factor authentication system, named FingerVib. Compared with other biometric-based authentication systems, FingerVib does not require users to remember any complicated information (e.g., hand gestures, doodles) and the working type is unobtrusive. When users unlock their phones by tapping, FingerVib utilizes the microphone to record the sound produced by fingers tapping on the phone and adopts IMU (Inertial Measurement Unit) to extract the vibration of users’ smartphones. One key contribution is that we model the inherent correlation between sounds and vibration signals. Specifically, FingerVib captures two novel reactions to describe how the individual’s contact palm modulates signals in two different domains. Based on these two responses, we develop a real-time noise-resistant unlocking activity detection algorithm, which allows accurate unlocking signal segmentation even if the two modalities are interfered. Further, we develop a modal fusion model where the model extracts cross-modal features and acquires inter-modal correlation features to ensure consistent performance of inference even when modalities are disturbed. In a user study with 41 participants, FingerVib achieves an authentication accuracy of 98.53% and an average performance of 1.36% FAR, 2.76% FRR and 2.72% EER against replay attacks and impersonation attacks. FingerVib’s fusion approach improves identification performance by roughly 9.7% and 11.6% over Wavocie and AUDIOIMU, respectively, within existing multi-modal fusion systems. Extensive experimental results demonstrate the effectiveness and robustness of FingerVib under various conditions.

Blood pressure (BP) is a critical physiological parameter closely associated with severe diseases such as heart failure and kidney damage. Current methods either require additional or dedicated hardware, or closing touching to the devices, causing discomfort and inconvenience. Therefore, a convenient, contactless BP measurement solution is highly desired. In this work, we present Ubicon-BP, a ubiquitous, device-free, and contactless BP detection application. Ubicon-BP calculates BP based on the pulse transit time (PTT), a key feature that is medically proven correlated with BP. However, using smartphone sensors to contactless calculate PTT is non-trivial since it requires a micro-second level precision for cardiac event detection. To address this issue, we propose leveraging the acoustic sensors in smartphone to detect vibrations caused by heart valve movements, as well as camera sensors to measure finger pulses. To accurately measure heartbeat signal that are susceptible to motion, we first improve the sensing granularity of acoustic signals and then introduce the IQ-MVED model to eliminate motion interference. Furthermore, when recovering pulse signals from video signals, issues such as poor generalization performance arise. Consequently, we propose the TS-CAN and meta-learning models to obtain personalized pulse signals. Finally, we transform the extracted time-frequency features from the recovered heartbeats and pulse signals to the corresponding BP. Comprehensive testing involving 50 subjects reveal a standard deviation error of$ 4.27 \; \text{mmHg}$for diastolic pressure and$ 6.36 \; \text{mmHg}$for systolic pressure, respectively.

Early detection of myocardial infarction (MI) is essential for alleviating symptoms and improving daily activity performance. Researchers typically employ continuous segments of heartbeat signals (20–30 seconds), such as ECG signals, for MI detection, as MI often induces changes in heartbeat patterns. Current MI detection methods, like wearable sensors, may induce discomfort from prolonged wear, and Radio Frequency (RF) based approaches might fail to extract fine-grained heartbeat signals during vigorous movement. This article presents a reliable and motion-robust MI detection method based on RF signals. By developing a series of advanced signal processing algorithms, MI-Ra can capture fine-grained heartbeat signals during various daily activities. Our design is inspired by the fact that RF reflections caused by heartbeat signals are mixed with other motion-induced reflections in a nonlinear manner. We utilize the Taylor series expansion method to extract the linear component of these mixed non-linear signals and propose a novel Generative Adversarial Networks (GAN) method, named IQ-TransGAN, to separate the heartbeat signal. To enhance MI detection reliability, MI-Ra employs a multi-periodicity modeling method to extract refined signal representations from recovered heartbeat signals. We have recruited 50 volunteers with MI from Zhongnan Hospital of Wuhan, China, and 50 volunteers without MI, for comprehensive evaluations. The results demonstrate that MI-Ra achieves an average MI detection accuracy of 95.2% when user is quasi-stationary. Even during user non-stationary conditions, MI-Ra maintains an average detection accuracy of 90.5%. MI-Ra shows promise in paving way for smart home healthcare.

Abstract Electroencephalogram (EEG), as a tool capable of objectively recording brain electrical signals during emotional expression, has been extensively utilized. Current technology heavily relies on datasets, with its performance being limited by the size of the dataset and the accuracy of its annotations. At the same time, unsupervised learning and contrastive learning methods largely depend on the feature distribution within datasets, thus requiring training tailored to specific datasets for optimal results. However, the collection of EEG signals is influenced by factors such as equipment, settings, individuals, and experimental procedures, resulting in significant variability. Consequently, the effectiveness of models is heavily dependent on dataset collection efforts conducted under stringent objective conditions. To address these challenges, we introduce a novel approach: employing a self‐supervised pre‐training model, to process data across different datasets. This model is capable of operating effectively across multiple datasets. The model conducts self‐supervised pre‐training without the need for direct access to specific emotion category labels, enabling it to pre‐train and extract universally useful features without predefined downstream tasks. To tackle the issue of semantic expression confusion, we employed a masked prediction model that guides the model to generate richer semantic information through learning bidirectional feature combinations in sequence. Addressing challenges such as significant differences in data distribution, we introduced adaptive clustering techniques that manage by generating pseudo‐labels across multiple categories. The model is capable of enhancing the expression of hidden features in intermediate layers during the self‐supervised training process, enabling it to learn common hidden features across different datasets. This study, by constructing a hybrid dataset and conducting extensive experiments, demonstrated two key findings: (1) our model performs best on multiple evaluation metrics; (2) the model can effectively integrate critical features from different datasets, significantly enhancing the accuracy of emotion recognition.

The interaction mode of smart eyewear has garnered significant research attention. Most smart eyewear relies on touchpads for user interaction. This article identifies a drawback arising from the use of touchpads, which can be obtrusive and unfriendly to users. In this article, we propose TeethFa, a novel fabric sensor-based system for recognizing teeth gestures. TeethFa serves as a hands-free interaction method for smart eyewear. TeethFa utilizes fabric sensors embedded in the glasses frame to capture pressure changes induced by facial muscle movements linked to teeth movements. This enables the identification of subtle teeth gestures. To detect teeth gestures, TeethFa designs a novel template-based signal segmentation method to determine the boundary of teeth gestures from fabric sensors, even in the presence of motion interference. To improve TeethFa’s generalization, we employ a meta-learning technique based on generalization adjustment to extend the model to new users. We conduct extensive experiments to assess TeethFa’s performance on 30 volunteers. The results demonstrate that our system accurately identifies five different teeth gestures with an average accuracy of 93.57%, and even for new users, the accuracy can reach 89.58%. TeethFa shows promise in offering a new interaction paradigm for smart eyewear in the future.

Tracking the menstrual cycle (MC) is essential for women to manage their health and schedule, especially for those with irregular MC. Existing MC tracking methods either rely on length of previous cycles (e.g., calendar noting) or require additional devices to collect more information (e.g., basal temperature), which are not able to realize both accuracy and convenience. Inspired by the medical studies that gait patterns will be affected by MC, we design a smartphone-based application named MC-Tracking, which monitors MC based on the Inertial Measurement Unit (IMU) signals. By identifying the walking activity based on the acceleration and angular velocity signals, we train an attention-based prediction model that can be generalized to new users with meta learning. 40 volunteers participate in an extensive experiment for more than 3 months, in which more than 2.4 TB of time-series data is collected to evaluate the performance of MC-tracking. It is verified that MC-tracing can predict the onset of MC seven days in advance with an average error of 0.56 days. We also demonstrate that the prediction accuracy is robust to the age, emotion, biological clock and smartphone brand.

Alzheimer’s disease (AD) is an insidious and progressive neurodegenerative disease, and the annual relevant social cost for AD patients can reach about $1 trillion worldwide. Therefore, early diagnosis and treatment of AD play a vital role in slowing disease progression. However, existing detection methods for cognitive impairment cannot consistently screen the stage of AD. To tackle this challenge, we propose an AD detection system, Ubi-AD, which combines the features of multiple biomarkers to realize passive and accurate AD detection. Unlike existing work, Ubi-AD can passively recognize the AD digital biomarkers during daily smartwatch usage without interfering with the user. At the user end, Ubi-AD first extracts the non-speech sounds (pause words, such as em, ah), which contain no privacy-sensitive content. Then, Ubi-AD recognizes the user’s walking activity, dining activity, and sleep activity from daily activities. Ubi-AD analyzes these data from smartwatch and predicts the AD stages using a multi-modal fusion neural network at the cloud end. We evaluate our model on a collected dataset from 45 volunteers. As a result, Ubi-AD can reach a detection accuracy of 93.4%, which means that Ubi-AD can provide multiple effective biomarkers for ubiquitous and passive detection in daily life.

Asthma is a common respiratory disease in modern society. However, people are rarely aware of the symptoms of asthma because the early stage of asthma is similar to that of the common cold (e.g., wheeze, cough, and shortness of breath). To tackle this challenge, we propose an asthma detection system, Ubi-Asthma, based on the smartwatch. Ubi-Asthma combines breathing signals and guttural sound (e.g., cough sound and throat-clearing sound) signals to realize passive and accurate asthma detection without interrupting the user. Not only can Ubi-Asthma extract breathing signals from the user even if the user is walking but also recognize guttural sound signals when the user is engaged in voice communication without being disturbed by noise. The features of breathing and guttural sound are combined to improve the accuracy of asthma detection. We have implemented a fully functional prototype using an off-the-shelf smartwatch. Fifty volunteers participate in an extensive experiment of up to 150 h to train Ubi-Asthma. As a result, Ubi-Asthma can reach a detection accuracy of 98.4%, which is higher than breath- or guttural-based asthma detection systems. Ubi-Asthma is expected to provide a potential solution for smart-home applications in the future.

Blood pressure (BP) is an essential vital sign related to many severe diseases, such as heart failure, kidney failure. Frequent BP detection can provide doctors more information to treat the disease. However, conventional at-home BP detection devices require completely blocking blood flow, which can lead to discomfort and disruption of normal activity when users want to perform frequent assessments. So a convenient solution should reduce the trouble of detecting BP in the daily life. In this work, we have designed and evaluated a smartphone-based BP detection application named WIB . WIB utilizes the smartphone’s acoustic sensors to obtain the chest motion caused by the heart beating and the smartphone’s camera to capture the pulse at the fingertip. We have recruited 30 volunteers who come from the author’s institution, to carry out comprehensive evaluations of WIB . We perform BP perturbation experiments to obtain different blood pressure data from these volunteers. The experiment results show that the average of Pearson correlation coefficient across all volunteers of the blood pressure estimation is 0.42–0.74 (α =0.6, σ =0.12), the average of RMSE across all volunteers is 4.2–8.8 mmHg (α =5.8, σ =1.8).

Fatigue is believed to be the leading factor for traffic accidents (e.g., fatigue driving) and health problems (e.g., heart disease and diabetes). However, fatigue-related risks are difficult to quantify because there is no efficient and reliable fatigue detection method comparable to blood alcohol testing for drunk drivers. Conventional fatigue detection methods either require wiring of sensors (e.g., EEG and ECG) that are inconvenient or leverage video camera systems that are lighting sensitive and may leak privacy. We present Ubi-Fatigue, a comfortable and contactless fatigue monitor system using wireless signals. Ubi-Fatigue combines both vital signs and facial features to achieve reliable fatigue detection. A series of novel signal recovery algorithms is developed to extract the heartbeat signal and the eye blink signal from the same raw signal captured by the single-antenna radar. We have implemented a fully functional prototype of Ubi-Fatigue using off-the-shelf radar. Twenty volunteers are involved in extensive experiments for a total duration of 480 h with more than 60 h of collected time-series data. The results demonstrate that Fatigue-Radio can reach a detection accuracy of 81.4%, which is higher than ECG- or visual-based fatigue detection systems, and is approximated to the ECG + visual-based fatigue detection system. Ubi-Fatigue is expected to provide a potential solution for smart home applications in the coming days.

Myocardial infarction (MI) is the myocardial necrosis caused by persistent ischemia and hypoxia of coronary arteries. People do not realize that they are suffering from MI until they have a heart attack. Early MI detection plays a vital role in symptom relief and improvement in the performance of daily activities. However, conventional MI detection methods require expensive and inconvenient medical tests, e.g., intrusive blood tests or wear electrocardiogram (ECG) sensors, which can only be performed in medical institutions. In this paper, we introduce a contactless and non-intrusive MI detection method based on wireless sensing that monitors abnormalities in heartbeats. Specifically, we present Health-Radio, a radar-based system towards early MI detection. Health-Radio extracts heart rate variability (HRV) from the RF signals reflected from users. In particular, with our carefully designed signal processing algorithms, Health-Radio is able to not only obtain heartbeat signals when the user is stationary, but also tolerate interference when the user is performing certain activities, e.g., eating, reading and browsing the Internet. We have recruited 30 MI patients from the Central Hospital of Wuhan, China, and 30 healthy university students to conduct comprehensive evaluations of the performance of Health-Radio. The experiment results show that Health-Radio can achieve a median MI detection accuracy of 81.2 percent when the users are stationary, which is comparable to ECG-based MI detection. Even when the users are not stationary, Health-Radio can still achieve a median detection accuracy of 66.5 percent. Health-Radio is promising in providing a new paradigm for smart-home healthcare in the future.