Lei Ding

Leather is among the most widely traded textile materials worldwide, and detecting surface defects is crucial to ensure its quality. The 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 article, we introduce Le-Radio, an Industrial Internet of Things (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 (CRSs) 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.

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 TorsoTrack, a nonintrusive, 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 multilayer 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 average $R^{2}$ of 0.9822, RMSE of 1.6733, and MAE of 0.9977. For previously unseen individuals, the system maintains an average $R^{2}$ of 0.9033, demonstrating its effectiveness in precise torso inclination assessment.

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.

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.

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 article 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 paper introduces BACFuse, a device-free, contactless, and non-invasive 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 multi-modal 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 a MAE of 2.13mg/dl, showing promise for future in-car driving management paradigms.

The purpose of this paper is to apply an intelligent algorithm to conduct the torque tracking control for lower limb exoskeleton robot driven by an electro-hydraulic servo system (EHSS). The dynamics of EHSS actuating the robot are mathematically modeled with two degrees of freedom joints, and the torque control strategy is made to realize the minimization of human-machine forces. The PI controller is selected to implement this strategy, and the selection of PI gains is important for system control. Therefore, the ameliorative moth flame optimization (AMFO) algorithm is chosen to optimize the PI gains. The main idea of moth flame optimization is to mimic the evolution of a moth’s lateral positioning mechanism over time. The AMFO algorithm is capable of achieving enhanced better global and local search capabilities by adding the inertia weights to the position update formula. Moreover, the particle swarm optimization (PSO) and whale optimization algorithm (WOA) are selected to obtain comparative results. The results indicate that, when compared to the WOA-PI and PSO-PI controllers, the AMFO-PI controller gains the least amount in terms of rise time, overshoot, steady error, mean absolute error, and root mean square error.

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.

A highly sensitive directional bending sensor based on a three-core fiber (TCF) Mach-Zehnder interferometer (MZI) is presented in this study. This MZI-based bending sensor was fabricated by fusion-splicing a section of TCF between two single-mode fibers (SMF) with core-offset. Due to the location of the core in the TCF, a bend applied to the TCF-based MZI led to an elongation or shortening of the core, which makes the sensor suitable for directional bending measurement. To analyze the bending characteristics, two types of TCF-based sensors, with the fusion-spliced core located at different positions between the SMFs, were investigated. A swept source was employed in the measurement technique. The experimental results showed that, for the two types of sensors in this setup, the bending sensitivities of the two sensors were 15.36 nm/m⁻¹ and 3.11 nm/m⁻¹ at the bending direction of 0°, and −20.48 nm/m⁻¹ and −5.29 nm/m⁻¹ at the bending direction of 180°. The temperature sensitivities of the two sensors were 0.043 nm/°C and 0.041 nm/°C, respectively. The proposed sensors are compact, versatile, inexpensive to fabricate, and are expected to have potential applications in biomedical sensing.