Maintaining focus is essential for carrying out tasks accurately and efficiently, but it can be difficult to do so in settings that are full of distractions and continuous change in daily life, the workplace, and education. Traditional assessment methods such as self-reporting, eye tracking, or camera-based observation, are often intrusive, subjective, or limited by privacy concerns. To address these limitations, this study proposes a novel radar-based framework for continuous estimation of human attention during fine-grained hand–object interaction tasks using frequency-modulated continuous-wave (FMCW) millimeter-wave radar. We capture fine-grained Doppler–time motion patterns. The considered activities included pouring water, stacking cups, and writing, representing different motion types (translational, repetitive, and fine-motor) collected under focused and distracted conditions. A multi-input Deep regression network is introduced, which combines handcrafted behavioral descriptors with temporal Doppler–time feature embeddings using a 1D–CNN– BiLSTM–Attention fusion pipeline. This network simultaneously encodes motion smoothness, energy dynamics, and temporal regularity to derive attention scores on a 0-100 scale. Extensive experiments show the model’s capability to generalize across different subjects and activities, achieving an overallRMSEof ≈2.21 andR2of 0.9959. Ablation analysis validates the needs of both handcrafted features and multi-head-attention fusion for performance. signal-to-noise ratios below 15 dB, noise tests revealed a performance decline of less than 5%. Changes in attention impact behavioral patterns was gained through correlation analysis. Our approach offers a privacy-preserving and contactless alternative to existing methods, making it ideal for use in classroom monitoring, workplace productivity assessment, and cognitive health evaluation.

Accurate authentication of paper-based materials is crucial for secure document verification, counterfeit currency detection, and intelligent packaging across industries such as finance, logistics, and security. Differentiating between subtle variations, such as glossy, recycled, coated, or counterfeit paper, remains a significant challenge due to minimal material-level differences. We introduce PSense, the first millimeter-wave MIMO radar-based sensing framework for paper material analysis, offering a fully contactless solution that relies solely on reflected signals, eliminating the need for transmission-based or dual-sided setups. To characterize intrinsic paper properties, we propose a novel material-sensitive feature called the Energy Reflection Ratio (ERR), which encodes key physical interactions including surface reflectivity, internal attenuation, and dispersion, uniquely capturing the electromagnetic signature of each paper type. We further present Trans-PapNet, a dual-model transfer learning framework designed to enhance classification robustness and generalization. One model extracts spatial and spectral patterns from radar signal scalograms using ResNet-50, while the other processes ERR-based physical features. These complementary representations are fused via a shared attention mechanism, enabling effective domain adaptation across paper types. Our method achieves an overall classification accuracy of 97.85%, with strong performance in three knowledge-based transfer scenarios (94.8%, 93.0%, and 90.0%) and under both seen and unseen material conditions (95.3%). These results underscore PSense's robustness and scalability, paving the way for real-world deployment in mobile, secure, and intelligent paper-based authentication systems.

Traditional RF-based liquid identification methods generally rely on a single characteristic, such as refractive index or permittivity, and often assume prior container knowledge, limiting their versatility. These approaches also face challenges in scenarios involving gradual state changes in the liquid. We propose LiqState, a contactless framework for fine-grained liquid identification and continuous state monitoring, capable of operating without prior container information. To mitigate container effects, we developed a LiqState reflection model that analyzes frequency-dependent changes, leveraging the diverse permittivity profiles of liquids across the mmWave frequency range. Our approach introduces a novel feature extraction method, VRCP, which captures four distinct physical and chemical properties for robust identification and state monitoring. Using LiqNet, a service-oriented and customized deep learning model, LiqState achieves an average classification accuracy of 97.3% across diverse conditions, accurately distinguishing 12 liquid types. Additionally, case studies highlight LiqState’s capability to monitor complex processes, such as milk fermentation (RMSE: 0.251) and fruit juice ripening (RMSE: 0.162), and differentiate between similar liquids with minimal alcohol concentration variations.

Wireless sensing offers a promising approach for non-destructive and contactless identification of the moisture content in fruits. Traditional methods assess fruit quality based on external features, such as color, shape, size, and texture. However, fruits often appear perfect externally while being rotten inside. Thus, accurately measuring internal conditions is crucial. This paper introduces mmFruit, a non-destructive and ubiquitous system that employs mmWave signals for precise and robust moisture level sensing in thin and thick pericarp fruits. We propose a novel dual incidence moisture estimation model for regular moisture monitoring to achieve high granularity and eliminate fruit type and size dependency. Additionally, we leverage unique reflection responses across different mmWave frequencies to provide discriminative information about fruit moisture levels. Our comprehensive theoretical model demonstrates how fruits’ refractive index, attenuation factor, and elasticity can be estimated by eliminating fruit type dependency. We developed an electric field distribution model utilizing two receiving antennas to address the challenge of varying fruit sizes through a differential approach, aiming to improve overall robustness. mmFruit integrates a customized Spatial-invariant network (SpI-Net) to eliminate interference from different frequencies and locations, ensuring stable moisture monitoring regardless of target displacement. Extensive experiments were conducted over a month in varied environments on seven types of fruits with thin and thick pericarps (apple, pear, peach, mango, orange, dragon fruit, and watermelon). The results demonstrate that mmFruit achieves a commendable RMSE of 0.276 in moisture estimation. It accurately distinguishes fruits with minor moisture level differences (0% to 7%) with 93.6% accuracy and higher moisture differences (45% to 65%) with over 95.1% accuracy, even in scenarios involving diverse displacements and rotations.

Abstract: Target material sensing in non-invasive and ubiquitous contexts plays an important role in various applications. Recently, a few wireless sensing systems have been proposed for material identification. In this article, we introduce mm-CUR, A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles using a Millimeter-Wave Sensor. This system eliminates the need for individual note inspection and pinpoints the location of counterfeit notes within the bundle. We use Frequency Modulated Continuous Wave (FMCW) radar sensors to classify different counterfeit currency bundles on a tabletop setup. To extract informative features for currency detection from FMCW signals, we construct a Radio Frequency Snapshot (RFS) and build signal scalogram representations that capture the distinct patterns of currency received from different currency bundles. We refine the RFS by eliminating multi-path interference, and noise cancellation and apply high pass filters for mitigating the smearing effect with the continuous wavelet transform (CWT). To broaden the usage of mm-CUR, we built a transferable learning model that yields robust detection results in different scenarios. The classification results demonstrated that the proposed counterfeit currency detection system can detect counterfeit notes in 100-note bundles with an accuracy greater than 93%. Compared to the standard CNN and DNN methods, the proposed mm-CUR model showed superior performance in distinguishing each bundle data, even for a limited-size dataset.