Generating high-quality sketches from video requires a nuanced understanding of semantic content and visual structure, particularly for complex scenes across diverse sketch styles. Efficient and flexible video-to-sketch style transformation remains a significant challenge. We introduce VideoSketcher, a training-free framework for style-controllable sketch video generation that preserves frame structure while applying specified sketch aesthetics. Leveraging text-to-image diffusion models, VideoSketcher utilizes strong semantic priors without the need for extensive training. Our approach enforces temporal consistency by retaining latent information across frames and employs a Time-Linked Attention mechanism to capture structural elements from the source video and inject stylistic information from the reference image. To bridge the semantic gap between sketches and original video content, we introduce Sketch Directive Amplification for selective transfer of stylistic features. Additionally, a Stroke Graph Regularization strategy, comprising line and point loss, refines line consistency in the latent space. Extensive experiments validate VideoSketcher’s superior temporal stability and fidelity across diverse sketch styles and content. Video demos can be found in the supplementary materials.

Early dementia detection is a global healthcare priority in diverse populations. In this study, we propose a language-agnostic screening pipeline for dementia detection in the early stage. First, we use speaker diarization to isolate the speech of the target subject from a conversational recording. From the extracted speech segments, we derive a set of acoustic features (e.g., spectral centroid, pitch mean, mel-frequency cepstral coefficients) and linguistic features (e.g., normalized tone contrast, articulation clarity coefficient, articulatory effort coefficient). These features are used to train a ResNet-based binary classifier to distinguish between Healthy Controls (HC) and individuals with Mild Cognitive Impairment (MCI). We evaluated the trained model on a held-out test set comprising speakers of previously unseen languages, achieving an accuracy of 70%. This cross-lingual transfer performance highlights the potential of our approach for scalable, language-independent dementia screening.

Fingertip forces are important biomarkers for the detection and management of various conditions, including stroke and Parkinson's disease. This paper presents TouchWave, a non-contact sensing system designed to monitor fingertip forces during activities of daily living (ADL). TouchWave leverages under-cabinet millimeter-wave (mmWave) sensors to capture both macroscopic hand movements and subtle biomechanical cues associated with fingertip force production. A novel signal processing scheme is developed to suppress noise while preserving force-related information in the mmWave signals. Additionally, a hybrid deep neural network model is proposed to estimate highfidelity fingertip forces. A comprehensive evaluation involving 21 participants demonstrates the effectiveness of TouchWave in both controlled settings and ADL scenarios.

In order to determine the overall health of an individual, hand grip strength has emerged as a reliable and widely used indicator of muscular and functional health. However, the conventional devices for measuring grip strength, such as dynamometers, require direct interaction with a bulky external device. In this work, we propose a novel, cost-effective approach to estimate grip strength using bio-acoustic signals captured from the forearm via a compact armband equipped with low-power MEMS microphones. Our method performs well on grip strength classification with an accuracy of 93.33%, and as a proof of concept, demonstrates a promising direction for non-invasive grip strength estimation.

Monitoring human motion through inertial metrics is vital for healthcare, rehabilitation, and activity recognition. Traditional approaches rely on wearable inertial measurement units (IMUs), which, despite their accuracy, impose burdens due to their intrusive nature, limiting long-term usability. To mitigate this, recent advances explore device-free alternatives, such as pose-based inertial inference from video or mmWave sensing. However, inertial signals derived from pose tracking are prone to error amplification during differentiation. In this paper, we present mV-IMU, a novel mmWave-enabled Virtual Inertial Measurement Unit framework that bypasses pose estimation altogether to directly reconstruct body accelerations from raw mmWave signals. Our approach leverages a deep inertia reconstruction model trained on kinematics-informed features extracted from mmWave point clouds, integrated with a physicsguided optimization scheme for enhanced accuracy. Extensive evaluations show that mV-IMU achieves inertial measurement fidelity close to wearable IMUs, enabling practical, non-intrusive motion monitoring for smart healthcare and rehabilitation contexts.

The electrocardiogram (ECG) has been the gold standard for heart disease evaluation due to the rich information about the electrical activity of the heart contained in it. However, existing ECG monitoring devices either lack the capability for continuous monitoring or are unable to support multi-lead ECG recordings. To address the issues, we propose an approach for generating multi-lead ECG from photoplethysmogram (PPG), which can be passively monitored by wearable devices such as smartwatches. The PPG collected from wearable devices is first passed to a trained conditional diffusion model to generate the single-lead ECG, and then through a long short-term memory (LSTM) model to construct and predict the multi-lead ECG. The final outputs can be used to monitor and detect abnormal cardiac patterns in daily life. We evaluate the performance of our proposed approach with the dataset collected from dailylife scenarios involving 32 subjects. The results show that our approach can generate multi-lead ECGs accurately. In addition, a case study is conducted using data collected from the hospital, which demonstrates the effectiveness of our approach in detecting ST elevation.11ST elevation refers to an upward deviation of the ST segment on an electrocardiogram (ECG) from the baseline, indicating a potential heart attack or other cardiac issues. It is a crucial diagnostic finding in acute myocardial infarction (heart attack) and requires prompt medical attention. It is a key indicator of myocardial ischemia in practice.

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semantic-focused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely resemble handcrafted results, outperforming existing methods in expressive stroke control and semantic coherence. Codes are available at https://github.com/rane7/Stroke2Sketch.

This study explores how virtual reality (VR) can be used to evaluate the impact of planogram design on shopper behavior in retail environments. We capture multi-channel sensor data and build in-VR visualizations (spatial heatmaps and shelf-level histograms) to support rapid analysis of shopper behavior and inform planogram designs. The results show that product placement significantly affects shopper interaction. This suggests that VR-based shelf perception analytics can support strategic planogram design, particularly for improving the visibility and reachability of high-priority items.

The hand localization problem has been a longstanding focus due to its many applications. The task involves modeling the hand as a singular point and determining its position within a defined coordinate system. However, due to data modality limitations, existing hand localization technologies face several challenges. For example, vision-based localization raises privacy concerns, while wearable-based methods compromise user comfort. In this article, we introduce mmHand, a new device-free, privacy-preserving dynamic hand localization system with pixel-level accuracy, using a single commodity mmWave device. We first propose a mmImage generation tool to fully extract spatial information from raw mmWave data and introduce a novel 2-D image-format representation of mmWave data. Next, we design a framework that provides a new quality evaluation method and pixel space labeling for the mmWave data. Finally, we present a cross-modality spatial feature-enhanced model with high spatial feature extraction capabilities, which can accurately localize hand positions at the pixel level in the mmWave radar U-V pixel coordinate system. We evaluate the system with experiments on 12 subjects in three scenarios, and the results across four metrics demonstrate the effectiveness of our hand localization system.

Cardiovascular metrics measurement and monitoring have been a critical need worldwide. The main objective of this work is to prototype an embedded imager for cardiovascular metrics monitoring. Utilizing an 850 nm Near-Infrared (NIR) light source and an Infrared (IR) camera, the system leverages the optical properties of human skin to extract Photoplethysmogram (PPG) signals, heart rate, and vascular structure from video data. We tested the system with 10 participants, comparing its heart rate measurements to those obtained from a contact PPG sensor, achieving an accuracy within ±5 bpm. Additionally, using an artificial hand phantom for blood vessel visualization, the system demonstrated a vessel extraction accuracy with an average error of 10.21 % in blood vessel width, confirming the effectiveness of our NIR-enhanced imaging approach.

Due to the complexity in understanding human behaviors under limited observations and insufficient training data, video anomaly detection is challenging. Most of existing approaches solely rely on visual clues and suffer from noisy observations which easily lead to unreasonable predictions. In this paper, we introduce physically explainable dynamics to enhance visual representations. Firstly, a Physical Intuition (PI) module is proposed to be combined with a Visual Representation (VR) module in estimating the forces applied on subjects. Secondly, a hierarchical structure is proposed to facilitate PI module in achieving physically plausible descriptions of human movements while maintaining the consistency with visual representations. Thirdly, a novel anomaly score is proposed considering the distributions of forces. Extensive experimental results on five benchmark datasets show that state-of-the-art performance can be achieved by the proposed framework with a strong robustness.

As the demand for precision medicine rapidly grows, companion diagnostics is proposed to monitor and evaluate therapeutic effects for adjusting medicine plans in time. Although a set of clinical companion diagnostics tools (e.g., polymerase chain reaction) have been investigated, they are expensive and only accessible in a lab environment, which hinders the promotion to broader patients. In light of this situation, we take the first steps towards developing a real-world companion diagnostic tool by leveraging mobile technology. In this paper, we present TherapyPal, a privacy-preserving medicine effectiveness computational framework by harnessing semantic hashing-based digital symptomatic phenotyping. Specifically, sensor data captured from daily-life activities is first transformed into spectrograms. Then, we develop a hashing learning network to extract privacy-masked symptomatic phenotypes on smartphones. Afterward, symptomatic hashes at different medicine states are fed to a contrastive learning network in the cloud for treatment effectiveness detection. To evaluate the performance, we conduct a clinical study among 65 Parkinson's disease (PD) patients under dopaminergic drug treatment. The results show that TherapyPal can achieve around 84.1% medicine effectiveness detection accuracy among patients and above 0.925 privacy-masked scores for protecting each private attribute, which validates the reliability and security of TherapyPal to be used as a real-world companion diagnostics tool.

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we presentVocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically,VocalPrintexploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the vocal tract and vocal source features and input them into an ensemble classifier for authentication.VocalPrintis practical as it allows the effortless transition to a smartphone while having sufficient usability due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show thatVocalPrintachieves over 96 percent authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

In this paper, we propose a transformative solution that uses a low-cost light sensor and commodity smartphone to support fast self-assessment of blood perfusion of ulcer regions in daily life. By harnessing the knowledge of light polarization, our system can "see-through" the skin to quantify the spatio-temporal properties of subdermal vasculature in terms of pulsation and hemoglobin. Our evaluation results show that our system can achieve 78.6% accuracy to detect poor and good blood perfusion.

Voice user interfaces (VUIs) have been adopted in many IoT and mobile devices in daily life. VUIs provide a good user experience with lower-cost hardware (i.e., microphone) and higher throughput (compared with keyboard and touchscreen). Currently, identity authentication and receiving commands are the two most common interactions through VUIs, leaving physiological information in the voice unexploited. Recognizing this untapped potential, we propose VocalHR to extend VUIs beyond voice commands to heart activity sensing without additional hardware. VocalHR is built upon the voice-heart modulation effect, which is rooted in the cardiac activities' impacts on the behavior of the vocal organ during voice production. VocalHR captures voice features of cardiac activity in multiple voice organs and proposes a deep learning pipeline to transform features into cardiac activities. As this is the first study exploring voice-based heart activity sensing, we conducted extensive experiments on 43 demographically diverse subjects to verify the intrinsic link between voice and heart activities. On average, VocalHR can achieve less than 11.1% normalized sensing error on the heart event timing. Our further evaluation shows VocalHR is robust to different microphone specifications and varying speech rates.

Temperature sensor is one of the most widespread technologies in the IoT era. Wireless temperature monitoring systems are convenient to deploy and can drive mass applications in the fields of smart home, transportation and logistics. Currently, wireless temperature monitoring products are based on microelectronic and semiconductor components, which are not cost-effective (e.g., a few dollars) and more importantly, generate electronic wastes. In this work, we present ThermoWave, a new paradigm of wireless temperature monitoring that is ecological, battery-less, and ultra-low cost. Specifically, ThermoWave is on the basis of the thermal scattering effect on millimeter-wave (mmWave) signals. Specifically, cholesteryl materials align their molecular patterns at different environmental temperatures, and this temperature-induced pattern change will be modulated and sensed by the scattered mmWave signals. There are three functional modules in the ThermoWave system. The ThermoTag is a cholesteryl material inked film or paper tag that can be conveniently attached to the object of interest to monitor temperature changes. Each ThermoTag costs less than 0.01 dollars. The temperature modulated mmWave scattering will be received by a mmWave-radar based ThermoScanner and demodulated by a software-based temperature decoder ThermoSense, which includes a model-based method (i.e., ThermoDot) for point temperature estimation and a data-driven method (i.e., ThermoNet) for thermal imaging. We prototype and evaluate the ThermoWave system performance in both controlled and real-world setups. Experimental results show that the ThermoWave achieves the precision of ±1.0°F in the range of 30°F to 120°F in a controlled setup. We also investigate the performance in real-world applications, and the ThermoWave can reach the ±3.0°F precision in the temperature estimation. We also test and discuss sustainability, durability, robustness, and cost-effectiveness of the ThermoWave in both design and experiments.

Drug effectiveness management is a complicated and challenging task in chronic diseases, like Parkinson's Disease (PD). Drug effectiveness control is not only linked to personal out-of-pocket cost but also affecting the quality of life among patients with chronic symptoms. In the current practice, although that health and medical professionals still play a key role in the personalized treatment plan, the critical decision on drug selection falls upon the individual report when patients call in or visit the clinics. Unfortunately, most of the patients with chronic diseases either fail to report their day-to-day symptoms or have a limited access to medical resources due to economic constraints. In this paper, we present PDLens, a first smartphone-based system to detect drug effectiveness among Parkinson's in daily life. Specifically, PDLens can extract digital behavioral markers related to PD drug responses from everyday activities, including phone calls, standing, and walking. PDLens models the PD symptom severity on drug treatment and detects the change of severity scores before and after drug intake. A ranking-based multi-view deep neural network is developed to decide the drug effectiveness upon the symptom severity changes. To validate the performance of PDLens, we conduct a pilot study with 81 PD patients and monitor their smartphone activities and severity changes over 33693 drug intake events across six (6) months. Compared with the standard clinical drug effectiveness test developed by Motor Disorder Society, results reveal that PDLens is a promising tool to facilitate drug effectiveness detection among PD patients in their daily lives.

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present VocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically, VocalPrint exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations during communication. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the text-independent vocal tract and vocal source features and input them to an ensemble classifier for user authentication. VocalPrint is practical as it leverages a low-cost, portable, and energy-efficient hardware allowing effortless transition to a smartphone while having sufficient usability as typical voice authentication systems due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that VocalPrint can achieve over 96% authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

Voice-user interface (VUI) has become an integral component in modern personal devices (\textite.g., smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low-quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial-of-service (DoS) of user requests. Therefore, it is of paramount importance to enable noise-resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio-frequency signals, such as millimeter wave (mmWave) for sensing the noise-resistant voice of an individual. We first perform an in-depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end-to-end noise-resistant speech sensing system.WaveEar comprises a low-cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near-throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real-world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise-resistant voice and enable a pervasive VUI in modern electronic devices.

Inventory management is pivotal in the supply chain to supervise the non-capitalized products and stock items. Item counting, indexing and identification are the major jobs of inventory management. Currently, the most adopted inventory technologies in product counting/identification are using either the laser-scannable barcode or the radio-frequency identification (RFID). However, the laser-scannable barcode is entangled by an alignment issue (i.e., the laser reader must align with one barcode in line-of-sight), and the RFID is economically and environmentally unfriendly (i.e., high-cost and not naturally disposable). To this end, we propose FerroTag which is a paper-based mmWave-scannable tagging infrastructure for the next generation inventory management system, featuring ultra-low cost, environment-friendly, battery-free and in-situ (i.e., multiple tags can be simultaneously processed outside the line-of-sight). FerroTag is developed on top of the FerroRF effects. Specifically, the magnetic nanoparticles within the ferrofluidic ink reply to probing mmWave with classifiable features (i.e., the FerroRF response). By designating the ink pattern and hence the location of particles, the related FerroRF response can be modified. Thus, a specifically designated ferrofluidic ink printed pattern, which is associated with a unique FerroRF response, is a remotely retrievable (a.k.a., mmWave-scannable) identity. Furthermore, we augment FerroTag by designing a high capacity pattern system and a fine-grained identification protocol such that the capacity and robustness of FerroTag can be systematically improved in mass product management in inventory. Last but not least, we evaluate the performance of FerroTag with 201 different tag design patterns. Results show that FerroTag can identify tags with an accuracy of more than 99% in a controlled lab setup. Moreover, we examine the reliability, robustness and performance of FerroTag under various real-world circumstances, where FerroTag maintains the accuracy over 97%. Therefore, FerroTag is a promising tagging infrastructure for the applications in inventory management systems.

With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.

Energy Internet (EI) is proposed as the evolution of smart grid, aiming to integrate various forms of energy into a highly flexible and efficient grid that provides energy packing and routing functions, similar to the Internet. As an essential part in EI system, a scalable and interoperable communication infrastructure is critical in system construction and operation. In this article, we survey the recent research efforts on EI communications. The motivation and key concepts of EI are first introduced, followed by the key technologies and standardizations enabling the EI communications as well as security issues. Open challenges in system complexity, efficiency, reliability are explored and recent achievements in these research topics are summarized as well.