Sensor Devices Research Articles

Effects of Fabrication Process of Conductive TPU Composites on Dual-Function Gas Sensor and Energy Devices

Effects of Fabrication Process of Conductive TPU Composites on Dual-Function Gas Sensor and Energy Devices

A Robust and Secure Electronic Internet of Things-Cloud Healthcare Framework for Disease Classification Using Deep Learning

In the modern era, the Internet of Things (IoT) based nano-electronic devices are essential to provide practical patient information through monitoring performance in the healthcare system. Moreover, remote health monitoring is becoming necessary for lowering healthcare expenses and enhancing patient care due to the aging population and the rise in chronic illnesses. Recently, there has been a lot of interest in the IoT based nanoelectronics devices as a potential solution for remote health monitoring. An extensive range of physiological data, including heart rates, blood oxygen levels, body temperatures, ECG signals, etc., can be collected and analyzed by IoT-based sensors devices, giving medical practitioners real-time feedback so they can respond appropriately. Therefore, in this paper, a novel Functional neural network-based Aquila optimization (FNN-AO) algorithm is developed to classify the various types of diseases. Furthermore, an Identity-based Encryption (IbE) algorithm is adapted to secure the healthcare data. Initially, IoT based electronic sensors collect and store healthcare data in the cloud storage system. This process was done with the help of the MATLAB platform, and parameters were analyzed and compared with existing techniques in terms of performance metrics. From the comparison, the proposed framework has a 2.34% improvement over the other models.

In situ synthesis of long tubular water-dispersible polyaniline with core/shell gold and silver@graphene oxide nanoparticles for gas sensor application

Gold nanoparticles (Au NPs) with graphene oxide (GO) shell (Au@GO), silver nanoparticles (Ag NPs) with GO shell (Ag@GO), and gold silver nanoparticles (AuAgNPs) with GO shell (AuAg@GO) were synthesized employing a cationic surfactant. The prepared core@shell structures were used for in situ synthesis of long tubular polyaniline structures employing cetyl trimethyl ammonium bromide (CTAB) as a soft template. This process led to a notable enhancement in the tubular nanostructure of PANI, extending its length beyond 10 μm, in the case of using core/shell Au@GO, Ag@GO, and AuAg@GO structures. To evaluate their applicability and compatibility, the dispersibility of these nanocomposites was assessed in three distinct solvents: water, dimethyl sulfoxide (DMSO), and N-Methyl-2-pyrrolidone (NMP). Subsequently, the dedoping of PANI within the prepared nanocomposites was scrutinized using UV–Visible (UV–Vis) spectroscopy, which revealed a reduction in the I750/I315 ratio from 1.00 to 0.66 when subjected to water and NMP solvents, respectively. Notably, the dedoping of the AuAg@GO/PANI nanocomposite was predominantly observed in NMP, attributable to the presence of hydrogen bonding interactions and the basic properties of NMP. In terms of ionic conductivity, it was observed that the prepared nanocomposite exhibited its highest conductivity in a water-based medium, registering at 1982 μs. Furthermore, the AuAg@GO/PANI nanocomposite exhibited superior sensing capabilities in comparison to PANI-based gas sensor devices, particularly when exposed to acetone, CO2, NO2, and H2S. Remarkably, at room temperature (25 °C), the AuAg@GO/PANI nanocomposite displayed rapid response and recovery times, with values of 279 s, 431 s, 335 s, and 509 s for 1 ppm concentrations of CO2, NO2, H2S, and acetone, respectively. The sensitivity of these sensors towards acetone, CO2, NO2, and H2S, was quantified by analyzing the slope of the response versus the target gas concentration, revealing the AuAg@GO/PANI nanocomposite to exhibit the highest sensitivity, particularly towards NO2.

Synthetic data

The rise of data-driven businesses poses a number of significant challenges for contemporary organisations. These include legal and ethical considerations arising from the use of personal data, the growing challenges of information security, and the difficulty managing the volume of data generated in business transactions of different kinds. The exponential growth of data continues unabated with global data volumes reaching 181 zettabytes by 2025, and with 90% of the world’s data generated in the last two years alone. This massive growth can be mainly attributed to data gathered by Internet of Things (IoT) and related sensory devices, in addition to data generated through the human use of digital tools and applications. Given this abundance of real-world data, in what context could synthetic data be necessary? This paper highlights the growing organisational use of synthetic data and explores where and how it can be optimally used. It examines the ethical aspects of synthetic data usage, the need to garner public perception and acceptance, and the key aspects of traceability, accountability and risk mitigation.

A Systematic Review of Electrochemical Sensing Devices Based on Metal-organic Frameworks for the Identification of Tumor Biomarkers

Background:: Electrochemical sensors have attracted enormous interest in recent decades due to their relatively simple synthesis process, the possibility of modifications in their composition, and their wide application in detecting analytes. Method:: To achieve excellent performance, materials used in its construction must facilitate the transfer of electrons and provide a large surface area and excellent electrocatalytic activity. Because they are functional nanomaterials, metal-organic frameworks (MOFs) meet the criteria for application as a sensing material and can be used to build high-performance sensors for numerous applications. They play a relevant role in the early diagnosis of various neoplasms and tumor processes, thus providing better prognoses. Considering this universe, the present study focused on evaluating the application of these nanomaterials in the recognition of tumor biomarkers. Result:: A systematic review of scientific publications was performed using the following descriptors "MOFs", "Metal-organic frameworks", "Biosensor", "Electrochemistry", and "Tumor Biomarker". Conclusion:: Herein, we analyze recent innovations of MOF-based biosensors applied to the detection of tumor biomarkers, discussing how promising characteristics and properties of these materials were harnessed in the development of biosensors to provide a new contribution to future studies and the development of applied MOFs to electrochemical biosensing.

A Guide to Printed Stretchable Conductors.

Printing of stretchable conductors enables the fabrication and rapid prototyping of stretchable electronic devices. For such applications, there are often specific process and material requirements such as print resolution, maximum strain, and electrical/ionic conductivity. This review highlights common printing methods and compatible inks that produce stretchable conductors. The review compares the capabilities, benefits, and limitations of each approach to help guide the selection of a suitable process and ink for an intended application. We also discuss methods to design and fabricate ink composites with the desired material properties (e.g., electrical conductance, viscosity, printability). This guide should help inform ongoing and future efforts to create soft, stretchable electronic devices for wearables, soft robots, e-skins, and sensors.

Cater to the call of on-site, quantitative, and low-cost food formaldehyde detection: A paper-based sensor by timing Congo red decoloration

Given the illegal formaldehyde (FA) contamination in seafood/aqueous products, the instrument-free, chemical-free, low-cost sensor that enables on-site FA detection is of great interest. We have developed a paper-based analytical device (PAD) sensor to detect FA on-site for easy operation by integrating FA-responsive dye decoloration reaction on filter paper and timing decoloration time for quantitative signal interpretation. This innovative dry chemical PAD sensor can be easily prepared. The on-site FA detection is performed by dropping a drop of the sample into the sensor sampling zone and timing the decoloration time, respectively. The difference in decoloration time between a fictitious blank (5.0 μM) and an FA standard is proportional to the FA concentration in the range of 10–700 μM with a detection limit of 5.6 μM. The utility and effectiveness of this PAD sensor has been verified by applying it to seafood, paper cups, and water samples with the supports of official method validation.

A review of IoT security and privacy using the Blockchain to Boost Data Integrity and Privacy

The Internet of Things (IoT) is an emerging technology. It enables users to exchange their data and resources by linking devices across the IoT network, resulting in a more relaxed and connected lifestyle. It allows real-time data to be delivered to end-user applications and systems via sensing, actuation, communication, computing, networking, and storage. It can transfer data across a network without requiring human intervention or computer-to-computer interaction. It makes it possible to detect the various sensors around the world through low-priced sensor devices. IoT Networking defines communication or inter-connection between Peer-to-Peer (P2P) either through a connected medium (Wired) or unconnected medium (Wireless) to procure a secure transmission of data. Blockchain is a peer-to-peer network where peers can communicate and do transactions without the need for centralized authority. It allows data to be stored and transferred in a safe and interconnected method. This section includes a literature review on the centralized IOT system model, highlighting the limitations of the centralized IoT system and blockchain security in IoT. This research study's systematic evaluation is processed based on the reviews of the literature available with recent advancements and inventions through patents, articles, conference papers, and journals. Also, the related work with IoT systems for data privacy and security issues and applying Blockchain technology to IOT are discussed.

Implantable pH Sensing System Using Vertically Stacked Silicon Nanowire Arrays and Body Channel Communication for Gastroesophageal Reflux Monitoring.

Silicon nanowires (SiNWs) are emerging as versatile components in the fabrication of sensors for implantable medical devices because of their exceptional electrical, optical, and mechanical properties. This paper presents a novel top-down fabrication method for vertically stacked SiNWs, eliminating the need for wet oxidation, wet etching, and nanolithography. The integration of these SiNWs into body channel communication (BCC) circuits was also explored. The fabricated SiNWs were confirmed to be capable of forming arrays with multiple layers and rows. The SiNW-based pH sensors demonstrated a robust response to pH changes, and when tested with BCC circuits, they showed that it was possible to quantize based on pH when transmitting data through the human body. This study successfully developed a novel method for SiNW fabrication and integration into BCC circuits, which could lead to improvements in the reliability and efficiency of implantable medical sensors. The findings demonstrate significant potential for bioelectronic applications and real-time biochemical monitoring.

Cation-Selective Actuator-Sensor Response of Microcrystalline Cellulose Multi-Walled Carbon Nanotubes of Different Electrolytes Using Propylene Carbonate Solvent.

Microcrystalline cellulose (MC) with 50 wt.% multi-walled carbon nanotube (MCNT) composites is obtained through extrusion, forming MC-MCNT fiber. In this study, we concentrate on three different electrolytes in propylene carbonate (PC) which have the same anions (TF-, trifluoro-methanesulfonate CF3SO3-) but different cations, EDMI+ (1-ethyl-2,3-dimethylimidazolium), Li+ (lithium ion), and TBA+ (tetrabutylammonium). Cyclic voltammetry and square wave potential steps, in combination with linear actuation measurements in a potential range of 0.7 V to -0.2 V, were conducted. Our goal in this work was to establish a cation-selective actuator-sensor device capable of distinguishing different cations. The linear actuation of MC-MCNT fiber had its main expansion at discharge due to the incorporation of TF- in the MC-MCNT fiber with the cations. In the following order, TBA+ > EDMI+ > Li+ had the best stress, strain, charge density, diffusion coefficients, and long-term stability. Chronopotentiometric measurements revealed that the cations in the PC solvent can be differentiated by their ion sizes. Further characterization of the MC-MCNT fiber was completed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and FTIR and Raman spectroscopy.

CONTINUOUS MONITORING OF CRP, IL-6, AND CALPROTECTIN IN INFLAMMATORY BOWEL DISEASE USING A PERSPIRATION BASED WEARABLE

Abstract BACKGROUND Wearable sensor devices represent a noninvasive technology to continuously track biomarkers linked to inflammatory bowel disease (IBD). We assessed the inflammatory markers associated with IBD in human perspiration. METHODS Participants with IBD were monitored for 40-130 minutes with a proprietary wearable sensor device used to measure C-reactive protein (CRP), interleukin-6 (IL-6), and calprotectin. Sensor response using Electrochemical Impedance Spectroscopy (EIS) and serum samples were measured on the same day. Mann Whitney test was used to analyze the relationship between active and remission IBD in serum and perspiration, classified according to endoscopic reports and serum biomarker levels. Asynchronously collected fecal calprotectin from a subset of the population was similarly analyzed. RESULTS A total of 33 subjects were enrolled. Expression of CRP in perspiration and serum were comparable between Active and Remission cohorts (p>0.05). Median expression of IL-6 in perspiration was elevated in the Active cohort compared to Remission cohort, and significantly elevated in serum (p<0.05). Expression of calprotectin in perspiration, serum, and stool were significantly elevated in the Active cohort compared to Remission cohort (p<0.05). Temporal data for three ulcerative colitis and Crohn’s disease patients provided insight into the basal levels of the markers across different disease states. CONCLUSIONS We demonstrate the clinical utility of perspiration as a noninvasive medium for continuous measurement of inflammatory markers in IBD and find that the measures correlate with serum and stool markers across a range of disease activity. Positive linear relationship between serum and sweat of all markers. Serum and sweat levels of both Calprotectin and CRP demonstrate strong linear relationship between mediums. Serum and sweat levels of IL6 demonstrate moderate linear relationship and high homoscedasticity.

Utilizing Data Mining for the Synthesis of Functionalized Tungsten Oxide with Enhanced Oxygen Vacancies for Highly Sensitive Detection of Triethylamine.

The optimal combination of metal ions and ligands for sensing materials was estimated by using a data-driven model developed in this research. This model utilized advanced computational algorithms and a data set of 100,000 literature pieces. The semiconductor metal oxide (SMO) that is most suitable for detecting triethylamine (TEA) with the highest probability was identified by using the Word2vec model, which employed the maximum likelihood method. The loss function of the probability distribution was minimized in this process. Based on the analysis, a novel hierarchical nanostructured tungsten-based coordination with 2,5-dihydroxyterephthalic acid (W-DHTA) was synthesized. This synthesis involved a postsynthetic hydrothermal treatment (psHT) and the self-assembly of tungsten oxide nanorods. The tungsten oxide nanorods had a significant number of oxygen vacancies. Various techniques were used to characterize the synthesized material, and its sensing performance toward volatile organic compound (VOC) gases was evaluated. The results showed that the functionalized tungsten oxide exhibited an exceptionally high sensitivity and selectivity toward TEA gas. Even in a highly disturbed environment, the detection limit for TEA gas was as low as 40 parts per billion (ppb). Furthermore, our findings suggest that the control of oxygen vacancies in sensing materials plays a crucial role in enhancing the sensitivity and selectivity of gas sensors. This approach was supported by the utilization of density functional theory (DFT) computation and machine learning algorithms to assess and analyze the performance of sensor devices, providing a highly efficient and universally applicable research methodology for the development and design of next-generation functional materials.

Strain sensor based on polyurethane/carbon nanotube elastic conductive spiral yarn with high strain range and sensitivity

AbstractWearable flexible electronic strain sensor devices have gained significant attention in recent years due to their potential for detecting human motion in various scenarios. However, the development of strain sensors with high sensitivity across a wide range of strains remains a major challenge. We present herein a novel strain sensor based on a graded structure thermoplastic polyurethane (TPU)/carbon nanotube (CNT) composite yarn with significantly enhanced mechanical performance imparted by the designed structure. The twisted CNTs/TPU spiral yarn demonstrated a fracture elongation of up to 1066% while maintaining charge conductivity under high‐strain conditions. Moreover, it exhibited sensitive changes in resistance versus tensile strain, excellent repeatability, and stability. As a strain sensor, it achieved a gauge factor (GF) of 67.2 within a strain range below 50%, reaching 51.7 in a strain range exceeding 150%. With a fast response time of 0.12 s, it enabled accurate identification of movements in different body parts. These findings highlight the broad application potential of the designed spiral yarn strain sensor in areas such as human motion monitoring and human–computer interaction.Highlights Prepares a flexible strain sensor with graded‐structure (CNT‐fiber‐yarn). Shows high sensitivity and a wide strain response range Reveals the conductive model of strain sensor.

Surgical Sabermetrics: A Scoping Review of Technology-enhanced Assessment of Nontechnical Skills in the Operating Room.

To evaluate the current evidence for surgical sabermetrics: digital methods of assessing surgical nontechnical skills and investigate the implications for enhancing surgical performance. Surgeons need high-quality, objective, and timely feedback to optimize performance and patient safety. Digital tools to assess nontechnical skills have the potential to reduce human bias and aid scalability. However, we do not fully understand which of the myriad of digital metrics of performance assessment have efficacy for surgeons. A systematic review was conducted by searching PubMed, EMBASE, CINAHL, and PSYCINFO databases following PRISMA-ScR guidelines. MeSH terms and keywords included "Assessment," "Surgeons," and "Technology". Eligible studies included a digital assessment of nontechnical skills for surgeons, residents, and/or medical students within an operative context. From 19,229 articles screened, 81 articles met the inclusion criteria. The studies varied in surgical specialties, settings, and outcome measurements. A total of 122 distinct objective, digital metrics were utilized. Studies digitally measured at least 1 category of surgical nontechnical skill using a single (n=54) or multiple objective measures (n=27). The majority of studies utilized simulation (n=48) over live operative settings (n=32). Surgical Sabermetrics has been demonstrated to be beneficial in measuring cognitive load (n=57), situation awareness (n=24), communication (n=3), teamwork (n=13), and leadership (n=2). No studies measured intraoperative decision-making. The literature detailing the intersection between surgical data science and operative nontechnical skills is diverse and growing rapidly. Surgical Sabermetrics may provide a promising modifiable technique to achieve desirable outcomes for both the surgeon and the patient. This study identifies a diverse array of measurements possible with sensor devices and highlights research gaps, including the need for objective assessment of decision-making. Future studies may advance the integration of physiological sensors to provide a holistic assessment of surgical performance.

Mindful breathing as an effective technique in the management of hypertension.

Introduction: Hypertension is one of the most important, modifiable risk factors for cardiovascular disease. The popularity of wearable devices provides an opportunity to test whether device guided slow mindful breathing may serve as a non-pharmacological treatment in the management of hypertension. Methods: Fitbit Versa-3 and Sense devices were used for this study. In addition, participants were required to own an FDA or Health Canada approved blood pressure measuring device. Advertisements were shown to 655,910 Fitbit users, of which 7,365 individuals expressed interest and filled out the initial survey. A total of 1,918 participants entered their blood pressure readings on at least 1day and were considered enrolled in the study. Participants were instructed to download a guided mindful breathing app on their smartwatch device, and to engage with the app once a day prior to sleep. Participants measured their systolic and diastolic blood pressure prior to starting each mindful breathing session, and again after completion. All measurements were self reported. Participants were located in the United States or Canada. Results: Values of systolic and diastolic blood pressure were reduced following mindful breathing. There was also a decrease in resting systolic and diastolic measurements when measured over several days. For participants with a systolic pressure ≥ 130mmHg, there was a decrease of 9.7mmHg following 15min of mindful breathing at 6 breaths per minute. When measured over several days, the resting systolic pressure decreased by an average of 4.3mmHg. Discussion: Mindful breathing for 15min a day, at a rate of 6 breaths per minute is effective in lowering blood pressure, and has both an immediate, and a short term effect (over several days). This large scale study demonstrates that device guided mindful breathing with a consumer wearable for 15min a day is effective in lowering blood pressure, and a helpful complement to the standard of care.

Smart Wound Monitoring and Healing Assessment System With Deep Learning Methods - A Systematic Review

This review describes a Deep Learning Smart Wound Monitoring (DL-SWM) system, focusing on the healing process of both acute and chronic skin wounds. Recognizing the impact of various factors such as environment, patient characteristics, and wound features on the healing timeline, the review emphasizes the need for more efficient wound monitoring methods. The proposed DL-SWM integrates biosensors, a microcontroller, and a fuzzy inference system to assess critical wound indicators, primarily focusing on hydration levels. The hardware design incorporates an Arduino-based biometric sensor device, while the fuzzy inference system predicts the impact of biomarkers on wound hydration. The review study also explores the segmentation of wounds using a Convolutional Neural Network (CNN) called MobileNetV2, providing detailed insights into the wound healing stages. In the literature review, various advancements in wound monitoring technologies, such as hydrogels, clinical decision-making systems, and wearable biological sensors, are discussed. The proposed DL-SWM is compared with existing methods through simulation analysis, demonstrating superior efficiency, accuracy, and lower error rates. The study concludes with the potential prospects of DL-SWM in revolutionizing wound monitoring and treatment, offering a more convenient and effective approach for healthcare practitioners and improving patient outcomes.

Low Power Hydroelectric Systems (Lphs) To Energize Iot Devices

Introduction: This paper describes the results of the exploratory research developed by the SciBas investigation group of Francisco José de Caldas Distrital University within the low power-Pico hydraulic Hydroelectric Generation Systems (0,5 Kw to 5Kw) framework, that uses the minimum water reservoirs or Ultra Low Head (ULH) or Very Low Head (VLH) - such as drainage channels – and provides energy to IoT devices or its applications. Problem: About 80% of the world’s population lives in places where energy depends on fossil fuels; thus, it is necessary to generate electric power with renewable sources. Nevertheless, Low Power Generation Systems are an under-researched field, so the applied experiences have not been categorized and documented enough. Objective: Formulate a baseline to provide a detailed understanding of the structure of Low Power Hydroelectric Systems (LPHS) and establish a perspective for future Colombian context research. Methodology: Construct conceptualizations, categorizations and sub-categorizations based on vertebrate diagrams oriented to conceptual maps that show documentary references included between the years 2019 and 2023. Results: A review of papers related to Hydroelectric Energy Generation was made, of which 30% consisted of research about LPHS and its advantages and disadvantages; 35% about the generators, batteries and converters that complement this kind of system; and, finally, IoT device applications and sensor networks comprise 35% of the investigation. Conclusion: It was established that, in addition to the fact that LPHS generation is propitious for IoT devices and its applications, the idea of designing and implementing systems that power sensors and devices through clean energy is an advantage over other forms of generation with resources that, although exhaustible, are easily found within the national geography and are therefore viable for their implementation in non-interconnected zones of the electric system. Originality: This research is made in drainage channels terms, confirming its unprecedented nature; as well as considering energizing of IoT sensors and sensors networks by LPHS. Limitations: The review is limited to academic records; therefore, a deepening fieldwork is required for local, regional and national experience documentation in order to expand the applied research baseline.

Using an automated tail movement sensor device to predict calving time in dairy cows

This study aimed to evaluate the effectiveness of an automated tail movement sensor device (Moocall; Bluebell, Dublin, Ireland) to predict time of calving in dairy cows. At a commercial dairy farm in southern Ontario, Moocall (MC) devices were attached with the device's strap, and an additional elastic wrap, to the tail of cows approximately 3 d before their expected calving date. The MC has 2 types of alarm, a high activity alarm in the previous hour (1HA), and a high activity alarm in the previous 2 h (2HA); these alarms were sent and registered to the MC software. The calving and close-up pens were video monitored to determine the exact time of the onset of stage II of calving (amniotic sac visible at the vulva) and the end of stage II of calving (total expulsion of the calf). A total of 49 cows were enrolled, but we excluded 13 animals from the analysis as they had 3 or more MC drops from the tail (n = 6), a swollen tail (n = 3), or the MC device was lost (n = 4); this left 36 cows. In total, the device dropped off 21 (42%) cows. The average number of alarms (1HA and 2HA) per cow before stage II of calving was 2.7 ± 2.3 (± standard error). The first alarm after fitting the device on the tail was used to determine the device's sensitivity and specificity. Depending on the interval before the onset of parturition (i.e., 2, 4, 8, 12 h) in which the alarm was triggered, sensitivity varied from 5% to 72% and specificity from 50% to 93%. The false positive rate varied between 6% and 50% depending on the interval from the alarm to the onset of parturition. The high false positive and device drop rates (despite the addition of the elastic wrap) may compromise the applicability of this sensor device in a commercial setting.

Pressure sensor based on Hetero-stack L-shape TFET: simulation study

In this paper, the first-time-ever pressure sensor based on Hetero-stack L-shape TFET has been proposed and investigated through the SILVACO ATLAS TCAD tool. Owing to the hetero-stacking of Germanium material and Silicon material, an enhanced Ion and reduced sub-threshold swing is obtained by the proposed device leading to enhanced Pressure sensor sensitivity. The basic working principle of this proposed pressure sensor device is when pressure is applied to the diaphragm, the diaphragm bends which varies the metal-dielectric thickness of the oxide layer which further alters the electrical characteristics of the proposed pressure sensor. The diaphragm bendings considered in the study are 0, 1, 2, and 2.5 nm. The performance of the proposed pressure sensor device is analyzed through different electrical performance characteristics such as energy band diagrams, electric field, potential, Ion, sub-threshold swing, electron band-to-band tunneling rate, and various other analog/RF parameters at different diaphragm bendings. The effects of ITCs at various bendings are also analyzed to provide an in-depth effect of the factors that might affect the performance of the sensor. The proposed sensor design will be proven as a milestone in the array of sensors built for ultra-sensitive pressure sensing applications.

Imaging privacy threats from an ambient light sensor.

Embedded sensors in smart devices pose privacy risks, often unintentionally leaking user information. We investigate how combining an ambient light sensor with a device display can capture an image of touch interaction without a camera. By displaying a known video sequence, we use the light sensor to capture reflected light intensity variations partially blocked by the touching hand, formulating an inverse problem similar to single-pixel imaging. Because of the sensors' heavy quantization and low sensitivity, we propose an inversion algorithm involving an ℓp-norm dequantizer and a deep denoiser as natural image priors, to reconstruct images from the screen's perspective. We demonstrate touch interactions and eavesdropping hand gestures on an off-the-shelf Android tablet. Despite limitations in resolution and speed, we aim to raise awareness of potential security/privacy threats induced by the combination of passive and active components in smart devices and promote the development of ways to mitigate them.