Contactless Sensor Research Articles

A practical key recovery attack on the lightweight WG-5 stream cipher

WG-5 is a lightweight stream cipher proposed for usage in the resource-constrained devices, e.g., passive RFID tags, industrial controllers, contactless smart cards and sensors. In this paper, a weakness called slide property of WG-5 which has not been discovered in previous works is for the first time explored and analyzed. The result shows that the probability that two related key-IV pairs of WG-5 generate the shifted keystreams can be up to 2−20, which is significantly high compared with an ideal stream cipher that generates the random keystreams. The correctness and accuracy of this theoretical probability is confirmed experimentally. Based on the slide property of WG-5, some key recovery attacks on WG-5 in the related key setting are proposed. The cryptanalytic result shows that the 80-bit secret key of WG-5 can be recovered with a time complexity of 225.615, requiring 6 related keys and 80 keystream bits for each of 224.585 chosen IVs. The experimental result validates our attack and shows that WG-5 can be broken within about 92.054 seconds on a common PC in the related key setting. These results imply that the design of WG-5 is far from optimal and needs to be strengthened to provide enough security for the lightweight constrained applications.

Improvement of environmental monitoring systems for water resources through the application of contactless electrochemical impedance sensors

Improvement of environmental monitoring systems for water resources through the application of contactless electrochemical impedance sensors

High-Speed Reader for Contactless Resonant Surface Acoustic Wave Sensors

High-Speed Reader for Contactless Resonant Surface Acoustic Wave Sensors

Novel contactless torque sensor based on optical coherence

This work proposes a novel contactless sensor to measure the micro torque of a rotating shaft based on the optical coherence displacement measurement (OCDM) principle. The experimental system features a simple mechanical testing system to measure the torque, a calibration device to establish the measurement baseline, and a polyetheretherketone (PEEK) shaft to improve the theoretical resolution of the system up to 0.003 Nm. The torque measurement results indicate that this sensor achieved a maximum nonlinearity error of 0.0484 %, maximum repeatability error of 0.2771 %, and maximum hysteresis error of 0.2252 %. This high accuracy of our system stems from the use of high-precision optical principles and the novel physical mapping of the torque (i.e., processing scales on the shaft surface to represent the twist angle by designing a laser engraving system) capable of resisting the negative effects of radial vibrations. Additionally, our torque measurement sensor possesses broader applicability by effectively resisting the impacts of temperature variations and electromagnetic interference. These attributes make it excel other torque sensors presented in the literature in recent years.

Improving Sensor Interoperability between Contactless and Contact-Based Fingerprints Using Pose Correction and Unwarping

Current fingerprint identification systems face significant challenges in achieving interoperability between contact-based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact-based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger-wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high-quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains.

Study on Various Position Sensing Technologies in Tractor Hitch Application

Conventionally, In the tractors, hitch position is obtained using a contact-based position sensor, the sensor is attached to the rockshaft mechanically with gears or linkages. In this arrangement, there are incidences of failures when working with heavy implements, as rockshaft is under severe loading conditions which tends to induce cracks or failures in sensor mechanical linkages mounting area. This results in machine downtime and rockshaft repair costs to the customer. These mounting threads in hardened Rockshaft reduce material strength which impacts the lifting capacity. There is associated difficulty at assembly line and further during maintenance for linkage installation and adjustments. Also, over the period, linkages develop a play at joints due to the friction. In the present work, we are sensing the Hitch position with contactless sensor technology. A contactless Hall effect sensor is selected, and proto setup has been designed to measure the hitch position by integrating the sensor and target. The prototype consists of a fixed Sensor and a rotating metal (ferromagnetic) piece placed inside a 3D printed casing. The profile of metal piece is such that when it is rotated about the axis, the distance between Sensor and metal piece changes gradually. This causes different output voltage at different amount of rotation in metal piece. Results from manual bench testing show that the concept is working. The sensor voltage output varies according to the hitch position in the bench setup. A high voltage value when metal piece is nearer to magnet and low value when its farther. A future step is to automate this setup on a tractor to remove errors due to manual rotation. This would be an enabler to analyze the sensor behaviors like hysteresis, repeatability, linearity more accurately.

Exploring acceptance of monitoring technology in home‐based dementia care: A survey study among informal carers

AbstractBackgroundThere is growing interest to support home‐based dementia care via contactless monitoring technologies which do not require any body contact and allow informal carers to remotely monitor health and safety of people with dementia (PwD). However, sustainable implementation of such technologies requires a better understanding of informal carers’ acceptance. This study therefore aimed to explore 1) acceptance towards different use scenarios, 2) acceptance towards different contactless sensor types, and 3) differences between accepters and refusers of contactless monitoring technology for home‐based dementia care.MethodA cross‐sectional online survey was conducted among n = 304 informal carers of community‐dwelling PwD (Mage = 58.5±10.7; 71% female) in the Netherlands and Germany. Questions targeted informal carers’ general acceptance towards contactless monitoring technology, their acceptance towards 5 different use scenarios, their acceptance towards 7 different contactless sensor types, as well as their own and their care recipient’s personal characteristics. Data were examined using descriptive and bivariate analyses.ResultAcceptance towards different use scenarios for contactless monitoring technology varied significantly (p<.001). The intention to use contactless monitoring technology was highest for detecting emergency situations (e.g., falls, wandering), and lowest for predicting acute situations (e.g., fall prediction). Acceptance towards different contactless sensor types varied significantly as well (p<.001). RF‐based sensors (e.g radar) and light sensors were considered most acceptable, whereas camera‐based sensors and audio sensors were seen as least acceptable for home‐based dementia care. Lastly, accepters and refusers of contactless monitoring technology significantly differed regarding gender (p = .010), their relation with the PwD (p = .003), eHealth literacy (p = .025), personal innovativeness (p<.001), experience with safety technology (GPS trackers, alarm buttons) (p = .002), and the PwD’s type of cognitive impairment (p = .035) and housing situation (p = .023).ConclusionOur findings can help to make better‐informed decisions during development and implementation of contactless monitoring technology to support home‐based dementia care, thereby increasing the chance of achieving a fit between technology, user, and use context.

Exploring acceptance of monitoring technology in home‐based dementia care: A survey study among informal carers

AbstractBackgroundThere is growing interest to support home‐based dementia care via contactless monitoring technologies which do not require any body contact and allow informal carers to remotely monitor health and safety of people with dementia (PwD). However, sustainable implementation of such technologies requires a better understanding of informal carers’ acceptance. This study therefore aimed to explore 1) acceptance towards different use scenarios, 2) acceptance towards different contactless sensor types, and 3) differences between accepters and refusers of contactless monitoring technology for home‐based dementia care.MethodA cross‐sectional online survey was conducted among n = 304 informal carers of community‐dwelling PwD (Mage = 58.5±10.7; 71% female) in the Netherlands and Germany. Questions targeted informal carers’ general acceptance towards contactless monitoring technology, their acceptance towards 5 different use scenarios, their acceptance towards 7 different contactless sensor types, as well as their own and their care recipient’s personal characteristics. Data were examined using descriptive and bivariate analyses.ResultAcceptance towards different use scenarios for contactless monitoring technology varied significantly (p<.001). The intention to use contactless monitoring technology was highest for detecting emergency situations (e.g., falls, wandering), and lowest for predicting acute situations (e.g., fall prediction). Acceptance towards different contactless sensor types varied significantly as well (p<.001). RF‐based sensors (e.g radar) and light sensors were considered most acceptable, whereas camera‐based sensors and audio sensors were seen as least acceptable for home‐based dementia care. Lastly, accepters and refusers of contactless monitoring technology significantly differed regarding gender (p = .010), their relation with the PwD (p = .003), eHealth literacy (p = .025), personal innovativeness (p<.001), experience with safety technology (GPS trackers, alarm buttons) (p = .002), and the PwD’s type of cognitive impairment (p = .035) and housing situation (p = .023).ConclusionOur findings can help to make better‐informed decisions during development and implementation of contactless monitoring technology to support home‐based dementia care, thereby increasing the chance of achieving a fit between technology, user, and use context.

Contactless sensor for real-time monitoring of lithium battery external short circuit based on magnetoelectric elastomer composites

The frequent accident due to external short circuit of lithium-ion batteries in electric vehicles have prompted people to develop sensing technology to achieve early warning. However, the limited space and high temperature in highly integrated lithium-ion battery packs present significant challenges for current sensors to achieve external short circuit monitoring with high structural flexibility and heat resistance. In this study, a piezoelectric elastomer is designed and blended with magnetostrictive ferric tetroxide to prepare the LMPE/Fe3O4 magnetoelectric current sensor (LMPE/Fe3O4 MCS). The amorphous structure and low modulus of the piezoelectric elastomer enable the LMPE/Fe3O4 MCS to exhibit large deformability over 50 % elongation, and the non-ferroelectric LMPE and ferric tetroxide with high Curie temperature offer the LMPE/Fe3O4 MCS with high heat resistance over 150 °C. Meanwhile, the excellent interfacial interaction between the piezoelectric elastomer and the ferric tetroxide eliminates stress transfer loss between the interface, presenting outstanding magnetoelectric response and excellent linearity (>0.99) with current sensitivity of 5.975 mV A−1. The LMPE/Fe3O4 MCS can achieve real-time monitoring and early warning for external vibration and short circuit during the charging and discharging process of lithium batteries, which shows promising solutions for the safety improvement of electric vehicles.

Understanding acceptance of contactless monitoring technology in home-based dementia care: a cross-sectional survey study among informal caregivers.

There is a growing interest to support home-based dementia care via contactless monitoring (CM) technologies which do not require any body contact, and allow informal caregivers to remotely monitor the health and safety of people with dementia (PwD). However, sustainable implementation of CM technologies requires a better understanding of informal caregivers' acceptance. This study aimed to examine the (1) general acceptance of CM technology for home-based dementia care, (2) acceptance of different sensor types and use scenarios, and (3) differences between accepters and refusers of CM technology. A cross-sectional online survey was conducted among n = 304 informal caregivers of community-dwelling PwD [Mean(SD) age = 58.5 (10.7)] in the Netherlands and Germany. The survey contained a textual and graphical introduction to CM technologies, as well as questions targeting (1) general acceptance of CM technology, (2) acceptance of seven different contactless sensor types, (3) acceptance of five different use scenarios, and (4) caregivers' own and their care recipients' personal characteristics. Data were examined using descriptive and bivariate analyses. Participants' general acceptance of CM technology was slightly positive. We found significant differences in acceptability between contactless sensor types (p < .001). RF-based sensors (e.g., radar) and light sensors were considered most acceptable, whereas camera-based sensors and audio sensors (e.g., microphones, smart speakers) were seen as least acceptable for home-based dementia care. Furthermore, participants' acceptance of different use scenarios for CM technology varied significantly (p < .001). The intention to use CM technology was highest for detecting emergencies (e.g., falls, wandering), and lowest for predicting acute situations (e.g., fall prediction). Lastly, accepters and refusers of CM technology significantly differed regarding gender (p = .010), their relation with the PwD (p = .003), eHealth literacy (p = .025), personal innovativeness (p < .001), usage of safety technology (p = .002), and the PwD's type of cognitive impairment (p = .035) and housing situation (p = .023). Our findings can inform the development and implementation of acceptable CM technology to support home-based dementia care. Specifically, we show which sensor types and use scenarios should be prioritized from the informal caregiver's view. Additionally, our study highlights several personal characteristics associated with informal caregivers' acceptance of CM technology that should be taken into account during implementation.

Design and Development of a Bioreactor System for Mechanical Stimulation of Musculoskeletal Tissue

Abstract We report on the development of a bioreactor system for mechanical stimulation of musculoskeletal tissues. The ultimate object is to improve the quality of medical treatment following injuries of the enthesis tissue. To this end, the tissue formation process through the effect of mechanical stimulation is investigated. A six-well system was designed, 3D printed and tested. An integrated actuator creates strain by applying a force. A contactless position sensor monitors the travels. An electronic circuit controls the bioreactor using a microcontroller. An IoT platform connects the microcontroller to a smartphone, enabling the user to alter variables, trigger actions and monitor the system. The system was stabilised by implementing two PID controllers and safety measures. The results show that the bioreactor design is suited to execute mechanical stimulation and to investigate the tissue formation and regeneration process. The bioreactor reported here can now be implemented in tissue engineering applications including tissue specimen.

Sleep-wake monitoring of people with intellectual disability: Examining the agreement of EMFIT QS and actigraphy.

Gaining insight into sleep-wake patterns of persons with intellectual disabilities is commonly done using wrist actigraphy. For some people, contactless alternatives are needed. This study compares a contactless bed sensor with wrist actigraphy to monitor sleep-wake patterns of people with moderate to profound intellectual disabilities. Data were collected with EMFIT QS (activity and presence) and MotionWatch 8/Actiwatch 2 (activity, ambient light, and event marker/sleep diary) for 14 nights in 13 adults with moderate-profound intellectual disabilities residing in intramural care. In a care-as-usual setting, EMFIT QS and actigraphy assessment show little agreement on sleep-wake variables. Currently, EMFIT QS should not be considered an alternative to wrist actigraphy for sleep-wake monitoring. Further research is needed into assessing sleep-wake variables using (contactless) technological devices and how the data should be interpreted within the care context to achieve reliable and valid information on sleep-wake patterns of people with intellectual disabilities.

Subspace-based modal identification and uncertainty quantification from video image flows

Vibration measurements for structural health monitoring (SHM) by operational modal analysis (OMA) are classically obtained from sensors that are embedded in or physically attached to the monitored structure, like accelerometers or strain gauges. However, the setup time of these sensors and their restricted number and space coverage limit their monitoring capabilities. Video image-based sensing methods can overcome these shortcomings. With adequate image processing methods, motion signals are extracted from video image flows, which are then processed by system identification methods to estimate modal parameters. In this way, the pixels in selected regions of interest within the images act as a dense network of contactless sensors distributed over the whole structure. In this paper, the efficiency of this video-based approach is demonstrated with laboratory experiments on a cantilever beam, in particular, by evaluating its capability for detecting weak damages mimicked by slight mass modifications. To this end, the steerable filter-based method (ST), that recovers displacements from local phase, is first extended to overcome its motion limitation of one pixel size. Then, the performance of the improved motion extraction method is compared with two other well-established methods in the context of OMA, where natural frequencies, damping ratios and mode shapes with high spatial resolution are estimated together with their uncertainty bounds using covariance-driven subspace identification. The compared methods are evaluated with the help of reference laser displacement measurements as well as a finite element model of the beam, revealing differences in the accuracy of the estimated mode shapes depending on the chosen method for motion extraction. Finally, aiming to investigate early structural damage detection, experiments are carried out under small structural changes and the results are compared to a reference state with the help of estimated uncertainties. Small but statistically significant changes in the modal parameters are detected, showing the potential of the vision based framework for SHM.

Noninvasive Cuffless Blood Pressure Monitoring. How Mechanism-Driven and Data-Driven Models Can Help in Clinical Practice

Continuous noninvasive cuffless blood pressure (BP) monitoring is essential for early detection and treatment of hypertension. In this paper, we provide an overview of the recent advancements in cuffless BP sensors. These include contact wearable sensors such as electrocardiography (ECG), photoplethysmography (PPG), contact non-wearable sensors such as ballistocardiography (BCG), and contactless sensors such as video plethysmography (VPG). These sensors employ different measuring mechanisms such as pulse arrival time (PAT), pulse transit time (PTT), and pulse wave analysis (PWA) to estimate BP. However, challenges exist in the effective use and interpretation of signal features to obtain clinically reliable BP measurements. The correlations between signal features and BP are obtained by mechanism-driven models which use physiological principles to identify mathematical correlations, and data-driven models which use machine learning algorithms to analyze observational data to identify multidimensional correlations. On the one hand, applying mechanism-driven models to non-linear scenarios and incomplete or noisy data is challenging On the other hand, data-driven models require a large amount of data in order to prevent physically inconsistent predictions, resulting in poor generalization. From this perspective, this paper proposes to combine the strengths of mechanism-driven and data-driven approaches to obtain a more comprehensive approach, the physiology-informed machine-learning approach, with the goal of enhancing the accuracy, interpretability, and scalability of continuous cuffless BP monitoring. This holds promise for personalized clinical applications and the advancement of hypertension management.

Contactless-Sensor-Based Output Feedback Control With Maximum Efficiency Tracking Technique in Inductive Power Transfer Systems

To maximize the overall efficiency of a series-series inductive power transfer (SSIPT) system, the maximum efficiency tracking (MET) control should be employed. Based on a contactless current sensor which is designed for the mutual inductance estimation and the output voltage feedback, this paper proposes a novel MET control method for the SSIPT system with front-side and load-side converters. In this method, the output voltage of the rectifier on the load side is estimated and used as a control reference on the front side. Moreover, this paper analyzes the small-signal model of the SSIPT system and presents an impedance-based stability criterion. The factors which influence output feedback control are investigated, and a comprehensive control strategy based on the impedance stability criterion is developed. Finally, a 600 W prototype is built to validate the proposed method. MET control is implemented in a wide range of load variations, with a maximum efficiency drop of approximately 2.3% over a 100 mm misalignment. The results show that the proposed scheme significantly improves the robustness and response performance of the SSIPT system.

Millimetric Inclusion Detection Through a Contactless Microwave Spiral Sensor for Biomedical Applications

This article introduces the design and fabrication of a microwave contactless sensor used to detect the presence of millimetric inclusions in a biological medium for biomedical applications. The hardware system comprises a self-resonant planar spiral resonator (SR) (sensing element) inductively coupled to an external concentric single-loop probe (reading probe), working at 648 MHz. The microwave sensor configuration relies on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -factor maximization of the spiral coil, that is, the sensing element, through an optimization process, to obtain a stronger sensitivity and, thus, a millimeter resolution for the inclusions’ detection. The detection is achieved by recording both the amplitude variation and the frequency shift of the input impedance of the reading probe. To validate the proposed solution, full-wave simulations have been performed to design and preliminary evaluate the radiating system performance in detecting millimetric biological inclusions. As an applicative example, we focus on air bubbles detection in hemodialysis procedures; in particular, we carried out the experimental verification by employing an agar phantom to replicate the dielectric characteristics of the blood tissue and polylactic acid (PLA) samples of various sizes to represent the air inclusions. We proved that the theoretical assumptions were in excellent agreement with both the numerical and experimental results, encouraging further analysis of the potential use of such sensors in biomedical applications. Indeed, the radiating device can be very helpful for all operations where it is necessary to detect the presence of undesired and dangerous contaminants in a contactless way, making the procedure safer for patients.

Resonator Arrays for Linear Position Sensors

A contactless position sensor based on an array of magnetically coupled resonators and an external single coil cell is discussed for both stationary and dynamic applications. The simple structure allows the sensor to be adapted to the system in which it is installed and can be used to detect the positions of objects in motion that bear an external resonator coil that does not necessitate a supply. By exploiting the unique behaviour of the array input impedance, it is possible to identify the position of the external resonator by exciting the first array cell with an external voltage source and measuring the resulting input current. The system is robust and suitable for application in harsh environments. The sensitivity of the measured input impedance to the space variation is adjustable with the definition of the array geometry and is analysed. Different configurations of the array and external resonator are considered, and the effects of various termination conditions and the resulting factor of merit after changing the coil resistance are discussed. The proposed procedure is numerically validated for an array of ten identical magnetically coupled resonators with 15 cm side lengths. Simulations carried out for a distance of up to 20 cm show that, with a quality factor lower than 100 and optimal terminations of both the array and external coil, it is possible to detect the position of the latter.

Multimodal integrated flexible electronic skin for physiological perception and contactless kinematics pattern recognition

Multimodal integrated flexible electronic skin offers exceptional processing capability and versatility in various application scenarios. Researchers are devoting to developing nanomaterials or nano-devices with multidisciplinary functions. Herein, a trinary-mode integrated flexible electronic skin (E-Skin) was designed for physiological perception and contactless kinematics pattern recognition, which serves as real-time health monitor, electrode patch and contactless sensor. The composite film of liquid metal particles and thermoplastic polyurethane elastomers (LMPs@TPU) was prepared by a combination of electrostatic spinning, electrostatic spraying, and knife coating, which can be stretched up to 404.7 % at stress of 1Mpa. The film was endowed three functional modes through different forms of encapsulation, which allows for (1) detecting human arthrosis movements, muscle stretching, and inspiration, (2) achieving a real-time capture of Electrocardiogram (ECG) and surface electromyography (sEMG), and a Human-Machine Interaction (HMI) system for game control based on sEMG signals was also developed, (3) a contactless sensor combining with a machine learning model which realizes the recognition of human motion patterns with a receiver operating characteristic curve and area under curve score (ROC-AUC-score) of 0.99. The three models of this E-Skin highlight the significant potential for industrialization in various fields, such as HMI, physiological perception, virtual reality, and motion analysis.

Bifurcation scenarios in the hardware-in-the-loop experiments of highly interrupted milling processes

Hardware-in-the-loop (HIL) measurements of highly interrupted milling processes were conducted. A real spindle was used with a dummy tool on which the cutting forces were emulated with contactless sensors and actuators. During the experiments, Hopf- and period-doubling bifurcations were identified. The nonlinear dynamics of these period-doubling bifurcations are analyzed for a discrete model of highly interrupted milling. This investigation found the bifurcation to be subcritical, which draws the attention to the limited practical validity of linear stability analysis.

Vision-based activity recognition in children with autism-related behaviors

Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems have been introduced to enable comprehensive analysis of neuro-developmental conditions such as Autism Spectrum Disorder (ASD). This condition affects children from their early developmental stages onwards, and diagnosis relies entirely on observing the child's behavior and detecting behavioral cues. However, the diagnosis process is time-consuming as it requires long-term behavior observation, and the scarce availability of specialists. We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. For this purpose, we adopt and enhance a dataset for analyzing autism-related actions using videos of children captured in uncontrolled environments (e.g. videos collected with consumer-grade cameras, in varied environments). The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames and classifying autism-related behaviors by analyzing the relationships between frames in a video. By extensively evaluating feature extraction and learning strategies, we demonstrate that the highest performance is attained through the use of an Inflated 3D Convnet and Multi-Stage Temporal Convolutional Network. Our model achieved a Weighted F1-score of 0.83 for the classification of the three autism-related actions. We also propose a light-weight solution by employing the ESNet backbone with the same action recognition model, achieving a competitive 0.71 Weighted F1-score, and enabling potential deployment on embedded systems. Experimental results demonstrate the ability of our proposed models to recognize autism-related actions from videos captured in an uncontrolled environment, and thus can assist clinicians in analyzing ASD.