Contact Sensors Research Articles

Multi-channel non-destructive testing of steel strip stress based on magneto-elastic effect

ABSTRACT Internal stress is an important factor that affects the quality of strip products. Most online detection methods for internal strip stress have been realised by contact sensors. However, contact sensors are expensive to manufacture and tend to scratch the strip surface. The purpose of this study was to develop a multi-channel non-contact stress detection system that can reflect the longitudinal stress inhomogeneity of the strip in the width direction. First, a finite element model of residual stress detection based on the magneto-elastic effect was established in COMSOL, and it was calculated that the detection sensitivity was optimal when the sensor was arranged at 45°. Magnetic shielding technology effectively reduces the minimum interference-free distance of the sensor. A prototype of the detection system was built, the accuracy of the simulation model and the feasibility of the online detection of common steel grades are proved by experiments. The simulation model can directly solve issues related to the sensor’s induced voltage and the minimum interference-free distance, thereby reducing the design cycle and trial production cost of the sensor and providing new ideas for the development of magnetic shielding technology. The experimental results demonstrated that the detection method was feasible, reliable, and displayed high detection precision.

Use It-No Need to Shake It!

Implicit authentication for traditional objects, such as doors and dumbbells, has rich applications but is rarely studied. An ongoing trend is that traditional objects are retrofitted to smart environments; for instance, a contact sensor is attached to a door to detect door opening (but cannot tell "who is opening the door"). We present the first accurate implicit-authentication system for retrofitted everyday objects, named MoMatch. It makes an authentication decision based on a single natural object use, unlike prior work that requires shaking objects. MoMatch is built on the observation that an object has a motion typically because a human hand moves it; thus, the object's motion and the legitimate user's hand movement should correlate. The main challenge is, given the small amount of data collected during one object use, how to measure the correlation accurately. We convert the correlation measurement problem into an image comparison problem and resolve it using neural networks successfully. MoMatch does not need to profile the user's biometric information and is resilient to mimicry attacks.

Influence of the Experimental Setup on the Damping Properties of SLM Lattice Structures

BackgroundMetal lattice structures obtained through Selective Laser Melting may increase the strength-to-weight ratio of advanced 3D printed parts, as well as their damping properties. Recent experimental results showed that AlSi10Mg and AISI 316L lattices are characterized by higher Rayleigh damping coefficients with respect to the fully dense material. However, some unclear or contradictory results were found, depending on the experimental setup adopted for modal analysis.ObjectiveIn this work the influence of the experimental setup when performing modal analysis on different SLM AISI 316L lattice structures was deeply investigated. The study provides a critical comparison of various experimental modal analysis approaches, allowing to evaluate the influence of external damping sources and material internal damping phenomena.MethodsThe dynamic behaviour of SLM AISI 316L specimens incorporating lattice structures was estimated by means of pulse testing and sinusoidal excitation through an electromagnetic shaker. The validity of the viscous damping model was assessed by means of sinusoidal excitation with different levels of vibration velocity. Moreover, the influence of experimental setup on modal analysis results was critically evaluated, by considering different actuators, contact and non-contact sensors and boundary/clamping conditions.ResultsThe classical viscous damping model describes with good approximation the damping properties of SLM lattice structures. When exciting single specimens in free-free conditions, those embedding lattice structure and unmelted metal powder filler were characterized by superior internal damping properties with respect to the specimens incorporating the lattice structure without any filler, which was however more effective than the full density equivalent material. Most of the other experimental setups introduced additional external damping sources, that could alter this important outcome.ConclusionsSLM lattice structures embedded into 3D printed components provide superior damping properties against mechanical and acoustic vibrations and the metal powder filler does significantly enhance such damping capacity. A correct estimation of material internal damping was achieved by applying non-contact sensors and free-free boundary conditions, whereas other experimental setups were partly inadequate.

Project-based learning through sensor characterization in a musical acoustics course.

Project-based learning engages students in practical activities related to course content and has been demonstrated to improve academic performance. Due to its reported benefits, this form of active learning was incorporated with an ongoing research project into an introductory, graduate-level Musical Acoustics course at the Peabody Institute of The Johns Hopkins University. Students applied concepts from the course to characterize a contact sensor with a polymer diaphragm for musical instrument recording. Assignments throughout the semester introduced students to completing a literature review, planning an experiment, collecting and analyzing data, and presenting results. While students were given broad goals to understand the performance of the contact sensor compared to traditional microphones, they were allowed independence in determining the specific methods used. The efficacy of the course framework and research project was assessed with student feedback provided through open-ended prompts and Likert-type survey questions. Overall, the students responded positively to the project-based learning and demonstrated mastery of the course learning objectives. The work provides a possible framework for instructors considering using project-based learning through research in their own course designs.

In-Home Monitoring Technology for Aging in Place: Scoping Review.

BackgroundFor successful aging-in-place strategy development, in-home monitoring technology is necessary as a new home modification strategy. Monitoring an older adult’s daily physical activity at home can positively impact their health and well-being by providing valuable information about functional, cognitive, and social health status. However, it is questionable how these in-home monitoring technologies have changed the traditional residential environment. A comprehensive review of existing research findings should be utilized to characterize recent relative technologies and to inform design considerations.ObjectiveThe main purpose of this study was to classify recent smart home technologies that monitor older adults’ health and to architecturally describe these technologies as they are used in older adults’ homes.MethodsThe scoping review method was employed to identify key characteristics of in-home monitoring technologies for older adults. In June 2021, four databases, including Web of Science, IEEE Xplore, ACM Digital Library, and Scopus, were searched for peer-reviewed articles pertaining to smart home technologies used to monitor older adults’ health in their homes. We used two search strings to retrieve articles: types of technology and types of users. For the title, abstract, and full-text screening, the inclusion criteria were original and peer-reviewed research written in English, and research on monitoring, detecting, recognizing, analyzing, or tracking human physical, emotional, and social behavior. The exclusion criteria included theoretical, conceptual, or review papers; studies on wearable systems; and qualitative research.ResultsThis scoping review identified 30 studies published between June 2016 and 2021 providing overviews of in-home monitoring technologies, including (1) features of smart home technologies and (2) sensor locations and sensor data. First, we found six functions of in-home monitoring technology among the reviewed papers: daily activities, abnormal behaviors, cognitive impairment, falls, indoor person positioning, and sleep quality. Most of the research (n=27 articles) focused on functional monitoring and analysis, such as activities of daily living, instrumental activities of daily living, or falls among older adults; a few studies (n=3) covered social interaction monitoring. Second, this scoping review also found 16 types of sensor technologies. The most common data types encountered were passive infrared motion sensors (n=21) and contact sensors (n=19), which were used to monitor human behaviors such as bodily presence and time spent on activities. Specific locations for each sensor were also identified.ConclusionsThis wide-ranging synthesis demonstrates that in-home monitoring technologies within older adults’ homes play an essential role in aging in place, in that the technology monitors older adults’ daily activities and identifies various health-related issues. This research provides a key summarization of in-home monitoring technologies that can be applied in senior housing for successful aging in place. These findings will be significant when developing home modification strategies or new senior housing.

Development of a portable measuring system using ultrasound guided and surface waves to characterize concrete structures

A portable system was developed to be used in lab and on field. The system is composed of one emitter and 60 receivers. All of them are Dry Point Contact sensors, P-wave, S1803 from ACSYS, with a nominal frequency of 100kHz and a wide frequency range from 15kHz to 175kHz. The 60 receivers are equally spaced of 5mm which enables to make measurements over 30cm. The acquisition is made with a two-channels A1560 ACSYS device. We developed an electronic part to multiplex the acquisition of all the receiver signals. A Raspberry-Pi device is used to control four 16-channels analogic multiplexers. We designed and manufactured with a 3D-printer a holder for the DPC sensors and the electronic part. The A1560 device and the Raspberry-pi are controlled with a Python program on a digital tablet. For one acquisition, which lasts a few seconds, 60 a-scans are registered. Several acquisitions are repeated by moving the system of a few centimeters. The results are averaged to lower the incoherent part of the signals due to multi-scattering effects on concrete aggregates. The result is processed with a p-w Transform to get the dispersion diagram and to assess the dispersion curve. In the case of surface waves, when the structure thickness is larger than the smallest wavelength, the dispersion curve can be inversed with a tool like Dinver from Geopsy. The final result is the ultrasound speed profile with depth. In the case of Lamb waves, the dispersion curve is compared to A0 and S0 modes calculated with a tool like Disperse form Imperial College or CIVA frome CEA. This efficient tool is used to study different concrete structures: - thick structure with concrete cover affected by carbonation or leaching, - thin structure like 6cm-thickness and 60cm-diameter concrete/steel/concrete pipes used in the nuclear industry.

Non-Contact Heart Rate Monitoring Using Machine Learning

Abstract: Recording and observing crucial signs is a fundamental part of locally established medical care. Utilizing contact sensors to record physiological signs can make uneasiness patients, particularly after delayed use. At the point when a subject is moving, the face might be gotten some distance from the camera. We use numerous cameras to empower the calculation to screen the crucial sign persistently, regardless of whether the subject leaves the edge or gets some distance from a subset of the framework's cameras. Besides, we work on the exactness of existing strategies by executing a light evening out plan to decrease the impact of shadows and inconsistent facial light on the HR assessment, an AI strategy to choose the most reliable channel yielded by the ICA module, and a relapse method to change the underlying HR gauge. The proposed strategy diminishes the RMSE by 27% contrasted with the cutting edge in the rest condition. At the point when the subject is moving, the proposed technique accomplishes a RMSE of 1.12 bpm.

A Review of Vision-Laser-Based Civil Infrastructure Inspection and Monitoring.

Structural health and construction security are important problems in civil engineering. Regular infrastructure inspection and monitoring methods are mostly performed manually. Early automatic structural health monitoring techniques were mostly based on contact sensors, which usually are difficult to maintain in complex infrastructure environments. Therefore, non-contact infrastructure inspection and monitoring techniques received increasing interest in recent years, and they are widely used in all aspects of infrastructure life, owing to their convenience and non-destructive properties. This paper provides an overview of vision-based inspection and vision–laser-based monitoring techniques and applications. The inspection part includes image-processing algorithms, object detection, and semantic segmentation. In particular, infrastructure monitoring involves not only visual technologies but also different fusion methods of vision and lasers. Furthermore, the most important challenges for future automatic non-contact inspections and monitoring are discussed and the paper correspondingly concludes with state-of-the-art algorithms and applications to resolve these challenges.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

BackgroundMeasuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it.MethodsIn this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation.ResultsBy analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved.ConclusionThe results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

Heat flow measurement of human skin using a calorimetric sensor with a programmable thermostat. An alternative to climate chambers

In this paper we discuss the advantages of using a calorimetric sensor with a programmable thermostat compared to using heat flux sensors without a thermostat. The aim of this new calorimetric sensor is to measure the heat loss of the skin accurately. The sensor’s measurement procedures and their experimental validation are shown. We also discuss the characteristics of the heat transfer phenomenon that takes place when a contact sensor is applied to the skin, emphasizing on the difficulties and opportunities that this type of measurement involves. This new thermostated calorimetric sensor is able to reproduce different temperature conditions on a localized skin region, providing a potential alternative to climate chambers. In addition, determining the heat flux for different thermostat temperatures allows the determination of an equivalent thermal conductance of the skin, that is directly related to its actual physiology. Finally, skin heat power is evaluated on different subjects and skin areas, and the results provided by our thermostated calorimetric sensor are compared with those provided by non-thermostated sensors.

Evaluating Visual Photoplethysmography Method.

Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related illnesses cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individual’s physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two heart-rate variability (HRV) features, NNi 20 and SAMPEN, were capable of differentiating between stress and non-stress states. In addition, three features, inter-beat interval (IBI), NNi 20, and SAMPEN, were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a "stressed" or "unstressed" state, the models were able to achieve an accuracy score of 83.11%.

Study of Alarm Threshold for Assumed Nasogastric Tube Self-removal Action Using a Contact Sensor System.

Physical restraints negatively impact the physical, mental, and social well-being of patients. However, elderly people with dementia are often physically restrained by preventing nasogastric tube (NG tube) self-removal. This study aimed to examine alarm threshold settings limited to the actions that the subject makes contact with the NG tube out of feeling uneasy for the NG tube (assumed self-removal actions) using a contact sensor system in order to achieve non-physical restraint. In this study, subjects with experience in caring for older people with dementia were required to perform an assumed self-removal action along with 15 kinds of non-self-removal actions, while we observed the contact detection and measured the voltage. The alarm thresholds for the assumed self-removal action were examined from the ROC curve using the voltage zero time and the number of times the voltage was up and down during each action as a variable. The system presented in this study allowed us to reduce the false alarm rate from 23.3% to 8.5%. Furthermore, we used this alarm threshold to verify the data of three new subjects, observing a false alarm rate of 0.0%. Our future research would focus on investigating and evaluating elderly with dementia using the alarm threshold of the assumed self-removal action that we examined. In addition, we would use ICT to link contact detection with the nurse call system. Clinical Relevance- In this study, we examined the alarm threshold limited to the expected self-removal action using a contact sensor system. We showed that it is possible to detect the NG tube self-removal with few false alarms.

Theoretical formulation/development of signal sampling with an equal arc length using the frame theorem

Nonuniform sampling with equal arc length intervals can be found in shape measurements with contact sensor arrays. In this study, the conditions of nonuniform spatial sampling with an equal arc length interval are derived from two frame theorems. First, for general nonuniform sampling, the condition is that the equal arc length interval of the sensors should be less than frac{1}{4Omega }. Second, for strictly increasing sampling (the sampling point set is strictly increasing), the condition is that the equal arc length interval of the sensors should be less than frac{1}{2Omega }. The Omega is the maximum frequency of the detected object. For the latter, if the sampling frequency is more than twice the sampling frequency required, the reconstruction error (RRMSE and MRE) is less than 5%. If the sampling frequency is more than 2.5 times, the reconstruction error is less than 3%. The simulation and the application test are carried out, and the results show that a sensor array with equal arc length interval can reconstruct the detected object with high accuracy.

Online Kinematic Calibration for Legged Robots

This paper describes an online method to calibrate certain kinematic parameters of legged robots, including leg lengths, that can be difficult to measure offline due to dynamic deformation effects and rolling contacts. A kinematic model of the robot’s legs that depends on these parameters is used, along with measurements from joint encoders, foot contact sensors, and aninertial measurement unit (IMU) to predict the robot’s body velocity. This predicted velocity is then compared to another velocity measurement from, for example, a camera or motion capture system, and the difference between them is used to compute anupdate on the kinematic parameters. The method can be incorporated into both Kalman filter or sliding-window optimization-based state estimator. We provide a theoretical observability analysis of our method, as well as validation both in simulation and on hardware. Hardware experiments demonstrate that online kinematic calibration can significantly reduce position drift when relying on odometry.

Evaluation of Field Applicability of High-Speed 3D Digital Image Correlation for Shock Vibration Measurement in Underground Mining

When combined with high-speed photography technology, the digital image correlation (DIC) method provides an excellent photographic image processing capability that can be used to convert the evolving full-field surface properties of structures to sets of two-dimensional (2D) or three-dimensional (3D) coordinate values. In this study, the applicability of the DIC method and high-speed cameras in underground mining was investigated by measuring the displacement and vibration of rock pillars caused by blasting. This technique is used extensively in engineering and is increasingly being applied to new fields. As a result of comparing the DIC results for blast vibration with the measured values of the contact sensor through field tests, the maximum displacement and vibration speed were found to be 86% and 93% accurate, respectively. In addition, the obtained values appeared similar to those predicted through numerical analysis. Field test results indicate that both methods yielded similar results. Therefore, it is concluded that it is feasible to use the DIC and high-speed camera to measure ground displacements and vibrations from blasting in underground mining. In addition, the system conditions required for blast vibration measurement were considered by comparing the accuracy with the existing measurement methods.

Hybrid electronic skin combining triboelectric nanogenerator and humidity sensor for contact and non-contact sensing

Developing an electronic skin (e-skin) by mimicking a human skin sensory system in which tactile sensing and non-contact sensing complement each is of great significance for helping wearable electronic products to realize the intelligent perception of contact and non-contact information. Here, a hybrid e-skin (CNES) that realizes both contact and non-contact sensing is reported, which consists of a triboelectric nanogenerator (TENG) and a humidity sensor that share a flexible substrate layer. The shared flexible substrate is formed by a hydrothermal method and presents a porous and nanoflake-like morphology surface. By introducing an Ag nanowires (NWs) electrification layer on the upper surface of the shared substrate, CNES exhibits advantages in contact sensing and electrical output performance. Therefore, we achieve the monitoring for falling behaviors with such a CNES serving as a contact sensor and the management of humidity management system with the matrixes of CNESs simulating the self-powered switches. In addition, the SnO2 humidity sensing layer that is introduced on the lower surface of the shared substrate facilitates the CNES to achieve the non-contact monitoring of human respiration signals, offering a reliable pathway to design electronic skin for both contact and non-contact sensing.

Electroanalytic properties of unmodified and modified solid contact potentiometric β-lactam sensors in aqueous and biological media

Results of a comparative study of the electroanalytic properties of solid contact sensors (tubular and planar) in cefuroxime (Cefur), cefotaxime (Ceftx), cefixim (Cefix), and amoxicillin (Amox) solutions are presented. Tetraalkylammonium associates — tetradecylammonium (TDA) and dimethyldistearylammonium (DMDSA) — with complex compounds silver (I) — β-lactam [Ag(β-lac)2] TAA; ZnO modifiers, polyaniline, and polyaniline nanotubes are used as electrode active components (EAC). The studied sensors based on [Ag(Cefur)2] TDA and [Ag(Amox)2] DMDSA in solutions of cefotaxime, cefuroxime, cefixime, and amoxicillin are characterized by a short response time: for tubular 20 – 25 sec (unmodified), 12 – 17 sec (modified); for planar – 20 – 25 sec (unmodified), 10 – 15 sec (modified). Modifiers stabilize electrode potential and perform a function of a mediator of electron transfer, which leads to improvement of electroanalytic characteristics of sensors. The linear range of electrode functions in antibiotic solutions is 1 × 10–4 – 1 × 10–2 M, the detection limit ranges between 2.5 × 10–5 – 8.9 × 10–5 M for unmodified and 5.6 × 10–6 – 7.5 × 10–5 M for modified sensors, and 4.2 × 10–5 – 7.2 × 10–5 M for planar sensors. The potential drift is 8 – 12 mV/day for unmodified and 5 – 7 mV/day for modified planar sensors; service life is 1.5 – 2 months. The advantage of planar sensors is the possibility of using them in microassay detection, which is relevant in the analysis of biological media. Application of solid-contact sensors for determination of the antibiotics under study in model aqueous solutions, medicinal preparations, oral fluid, blood serum in various infectious diseases is demonstrated.

Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia.

Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.

The Use of Multilayer Perceptron (MLP) to Reduce Delamination during Drilling into Melamine Faced Chipboard

Drilling into melamine-faced-wood-based panels is one of the most common processes in modern furniture manufacturing. Delamination is usually the main and the most troublesome quality defect in this case. A lot of scientific studies draw the conclusion that the progress of tool wearing during the cutting of wood-based materials is the key problem. Therefore, tool condition monitoring and the replacement of worn tools at the right time is the most useful and common (in the industrial practice) way to reduce delamination. However, the automation of this process is still a problem due to various issues. There is yet no commercial (even prototypical) offer for the furniture industry in this regard. For this reason, it is considered advisable to try to use the multilayer perceptron (MLP) algorithm to automatically identify a drill’s condition during drilling in a laminated chipboard. It has been established that, for practical purposes, it is important to distinguish between the three different classes of tool conditions, which can be conventionally described as “Green” (keep working), “Red” (implicitly stop and replace) and “Yellow” (warning signal—stop and replace if you want to avoid deterioration in cutting quality). To register the signals generated in the cutting zone and those constituting the basis for the identification of the tool condition in the “on-line” mode, the following elements were used: contact sensor of acoustic emission, accelerometer for vibration, two-component force gauge and a microphone. The classification effects (with an overall accuracy above 70%) were ultimately fairly decent but slightly worse than those of the classification algorithms tested earlier (i.e., “nearest neighbors” or “support vector machine” algorithms). The most troublesome, however, is the fact that serious errors (mistakes between “Green” and “Red” classes) were occasionally noted (for about 1% of the analyzed cases).

A universal theoretical model for hybrid structure sensor with proximity and large-range contact force sensing

Human-robot collaboration (HRC) is highly relied on the multi-information from the environment, especially for the external object approaching and large-range contact force perception. The information collected by the sensor with only a single-function will make the system complex and hard to achieve. Herein, we propose a universal theoretical model for hybrid structure sensor with multi-information perception capacity, which combines the capacitive and piezoresistive mechanisms for proximity and large-range contact force detection, respectively. The capacitor from the complex hybrid structure is decomposed into several ones with a single dielectric constant, which can simplify the model. The change of equivalent relative dielectric constant is response for the change of sensor output during the object approaching and contact in a tiny force. The bulk and interface resistance of the composite film will extend the sensitive range of contact force. Furthermore, the theoretical model is solved via numerical method and verified by experimental results. Finally, the maximum error is only 0.57%, 3.07% and 3.83% for object approaching, contacting force in 0–20 N and in 20–120 N, respectively. It indicates the accuracy and validity of the model, which can provide a promising method for designing integrated sensor for proximity and contact sensing and active safety control on HRC.