Contactless Detection Research Articles

Surface Water Wave Detector for Floating Devices with Capacitive Sensor

Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the real-time wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42° and a mean absolute error of 5.85°.

Surface Water Wave Detector for Floating Devices with Capacitive Sensor

Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the real-time wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42° and a mean absolute error of 5.85°.

LiqDetector

With the advancement of wireless sensing technologies, RF-based contact-less liquid detection attracts more and more attention. Compared with other RF devices, the mmWave radar has the advantages of large bandwidth and low cost. While existing radar-based liquid detection systems demonstrate promising performance, they still have a shortcoming that in the detection result depends on container-related factors (e.g., container placement, container caliber, and container material). In this paper, to enable container-independent liquid detection with a COTS mmWave radar, we propose a dual-reflection model by exploring reflections from different interfaces of the liquid container. Specifically, we design a pair of amplitude ratios based on the signals reflected from different interfaces, and theoretically demonstrate how the refractive index of liquids can be estimated by eliminating the container's impact. To validate the proposed approach, we implement a liquid detection system LiqDetector. Experimental results show that LiqDetector achieves cross-container estimation of the liquid's refractive index with a mean absolute percentage error (MAPE) of about 4.4%. Moreover, the classification accuracies for 6 different liquids and alcohol with different strengths (even a difference of 1%) exceed 96% and 95%, respectively. To the best of our knowledge, this is the first study that achieves container-independent liquid detection based on the COTS mmWave radar by leveraging only one pair of Tx-Rx antennas.

Contactless fall detection with radio frequency sensors for the aging population

AbstractBackgroundFalls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device.MethodSix healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants.ResultOur study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity.ConclusionContactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.

Contactless fall detection with radio frequency sensors for the aging population

AbstractBackgroundFalls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device.MethodSix healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants.ResultOur study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity.ConclusionContactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.

Biomedical Radar System for Real-Time Contactless Fall Detection and Indoor Localization

Fall incidents represent a major public health problem among elderly people. This resulted in a significant increase of the number of investigated systems aiming at detecting falls promptly. In this respect, in this work, a biomedical radar system is proposed for remote real-time fall detection and indoor localization. The system, consisting of a sensor and a base station, combines radar and wireless communication techniques, and uses a data processing technique to distinguish between fall events and normal movements. The classification, based on a Least-Square Support Vector Machine (LS -SVM), combined with the sliding window principle allows to perform fall detection in real-time. Moreover, it is capable to localize the subjects when the fall incident has been detected. The in-vivo validation showed a high success rate in detecting fall events, with a maximum delay of 340 ms. Moreover, a maximum mean absolute errors (MAE) of 3.8 cm and a maximum root-mean-square error (RMSE) of 7.5 cm were reported in measuring the subject's absolute distance.

A new approach to contactless probing of vital signs

Vital sign detection is used across ubiquitous scenarios in medical and health settings and contact and wearable sensors have been widely deployed. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions along with privacy concerns. This brief report appraises a photonic radar for non-contact vital sign detection. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. This could cater for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Sensor-based Human Activity Recognition (HAR) requires the acquisition of Channel State Information(CSI) data with time series based on sensors to predict human behavior. Many existing approaches are based on wearable sensors and cameras, which increase the burden and privacy issues for patients. Self-powered sensors are capable of non-contact collection of time series data generated by human activity while ensuring their own stable operation. In this paper, we propose a deep learning-based framework for contactless real-time activity detection of humans using self-powered sensors, which is called Multilayer Bi-directional Long Short-Term Memory (MBLSTM). The collected WIFI CSI data are fed into our proposed network model, which is then used to learn representative features of both sides from the original continuous CSI measurements. And the attention model is used to assign different weights to the learned features, and finally activity recognition is performed. Experimental results show that our proposed method achieves an accuracy of more than 96% for the recognition of six activities in multiple rounds of testing, outperforming other benchmark methods used for comparison.

CLNet: a contactless fingerprint spoof detection using deep neural networks with a transfer learning approach

CLNet: a contactless fingerprint spoof detection using deep neural networks with a transfer learning approach

Detection of Vital Sign Based on UWB Radar by a Time Domain Coherent Accumulation Method

Ultra-wide band (UWB) radar is a research hotspot in the field of contactless vital sign detection. It is difficult to detect heartbeat information of human accurately at a distance more than 1 m due to the weak echo. In this paper, a variational mode decomposition (VMD) method based on time domain coherent accumulation (TDCA) is proposed to detect human vital signs such as respiration rate (RR) and heart rate (HR). Besides, radar echo of vital sign detection is modeled. The vital sign detection region (VSDR) is determined according to the standard deviation of radar echo, and a series of slow-time signals in this region are accumulated with TDCA method, so as to improve the signal-to-noise ratio (SNR) of vital signs. In order to improve the accuracy of RR and HR detection, the sliding time window combined with VMD algorithm is used to detect. Experimental results in different distances show that the proposed VMD method based on TDCA can effectively detect RR and HR. The detection distance of HR can be increased to 2 m and the accuracy of HR detection is more than 93%.

Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

Objective. Sleep apnea has a high incidence and is a potentially dangerous disease, and its early detection and diagnosis are challenging. Polysomnography (PSG) is considered the best approach for sleep apnea detection, but it requires cumbersome and complicated operations. Thus, it cannot satisfy the family healthcare needs. Approach. To facilitate the initial detection of sleep apnea in the home environment, we developed a sleep apnea classification model based on snoring and hybrid neural network, and implemented the well trained model in an embedded hardware platform. We used snore signals from 32 patients at Shenzhen People's Hospital. The Mel-Fbank features were extracted from snore signals to build a sleep apnea classification model based on Bi-LSTM with attention mechanism. Main results. The proposed model classified snore signals into four types: hypopnea, normal condition, obstructive sleep apnea, and central sleep apnea, with 83.52% and 62.31% accuracies, corresponding to the subject-dependence and subject-independence validation, respectively. After pruning and model quantization, at the cost of 0.81% and 0.95% accuracy loss of the subject dependence and subject independence classification, respectively, the number of model parameters and model storage space were reduced by 32.12% and 60.37%, respectively. The model exhibited accuracies of 82.71% and 61.36% based on the subject dependence and subject independence validations, respectively. When the well trained model was successfully porting and running on an STM32 ARM-embedded platform, the model accuracy was 58.85% for the four classifications based on leave-one-subject-out validation. Significance. The proposed sleep apnea detection model can be used in home healthcare for the initial detection of sleep apnea.

Technological Solutions for Digital Identity

Digital transformation of enterprises is driving the need for a digital identity to recognize people for delivering services. The implementation of digital identity is complex, requiring several technological solutions and much coordination. Capturing and processing data is challenging because biometric issues may arise due to imaging errors. This article addresses this issue and proposes a computer vision-based framework for contactless recognition process using a focus group discussion approach for inputs from experts. The proposed framework enhances image capturing process, extraction of high-quality features from captured images, image processing, contactless face detection, and authentication. The study also derives lessons for other biometric-based identity projects based on image analysis. The proposed framework can be used as a reference for understanding multidimensional perspectives, scalability, and adoption of technological solutions in other similar projects in developing countries in future.

Programmable-Modulated Ultrasonic Transducer Array for Contactless Detection of Viral RNAs.

The current polymerase chain reactions-based nucleic acid tests for large-scale infectious disease diagnosis are always lab-dependent and generate large amounts of highly infectious plastic waste. Direct non-linear acoustic driven of microdroplets provide an ideal platform for contactless spatial and temporal manipulation of liquid samples. Here, a strategy to programmable-manipulate microdroplets using potential pressure well for contactless trace detection is conceptualized and designed. On such contactless modulation platform, up to seventy-two piezoelectric transducers are precisely self-focusing single-axis arranged and controlled, which can generate dynamic pressure nodes for effectively contact-free manipulating microdroplets without vessel contamination. In addition, the patterned microdroplet array can act as contactless microreactor and allow multiple trace samples (1-5µL) biochemical analysis, and the ultrasonic vortex can also accelerate non-equilibrium chemical reactions such as recombinase polymerase amplification (RPA). The results of fluorescence detection indicated that such programmable modulated microdroplet achieved contactless trace nucleic acid detection with a sensitivity of 0.21copyµL-1 in only 6-14min, which is 30.3-43.3% shorter than the conventional RPA approach. Such a programmable containerless microdroplet platform can be used for toxic, hazardous, or infectious samples sensing, opening up new avenues for developing future fully automated detection systems.

Photonic radar for contactless vital sign detection

Vital sign detection is used across ubiquitous scenarios in medical and health settings, and contact and wearable sensors have been widely deployed. However, they are unsuitable for patients with burn wounds or infants with insufficient areas for attachment. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions and has privacy concerns. Here we report a photonic radar for non-contact vital sign detection to overcome these challenges. This photonic radar can achieve millimetre-level range resolution based on synthesized radar signals with a bandwidth of up to 30 GHz. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. Moreover, we demonstrate that the optical signals generated from the proposed system can enable vital sign detection based on light detection and ranging (LiDAR). This demonstration reveals the potential of a sensor-fusion architecture that can combine the complementary features of radar and LiDAR to achieve improved sensing accuracy and system resilience. The work provides a technical basis for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Detecting human head movement during sleep is important as it can help doctors to assess many physical or mental health problems, such as infantile eczema, calcium deficiency, insomnia, anxiety disorder, and even Parkinson’s disease, and provide useful clues for accurate diagnosis. To obtain the information of head movement during sleep, current solutions either use a camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes rely on light conditions and raise privacy concerns. Many people, including the elderly and infants, may be reluctant to wear wearable devices during sleep. In this paper, we propose Wi-Senser, a nonintrusive and contactless smart monitoring system for detecting head movement during sleep. Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. Specifically, we constructed a filtering channel including a Hampel filter, wavelet filter, and mean filter to remove outliers and noises. We propose a new metric of carrier sensitivity to select an optimal subcarrier for recording the change in targeted body movement from 30 candidate subcarriers. Finally, we designed a peak-finding algorithm to capture the real peak set recording the change in human head movement. We designed and implemented Wi-Senser with just one commercial off-the-shelf (COTS) router and one laptop equipped with an Intel 5300 network interface card (NIC). We evaluated the performance of Wi-Senser with 10 volunteers (6 adults and 4 children). Extensive experiments demonstrate that Wi-Senser can achieve 97.95% accuracy for monitoring head movement during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous, and accurate detection of minute human movement without any additional cost.

Non-contact automatic COVID-19 nucleic acid detection robot

This product is mainly used to realise the contactless automatic COVID-19 nucleic acid detection robot, which can navigate to the workstation or location of the set personnel through the camera and radar and is mainly applied to the autonomous movement function of the existing automatic nucleic acid detection robot. The robot will conduct intelligent navigation according to the position of the nucleic acid person, avoid obstacles autonomously through radar and camera, and use clever navigation to plan the nearest route to the position part of the needed nucleic acid. This product carries a camera and laser radar based on SLAM technology for obstacle avoidance of autonomous mobile robots, through the experiment of advanced algorithms and the team constantly optimised. The algorithm makes its universality, compatibility, real-time and precision to achieve the desired effect. These autonomous mobile robots in the industrial production industry are essential to the application.

Identification of Insulator Contamination Status Based on Multi-spectral Image Fusion and Radial Basis Function Neural Network

Insulator contamination monitoring is an important way to avoid insulator contamination and maintain insulation performance. In order to ensure power supply and achieve contactless detection of insulator contamination status, a method is proposed in this paper to identify insulator contamination status by adopting infrared, ultraviolet, and visible multi-spectral image information fusion. An insulator with different contamination states in a number of substations is taken as the research object in this paper. The image segmentation is performed by using the seed region growth method to extract the infrared, ultraviolet and visible features of the insulator surface, and the radial basis function neural network learning algorithm is used to classify and decompose and fuse the images according to their different local area energies. The comparison of the recognition rates using infrared and ultraviolet features with those fused shows that the method has significant advantages and provides a new method for the detection of insulator contamination status.

A Superconducting Micro-Magnetometer for Quantum Vortex in Superconducting Nanoflakes.

Superconducting quantum interferometer device (SQUID) plays a key role in understanding electromagnetic properties and emergent phenomena in quantum materials. The technological appeal of SQUID is that its detection accuracy for the electromagnetic signal can precisely reach the quantum level of a single magnetic flux. However, conventional SQUID techniques normally can only be applied to a bulky sample and do not have the capability to probe the magnetic properties of micro-scale samples with small magnetic signals. Herein, it is demonstrated that, based on a specially designed superconducting nano-hole array, the contactless detection of magnetic properties and quantized vortices in micro-sized superconducting nanoflakes is realized. An anomalous hysteresis loop and a suppression of Little-Parks oscillation are observed in the detected magnetoresistance signal, which originates from the disordered distribution of the pinned vortices in Bi2 Sr2 CaCu2 O8+δ . Therefore, the density of pinning centers of the quantized vortices on such micro-sized superconducting samples can be quantitatively evaluated, which is technically inaccessible for conventional SQUID detection. The superconducting micro-magnetometer provides a new approach to exploring mesoscopic electromagnetic phenomena of quantum materials.

Non‐destructive and contactless defect detection inside leading edge coatings for wind turbine blades using mid‐infrared optical coherence tomography

AbstractLeading edge erosion of wind turbine blades is one of the most critical issues in wind energy production, resulting in lower efficiency, as well as increased maintenance costs and downtime. Erosion is initiated by impacts from rain droplets and other atmospheric particles, so to protect the blades, special protective coatings are applied to increase their lifetime without adding significantly to the weight or friction of the blade. These coatings should ideally absorb and distribute the force away from the point of impact; however, microscopic defects, such as bubbles, reduce the mechanical performance of the coating, leading to cracks and eventually erosion. In this work, mid‐infrared (MIR) Optical Coherence Tomography (OCT) is investigated for non‐destructive, contactless inspection of coated glass‐fiber composite samples to identify subsurface coating defects. The samples were tested using rubber projectiles to simulate rain droplet and particle impacts. The samples were subsequently imaged using OCT, optical microscopy, and X‐ray tomography. OCT scanning revealed both bubbles and cracks below the surface, which would not have been detected using ultrasonic or similar non‐destructive methods. In this way, OCT can complement the existing quality control in turbine blade manufacturing, help improve the blade lifetime, and reduce the environmental impact from erosion.

A New Online Monitoring Method for Water-in-Oil Droplet Based Microfluidic Devices

The online water-in-oil (w/o) droplet monitoring is essential for the uniformity of the droplet based microfluidic devices. In this work, a contactless impedance detection (CID) method for online w/o droplet monitoring in microfluidic devices is proposed. In the proposed method, a new CID sensor, which can work at low working frequency and overcome the unfavorable influence of coupling capacitance, is developed to obtain the impedance information of the w/o droplet. Based on the obtained impedance information, measurement models are established to realize online measurement of drop length and dispersed phase holdup. Then the coefficients of variation (CVs) of the two measured parameters are calculated to evaluate the uniformity of the droplets for online monitoring. Online monitoring experiments of w/o droplet were carried out in a microfluidic device with a 500 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}\,\,\times $ </tex-math></inline-formula> 500 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> microchannel. The experimental results verify the effectiveness of the proposed droplet monitoring method. The developed CID sensor can realize impedance measurement at a working frequency lower than 50kHz. The drop length and dispersed phase holdup are useful parameters for online monitoring of droplets. With the established measurement models, the maximum relative error of droplet length measurement is less than 10% in the droplet length range of 1.5mm-3.5mm. And the maximum error of dispersed phase holdup measurement is less than 5% in the dispersed phase holdup range of 0.20-0.67.