Contact-based Sensors Research Articles

Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors

Unobtrusive monitoring of drivers’ physiological parameters is a topic gaining interest, potentially allowing to improve the performance of safety systems to prevent accidents, as well as to improve the driver’s experience or provide health-related services. In this article, two unobtrusive sensing techniques are evaluated: capacitively coupled sensing of the electrocardiogram and respiration, and radar-based sensing of heartbeat and respiration. A challenge for use of these techniques in vehicles are the vibrations and other disturbances that occur in vehicles to which they are inherently more sensitive than contact-based sensors. In this work, optimized sensor architectures and signal processing techniques are proposed that significantly improve the robustness to artefacts. Experimental results, conducted under real driving conditions on public roads, demonstrate the feasibility of the proposed approach. R peak sensitivities and positive predictivities higher than 98% both in highway and city traffic, heart rate mean absolute error of 1.02 bpm resp. 2.06 bpm in highway and city traffic and individual beat R-R interval 95% percentile error within ±27.3 ms are demonstrated. The radar experimental results show that respiration can be measured while driving and heartbeat can be recovered from vibration noise using an accelerometer-based motion reduction algorithm.

Real-Time Epileptic Seizure Detection During Sleep Using Passive Infrared Sensors

This paper addresses the problem of detecting epileptic seizures experienced by a human subject during sleep. Commonly used solutions to this problem mostly rely on detecting motion due to seizures using contact-based sensors or video-based sensors. We seek a low-cost, low-power alternative that can sense motion without making direct contact with the subject and provides high detection accuracy. We investigate the use of Passive InfraRed (PIR) sensors to sense human body motion caused by epileptic seizures during sleep which makes the body shake and causes the PIR sensor to generate an oscillatory output signal. This signal can be distinguished from that of ordinary motions during sleep using analysis with machine learning algorithms. The supervised hidden Markov model algorithm (HMM) and a 1-D and 2-D convolutional neural network (ConvNet) are used to classify the data set of the PIR sensor output into the occurrence of epileptic seizures, ordinary motions, or absence of motion. The method was tested on the PIR signals captured at 1 m from 33 recruited healthy subjects who, after watching seizure videos, either moved their body on a bed to simulate a seizure, ordinary motion, or lay still. The HMM algorithm attained 97.03% accuracy, while 1D-ConvNet and 2D-ConvNet attained an accuracy of 96.97% and 98.98%, respectively. All simulated seizures were successfully detected, with errors occurring only in distinguishing between ordinary motion and no motion, thereby demonstrating the potential for using PIR sensors in the epileptic seizure detection.

An optical-based technique to obtain operating deflection shapes of structures with complex geometries

Abstract The dynamic characteristics of structures are conventionally obtained by exciting the structure using an impulse hammer or a mechanical shaker and measuring the response using uniaxial or multi-axial accelerometers. However, contact-based sensors can mass-load the structure and do not provide full-field data. Hence, obtaining the true dynamics of the structure using conventional sensors can be challenging. That makes test engineers seek different non-contact techniques that can provide full-field data without mass-loading the structure. Recently, stereo-photogrammetry and three-dimensional digital image correlation (3D DIC) have been adopted to collect operating data for structural analysis. These non-contact optical techniques provide a wealth of distributed data over the entire structure. However, the stereo-camera system is limited by the field-of-view of the cameras; a single pair of DIC cameras may not be able to provide deformation data for the entire structure. Hence, it is challenging to obtain the vibration characteristics of the entire structure. In the current work, a multi-view 3D DIC approach is used and validated to predict the vibrational characteristics of an automotive muffler with a complex structure. A pair of DIC cameras travels over the entire structure to capture the deformation of each field of view. The measured data includes the geometry and displacement data, which is later mapped into a global coordinate system. The measured data in the time domain for each field-of-view is transformed to the frequency domain to extract the operational deflection shapes and resonant frequencies for each field of view. The obtained deflection shapes are stitched together in the frequency domain to extract the operating deflection shapes and resonant frequencies of the automotive body panel with a complex structure.

A literature review of next-generation smart sensing technology in structural health monitoring

Advent of computationally efficient smartphones, inexpensive high-resolution cameras, drones, and robotic sensors has brought a new era of next-generation intelligent monitoring systems for civil infrastructure. Vibration-based condition assessment has garnered as a prominent method of evaluating the health of large-scale infrastructure. The use of contact-based sensors for acquiring vibration data becomes uneconomical and tedious due to their instrumentation cost, centralized nature, and densification required to collect sufficient data for system identification of modern complex structures. A need to advance and develop alternative methods for efficient sensing system results in next-generation measurement technology of structural health monitoring. The abundance of handheld smartphones with easily programmable framework has helped in modifying relevant software to acquire vibration data using embedded sensors in the smartphone. The inexpensive cameras have been used to capture images and videos that are utilized to understand the structural behavior with the aid of advanced signal processing techniques. The inaccessible components of structures require noncontact sensors such as unmanned aerial vehicles (UAVs) or so-called drones and mobile sensors to acquire structural data. To the authors' knowledge, this paper first time presents a comprehensive review of a suite of next-generation smart sensing technology that has been developed in recent years within the context of structural health monitoring. The state-of-the-art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.

Alignment-Free Cross-Sensor Fingerprint Matching Based on the Co-Occurrence of Ridge Orientations and Gabor-HoG Descriptor

The existing automatic fingerprint verification methods are designed to work under the assumption that the same sensor is installed for enrollment and authentication (regular matching). There is a remarkable decrease in efficiency when one type of contact-based sensor is employed for enrolment and another type of contact-based sensor is used for authentication (cross-matching or fingerprint sensor interoperability problem). The ridge orientation patterns in a fingerprint are invariant to the sensor type. Based on this observation, we propose a robust fingerprint descriptor called the co-occurrence of ridge orientations (Co-Ror), which encodes the spatial distribution of ridge orientations. Employing this descriptor, we introduce an efficient automatic fingerprint verification method for cross-matching problem. Furthermore, to enhance the robustness of the method, we incorporate scale-based ridge orientation information through the Gabor-HoG descriptor. The two descriptors are fused with the canonical correlation analysis (CCA), and the matching score between two fingerprints is calculated using city-block distance. The proposed method is alignment-free and can handle the matching process without the need for a registration step. The intensive experiments on two benchmark databases (FingerPass and MOLF) show the effectiveness of the method and reveal its significant enhancement over the state-of-the-art methods, such as VeriFinger (a commercial SDK), minutia cylinder-code (MCC), MCC with scale, and the thin-plate spline (TPS) model. The proposed research will help security agencies, service providers, and law-enforcement departments to overcome the interoperability problem of contact sensors of different technologies and interaction types.

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors.

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate () and 100% detection probability at 1% (false acceptance rate) (), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% and 100% , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

Non-Contact Sensor for Long-Term Continuous Vital Signs Monitoring: A Review on Intelligent Phased-Array Doppler Sensor Design.

It has been the dream of many scientists and engineers to realize a non-contact remote sensing system that can perform continuous, accurate and long-term monitoring of human vital signs as we have seen in many Sci-Fi movies. Having an intelligible sensor system that can measure and record key vital signs (such as heart rates and respiration rates) remotely and continuously without touching the patients, for example, can be an invaluable tool for physicians who need to make rapid life-and-death decisions. Such a sensor system can also effectively help physicians and patients making better informed decisions when patients’ long-term vital signs data is available. Therefore, there has been a lot of research activities on developing a non-contact sensor system that can monitor a patient’s vital signs and quickly transmit the information to healthcare professionals. Doppler-based radio-frequency (RF) non-contact vital signs (NCVS) monitoring system are particularly attractive for long term vital signs monitoring because there are no wires, electrodes, wearable devices, nor any contact-based sensors involved so the subjects may not be even aware of the ubiquitous monitoring. In this paper, we will provide a brief review on some latest development on NCVS sensors and compare them against a few novel and intelligent phased-array Doppler-based RF NCVS biosensors we have built in our labs. Some of our NCVS sensor tests were performed within a clutter-free anechoic chamber to mitigate the environmental clutters, while most tests were conducted within the typical Herman-Miller type office cubicle setting to mimic a more practical monitoring environment. Additionally, we will show the measurement data to demonstrate the feasibility of long-term NCVS monitoring. The measured data strongly suggests that our latest phased array NCVS system should be able to perform long-term vital signs monitoring intelligently and robustly, especially for situations where the subject is sleeping without hectic movements nearby.

A Review on the Design of Non-Contact Vital Signs (NCVS) Biosensors for Long-Term Continuous Monitoring

It has been the dream of many scientists and engineers to realize a non-contact remote biosensing system that can perform continuous, accurate, and long-term monitoring of human vital signs as what we saw in some SciFi movies. Having a system that can measure and record key vital signs (such as heart rates and respiration rates) remotely and continuously without touching the patients, for example, can be an invaluable tool for physicians who need to make rapid life-and-death decisions. Such a system would also be an effective tool to help physicians and patients make better informed decisions when viewing a patient’s long-term monitored data. Therefore, there has been a large increase in research activities to develop a system that can monitor a patient’s vital signs and quickly transmit the information to healthcare professionals. Radio-Frequency (RF) Non-contact vital signs (NCVS) monitoring system are particularly attractive for long term vital signs monitoring because there are no wires, electrodes, wearable devices, nor contact-based sensors for the subjects to worry about. Approach: In this paper, we will provide a brief review on some latest development on NCVS sensors, and also compare them against a novel phased-array Doppler-based RF non-contact NCVS biosensor system we have built in our RF-SoC Lab. To determine the accuracy of our NCVS sensor, the heart rate measurements from our NCVS system are compared against an external contact-based piezoelectric finger sensor as a reference. Objective: In some of our previous works, the NCVS sensor performance was tested within a clutter-free anechoic chamber inside our Lab to mitigate the environmental clutters. To examine the performance of our NCVS sensor in a more practical setting, most tests here were performed within the typical Herman-Miller type office cubicle setting, inside a section of our Lab. Additionally, we will detail the measurement data to demonstrate the feasibility of long-term noncontact vital signs monitoring. Conclusion: The measured data strongly suggests that our latest phased array NCVS system should be able to perform long-term vital signs monitoring, especially for situations where the subject is sleeping and there is not expected to have hectic movements nearby. Recommendation: Our phased-array NCVS biosensor system appears to be quite robust and attractive. We will be performing several 24-hours tests to quantitatively analyze the effects of the background noise to long-term vital signs monitoring in office cubicle setting, and report our findings on these important topics in the near future. In the future, we also plan to continue to perform long-term NCVS monitoring on subjects during their sleep next for potential applications on sleep apnea and sudden cardiac arrest (SCA) patients.

Analysis of test tools for evaluation of contact-based sensor systems for pedestrian detection

Active systems of the passive vehicle safety, like deployable bonnets or external airbags, are installed in series vehicles since 2005 to fulfil pedestrian protection requirements. However, an appropriate test procedure for these systems was not available until 2010, when Euro NCAP published a revised version of the pedestrian test protocol (version 5.2). In this test protocol, a test method to evaluate contact-based sensor systems for pedestrian detection using so-called hardest-to-detect impactors is described. These impactors shall reproduce the lowest possible impact load induced in the vehicle front in case of a collision with a pedestrian, which is strongly dependent on the anthropometry of the pedestrian as well as the impact configuration. In this paper a holistic analysis of the accident kinematics of pedestrian–vehicle collisions and resulting loads induced in the vehicle front is conducted. The vehicle front is modelled using a generic vehicle buck, which is specifically designed for this analysis. As can be observed by the conducted simulations, the pedestrian size inducing the lowest possible impact load varies for different vehicle categories. Thus, it is not sufficient to consider only one pedestrian size. The comparison of these vehicle–pedestrian simulations with the loads being induced by three hardest-to-detect impactors shows that none of the three impactors is able to reproduce the ‘worst-case scenario’ for all analysed four vehicle categories. The results of this study, which was conducted on behalf of and funded by the German Federal Highway Research Institute (BASt), could be used for the development of a suitable, effective and objective test procedure for active systems of the passive vehicle safety with regard to pedestrian protection.

Exploiting spatial redundancy of image sensor for motion robust rPPG.

Remote photoplethysmography (rPPG) techniques can measure cardiac activity by detecting pulse-induced color variations on human skin using an RGB camera. State-of-the-art rPPG methods are sensitive to subject body motions (e.g., motion-induced color distortions). This study proposes a novel framework to improve the motion robustness of rPPG. The basic idea of this paper originates from the observation that a camera can simultaneously sample multiple skin regions in parallel, and each of them can be treated as an independent sensor for pulse measurement. The spatial redundancy of an image sensor can thus be exploited to distinguish the pulse signal from motion-induced noise. To this end, the pixel-based rPPG sensors are constructed to estimate a robust pulse signal using motion-compensated pixel-to-pixel pulse extraction, spatial pruning, and temporal filtering. The evaluation of this strategy is not based on a full clinical trial, but on 36 challenging benchmark videos consisting of subjects that differ in gender, skin types, and performed motion categories. Experimental results show that the proposed method improves the SNR of the state-of-the-art rPPG technique from 3.34 to 6.76 dB, and the agreement ( ±1.96σ) with instantaneous reference pulse rate from 55% to 80% correct. ANOVA with post hoc comparison shows that the improvement on motion robustness is significant. The rPPG method developed in this study has a performance that is very close to that of the contact-based sensor under realistic situations, while its computational efficiency allows real-time processing on an off-the-shelf computer.

Thermal Impedance Spectroscopy for Li-Ion Batteries with an IR Temperature Sensor System

Thermal impedance spectroscopy (TIS) is a non-destructive method for characterizing thermal properties of entire battery cells. Heat capacity, thermal conductivity and heat exchange with environment are deter-mined by an evaluation of the heat transfer behavior of the battery. TIS measurements are usually conduct-ed with contact-based temperature sensors, such as thermocouples or thermistors, which show drawbacks at higher convection rates and higher temperature differences between battery and environment. To elude drawbacks in these kinds of sensors, an infrared-based temperature sensor system for battery sur-face temperature measurements is implemented. TIS measurements are conducted with this sensor system and with conventional, contact-based temperature sensors. Accuracy and reliability of thermal parameter identification is analyzed for the different sensor systems. Moreover, thermal parameters are identified for different cylindrical 18650 Li-ion cells with capacities between 1.1 Ah and 2.7 Ah. The comparison of different types of temperature sensors shows that contact-based sensors underestimate surface temperatures even at low temperature differences to environment. This causes an error in thermal parameter identification. The TIS measurements performed with contact-based sensors show divergence of 20 - 60 % for heat capacity, 30 - 70 % for thermal conductivity and 20 - 60 % for convective heat exchange with environment. With our IR temperature sensor system, parameter identification is performed for different batteries. Re-sulting values for specific heat capacity are in a range between 900 and 1020 J/kgK and thermal conduc-tivities in radial direction lies between 3.1 and 3.6 W/mK. Our investigations show that IR-based temperature sensors are an effective progression for TIS measure-ments and improve quality of parameter identification at low cost. Moreover, discrepancies mentioned in TIS literature can be explained by our findings.