Concept: Capacitive displacement sensor
This paper presents a fully differential single-axis accelerometer fabricated using the MetalMUMPs process. The unique structural configuration and common-centriod wiring of the metal electrodes enables a fully differential sensing scheme with robust metal sensing structures. CoventorWare is used in structural and electrical design and simulation of the fully differential accelerometer. The MUMPs foundry fabrication process of the sensor allows for high yield, good process consistency and provides 20 μm structural thickness of the sensing element, which makes the capacitive sensing eligible. In device characterization, surface profile of the fabricated device is measured using a Veeco surface profilometer; and mean and gradient residual stress in the nickel structure are calculated as approximately 94.7 MPa and -5.27 MPa/μm, respectively. Dynamic characterization of the sensor is performed using a vibration shaker with a high-end commercial calibrating accelerometer as reference. The sensitivity of the sensor is measured as 0.52 mV/g prior to off-chip amplification. Temperature dependence of the sensing capacitance is also characterized. A -0.021fF/°C is observed. The findings in the presented work will provide useful information for design of sensors and actuators such as accelerometers, gyroscopes and electrothermal actuators that are to be fabricated using MetalMUMPs technology.
Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.
Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that “sensor-detectable”, proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than “self-reportable” talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.
Gait is not only one of the most important functions and activities in daily life but is also a parameter to monitor one’s health status. We propose a single channel capacitive proximity pressure sensor (TCPS) and gait monitoring system for smart healthcare. Insole-type TCPS (270 mm in length) was designed consisting of three layers including two shield layers and a sensor layer. Analyzing the step count and stride time are the basic indicators in gait analysis, thus they were selected as evaluation indicators. A total of 12 subjects participated in the experiment to evaluate the resolution of our TCPS. To evaluate the accuracy of TCPS, step count and its error rates were simultaneously detected by naked eye, ZIKTO Walk (ZIKTO Co., Korea), and HJ-203-K pedometer (Omron Co., Japan) as reference. Results showed that the error rate of 1.77% in TCPS was lower than those of other devices and correlation coefficient was 0.958 (p-value = 0.000). ZIKTO Walk and pedometer do not provide information on stride time, therefore it was detected by F-scan (Tekscan, USA) to evaluate the performance of TCPS. As a result, error rate of stride time measured by TCPS was found to be 1% and the correlation coefficient was 0.685 (p-value = 0.000). According to these results, our proposed system may be helpful in development of gait monitoring and measurement system as smart healthcare.
Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.
A new type of flexible proximity sensor that uses the micro-sized organic crystal as the sensing element is demonstrated. The two-terminal organic sensor can accurately perceive the external objects, such as the human finger, fibre and even AFM tip. The proximity sensor shows an unprecedented distance resolution of 0.05 mm, which is two order of magnitude higher than that of previously reported conventional capacitor proximity sensors. A novel method has been proposed to realize the location detection of the approaching unknown object by modulating the relative distance between stimuli object and sensor. Our results open a new route to realize ultra-sensitive perception of objects, making it promising candidate for applications in artificial intelligence, healthcare systems and high precision robots.
The objective of this research was to investigate a strategy for designing and fabricating computer-manufactured socket inserts that were embedded with sensors for field monitoring of limb-socket interactions of prosthetic users. An instrumented insert was fabricated for a single trans-tibial prosthesis user that contained three sensor types (proximity sensor, force sensing resistor, and inductive sensor), and the system was evaluated through a sequence of laboratory clinical tests and two days of field use. During in-lab tests 3 proximity sensors accurately distinguish between don and doff states; 3 of 4 force sensing resistors measured gradual pressure increases as weight-bearing increased; and the inductive sensor indicated that as prosthetic socks were added the limb moved farther out of the socket and pistoning amplitude decreased. Multiple sensor types were necessary in analysis of field collected data to interpret how sock changes affected limb-socket interactions. Instrumented socket inserts, with sensors selected to match clinical questions of interest, have the potential to provide important insights to improve patient care.
Currently, frequency-modulated continuous-wave (FMCW) proximity sensors are widely used. However, they suffer from a serious sweep jamming problem, which significantly reduces the ranging performance. To improve its anti-jamming capability, this paper analyzed the response mechanism of a proximity sensor with the existence of real target echo signals and sweep jamming, respectively. Then, a multi-channel harmonic timing sequence detection method, using the spectrum components' distribution difference between the real echo signals and sweep jamming, is proposed. Moreover, a novel fast Fourier transform (FFT)-based implementation was employed to extract multi-channel harmonic information. Compared with the traditional band-pass filter (BPF) implementation, this novel realization scheme only computes FFT once, in each transmission cycle, which significantly reduced hardware resource consumption and improved the real-time performance of the proximity sensors. The proposed method was implemented and proved to be feasible through the numerical simulations and prototype experiments. The results showed that the proximity sensor utilizing the proposed method had better anti-sweep jamming capability and ranging performance.
A highly sensitive, capacitive biosensor was developed to monitor trace amounts of an amphetamine precursor in aqueous samples. The sensing element is a gold electrode with molecular imprinted polymers (MIPs) immobilized on its surface. A continuous-flow system with timed injections was used to simulate flowing waterways, such as sewers, springs, rivers, etc., ensuring wide applicability of the developed product. MIPs, implemented as a recognition element due to their stability under harsh environmental conditions, were synthesized using thermo- and UV-initiated polymerization techniques. The obtained particles were compared against commercially available MIPs according to specificity and selectivity metrics; commercial MIPs were characterized by quite broad cross-reactivity to other structurally related amphetamine-type stimulants. After the best batch of MIPs was chosen, different strategies for immobilizing them on the gold electrode’s surface were evaluated, and their stability was also verified. The complete, developed system was validated through analysis of spiked samples. The limit of detection (LOD) for N-formyl amphetamine was determined to be 10μM in this capacitive biosensor system. The obtained results indicate future possible applications of this MIPs-based capacitive biosensor for environmental and forensic analysis. To the best of our knowledge there are no existing MIPs-based sensors toward amphetamine-type stimulants (ATS).
We propose an aqueous functionalized molybdenum disulfide nanoribbon suspended over a solid electrode as a capacitive displacement sensor aimed at determining the DNA sequence. The detectable sequencing events arise from the combination of Watson-Crick base-pairing, one of nature’s most basic lock-and-key binding mechanisms, with the ability of appropriately sized atomically thin membranes to flex substantially in response to sub-nanonewton forces. We employ carefully designed numerical simulations and theoretical estimates to demonstrate excellent (79 % to 86 %) raw target detection accuracy at ~70 million bases per second and electrical measurability of the detected events. In addition, we demonstrate reliable detection of repeated DNA motifs. Finally, we argue that the use of a nanoscale opening (nanopore) is not requisite for the operation of the proposed sensor and present a simplified sensor geometry without the nanopore as part of the sensing element. Our results therefore potentially suggest a realistic, inherently base-specific, high-throughput electronic DNA sequencing device as a cost-effective de-novo alternative to the existing methods.