Sensor Validation Research Articles

Validity and Test-Retest Reliability of the Vmaxpro Sensor for Evaluation of Movement Velocity in the Deep Squat.

Feuerbacher, JF, Jacobs, MW, Dragutinovic, B, Goldmann, J-P, Cheng, S, and Schumann, M. Validity and test-retest reliability of the Vmaxpro sensor for evaluation of movement velocity in the deep squat. J Strength Cond Res 37(1): 35-40, 2023-We aimed at assessing the validity and test-retest reliability of the inertial measurement unit-based Vmaxpro sensor compared with a Vicon 3D motion capture system and the T-Force sensor during an incremental 1-repetition maximum (1RM) test and at submaximal loads. Nineteen subjects reported to the laboratory for the 1RM test sessions, whereas 15 subjects carried out another 3 sessions consisting of 3 repetitions with 4 different intensities (30, 50, 70, and 90% of 1RM) to determine the intra- and interday reliability. The Vmaxpro sensor showed high validity (Vicon: R2 = 0.935; T-Force: R2 = 0.968) but an overestimation of the mean velocities (MVs) of 0.06 ± 0.08 m·s-1 and 0.06 ± 0.06 m·s-1 compared with Vicon and T-Force, respectively. Regression analysis indicated a systematic bias that is increasing with higher MVs. The intraclass correlation coefficients (ICCs) for Vmaxpro were moderate to high for intraday (ICC: 0.662-0.938; p ≤ 0.05) and for interday (ICC: 0.568-0.837; p ≤ 0.05) reliability, respectively. The Vmaxpro is a valid and reliable measurement device that can be used to monitor movement velocities within a training session. However, practitioners should be cautious when assessing movement velocities on separate days because of the moderate interday reliability.

Synthesis and validation of ultrasensitive stripping voltammetric sensor based on polypyrrole@ZnO/Fe3O4 core-shell nanostructure for picomolar detection of artesunate and dopamine drugs.

A stripping voltammetric sensor for ultrasensitive detection of artesunate (ART) and dopamine HCl (DA) has been successfully developed using a Ppy@ZnO/Fe3O4 core-shell nanocomposite ([PZM])-modified carbon paste sensor (MCPS). Fourier transform infrared spectroscopy, X-ray diffraction, field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, dynamic light scattering, Brunauer-Emmett-Teller surface area method, and high-resolution transmission electron microscopy were used to characterize the physicochemical properties of the nanomaterials. Noteworthily, the morphology of [PZM] reveals a spherical core-shell nanostructure with an increase in the average diameter range of 20-37.5 nm (specific surface area (SSA) of 28.5 m2 g-1 (0.0247 cm3 g-1)) when compared with the average diameter range 7.5-15.7 nm (SSA of 5.43 m2 g-1 (0.0111 cm3 g-1)) of ZnO/Fe3O4[ZM]. The [PZM] MCPS provided the best electroactive surface area (0.078 cm2) and the least electrocatalytic activity (Rst = 370 Ω). Furthermore, the MCPS showed low detection limits (LODs) of 0.092 pg mL-1 (0.24 pM) and 0.0046 pg mL-1 (0.03 pM) for ART and DA, respectively. Moreover, LODs were found to be 0.029 pg mL-1 (0.75 pM) and 0.014 pg mL-1 (0.09 pM) for ART mixed with 0.7 pM of DA (ART1) and DA in the presence of 2.0 pM of ART drug (DA1), respectively. In addition, the MCPS revealed a proper repeatability, reproducibility, and storage stability (93.5-90.48%). During the routine analysis, the [PZM] MCPS detected ART and DA concentrations in human urine, without interference.

Validation of the Novel Transdermal Arterial Gasotransmitter Sensor (Tags™) System in Measuring Transdermal Hydrogen Sulfide in Human Subjects

Validation of the Novel Transdermal Arterial Gasotransmitter Sensor (Tags™) System in Measuring Transdermal Hydrogen Sulfide in Human Subjects

Structure revision and chemical synthesis of ligandrol's main bishydroxylated long-term metabolic marker.

Although the main bishydroxylated long-term metabolite of the WADA-banned anabolic agent ligandrol (LGD-4033) is an important metabolic marker, it is not readily available in sufficient quantities to facilitate the development and validation of related analytical protocols or sensors. A chemically more robust structure was postulated as an alternative to the one previously established. The NMR spectra of the synthesized material and its LC-HRMS comparison with a relevant metabolic sample support the proposed structural revision.

Hydrogen Leakage Detection of Reusable Rocket Experiment Vehicle with Hydrogen/Oxygen Concentration Ratio Sensor

In future space transportation system, concept of fault tolerant is important. Propellant leakage is one of fault modes that should be considered in the system. The space transportation vehicles could have flight environment with very little or without oxygen. Therefore, for the propellant leakage detection, gas sensors that can work without oxygen are needed. The authors have developed the gas sensor that can work without oxygen and of which output depends on the ratio of hydrogen to oxygen concentration. The present sensors were installed on a hydrogen-fueled reusable rocket experimental vehicle so that the characteristics of the sensor’s behavior was measured and evaluated though ground firing test operation of the rocket engines. Comparison of the present sensor’s behavior to that of reference sensors shows validity of the present sensor.

Identification of defected sensors in an array of amperometric gas sensors

PurposeAmperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.Design/methodology/approachIn this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.FindingsThe results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response.Originality/valueThis work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.

Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements.

LiDAR sensors are needed for use in vehicular applications, particularly due to their good behavior in low-light environments, as they represent a possible solution for the safety systems of vehicles that have a long braking distance, such as trams. The testing of long-range LiDAR dynamic responses is very important for vehicle applications because of the presence of difficult operation conditions, such as different weather conditions or fake targets between the sensor and the tracked vehicle. The goal of the authors in this paper was to develop an experimental model for indoor testing, using a scaled vehicle that can measure the distances and the speeds relative to a fixed or a moving obstacle. This model, containing a LiDAR sensor, was developed to operate at variable speeds, at which the software functions were validated by repeated tests. Once the software procedures are validated, they can be applied on the full-scale model. The findings of this research include the validation of the frontal distance and relative speed measurement methodology, in addition to the validation of the independence of the measurements to the color of the obstacle and to the ambient light.

Abstract 11669: “Hearing the Heart” - Validation of a Novel Digital Health Earbud Technology to Measure Cardiac Time Intervals Through Infrasonic Hemodynography

Introduction: The cardiovascular (CV) system produces low frequency, ‘infrasonic’, auditory vibrations during the cardiac cycle. Herein, we report the first-in-person validation of a novel earbud sensor to capture CV time intervals and the feasibility of non-invasive infrasonic hemodynography (IH) using the MindMics ® wireless earbuds for long term in-ear CV monitoring. Methods: Infrasonic waveforms were captured during cardiac catheterization (CC) among 5 study subjects wearing the IH ear-buds (Figure A) who underwent CC for the evaluation of coronary artery disease. Simultaneous IH and CC waveforms were acquired and time synchronized at 1000Hz sampling rate as time-series datasets. Each subject underwent echocardiography to identify aortic valve opening/closure (AVO/AVC) and left ventricular (LV) outflow tract flow measurements with hemodynamic waveforms during CC measuring LV ejection time (LVET). Validation of the IH waveform (in-ear acoustic pressure measured in Pascals) was compared to echocardiography (AVO/AVC) and hemodynamic waveforms (LVET) with concordance and Bland-Altman analysis, and with overlaid data visualizations to CV time intervals. Results: 5 study subjects comprised 257 CV cycles with a total data set of >450,000 time-series data points. IH signals collected simultaneously with the pulsed wave Doppler demonstrated alignment with AVO/AVC (Figure B) and were synchronized to CC waveforms in the aorta (Figure C). A high correlation between LVET measured from IH and CC was observed (R=0.87, p<0.0001, Figure D), with a mean absolute error of 14.7ms and a bias of 7.2ms (Figure E) (mean±SEM of 342.3±2.1ms for CC and 349.5±2.1ms for IH). Conclusions: In a first-in-person study, we report high accuracy between IH, echocardiography, and CC hemodynamic waveforms to capture CV time intervals including CV performance measures. Further studies are underway to validate IH and the earbud sensor towards non-invasive hemodynamic monitoring.

Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information.

This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.

Design and Field Implementation of a Low-Cost, Open-Hardware Platform for Hydrological Monitoring

Hydrologic processes acting on catchments are complex and variable, especially in mountain basins due to their topography and specific characteristics, so runoff simulation models and water management are also complex. Nevertheless, model parameters are usually estimated on the basis of guidelines from user manuals and literature because they are not usually monitored, due to the high cost of conventional monitoring systems. Within this framework, a new and promising generation of low-cost sensors for hydrologic monitoring, logging, and transition has been developed. We aimed to design a low-cost, open-hardware platform, based on a Raspberry Pi and software written in Python 3, for measuring, recording, and wireless data transmission in hydrological monitoring contexts. Moreover, the data are linked to a runoff model, in real time, for flood prevention. Complementarily, it emphasizes the role of the calibration and validation of soil moisture, rain gauges, and water depth sensors in laboratories. It was installed in a small mountain basin. The results showed mean absolute errors of ±2.2% in soil moisture, ±1 mm in rainfall, and ±0.51 cm in water depth measurements; they highlight the potential of this platform for hydrological monitoring and flood risk management.

Concurrent validation of inertial sensors for measurement of knee kinematics in individuals with knee osteoarthritis: A technical report

Concurrent validation of inertial sensors for measurement of knee kinematics in individuals with knee osteoarthritis: A technical report

FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment.

The assessment of heat transfer is a complex task, especially for operations in the oil and gas industry, due to the harsh and flammable workspace. In light of the limitations of conventional sensors in harsh environments, this paper presents a fiber Bragg grating (FBG)-based sensor for the assessment of the heat transfer rate (HTR) in different liquids. To better understand the phenomenon of heat distribution, a preliminary analysis is performed by constructing two similar scenarios: those with and without the thermal insulation of a styrofoam box. The results indicate the need for a minimum of thermal power to balance the generated heat with the thermal losses of the setup. In this minimum heat, the behavior of the thermal distribution changes from quadratic to linear. To assess such features, the estimation of the specific heat capacity and the thermal conductivity of water are performed from 3 W to 12 W, in 3 W steps, resulting in a specific heat of 1.144 cal/g °C and thermal conductivity of 0.5682 W/m °C. The calibration and validation of the HTR sensor is performed in a thermostatic bath. The method, based on the temperature slope relative to the time curve, allowed for the measurement of HTR in water and Kryo 51 oil, for different heat insertion configurations. For water, the HTR estimation was 308.782 W, which means an uncertainty of 2.8% with the reference value of the cooling power (300 W). In Kryo 51 oil, the estimated heat absorbed by the oil was 4.38 kW in heating and 718.14 kW in cooling.

What's Next For Wearable Sensing?

The commercial explosion of wearable sensing devices in the early 2010s forever changed the landscape of wearable computing. In a few short years, wrist-mounted devices such as wristbands and smart watches dominated the market.1 In 2017, this department featured an article titled “What will we wear after smartphones?” highlighting potential pathways for wearable computing as the early enthusiasm for commercial wearable sensors began to wane, and new form factors like on-skin devices gained traction in the research community.2 In the past few years, we have witnessed substantial changes in many of the domains discussed in that article. Sensor validation and comparison with other state of the art or reference systems has become of paramount importance in a saturated wearables market. Similarly, FDA approval or CE marking of smartphone or sensor-based medical applications is now a priority of many of the players targeting healthcare applications. For traditional form factors such as wristbands and other accessories, large improvements have also been made in hardware, thanks to further miniaturization and improved design (see Figure 1). Figure 1. Phone cameras, watches, and rings have become widespread sensing modalities for accurate monitoring of biometric data. Figure 2. Graphs show mean deviation from baseline (lines) with 95% CIs (shaded areas) for daily resting heart rate (RHR), sleep quantity, and step count during −7 to 133 days after symptom onset for COVID-19–positive versus COVID-19–negative participants (panels (a), (c), and (e)) and for COVID-19–positive participants grouped by mean change in RHR during days 28 to 56 after symptom onset (panels (b), (d), and (f)). Acquired with permission from Radin et al.8

Particle generation of low vapor pressure analytes for an on-demand aerosol standard

The continual or on-demand delivery of aerosol is important for sensor validation and the development of trace particle detection methodologies. The system presented here operates by introducing ae...

Validation of a Commercial Collar-Based Sensor for Monitoring Eating and Ruminating Behaviour of Dairy Cows.

Simple SummaryThis paper aims to validate a recently released commercial sensor (AFICollar®, Afimilk, Israel) to monitor dairy cows’ feeding and ruminating behavior. In order to evaluate the sensor’s performance under different feeding scenarios, the cows involved were divided into groups and fed different types of feed, including grazed pasture. Further, two version of the software used to convert raw data into behavioral information were tested and compared. Sensor data have been validated against visual observations, which served as the gold standard. Various statistical methods have been employed to assess sensor precision and accuracy. The results indicate that the sensor is adequately accurate for both feeding and ruminating time. However, the precision of the system appeared somewhat limited and should be improved.The use of sensor technologies to monitor cows’ behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar-based sensor system, the AFICollar® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm® 5.4 and the updated version AFIfarm® 5.5, were compared. The study involved twenty Holstein-Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland–Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: −19.60 min/h, 17.46 min/h; rumination: −15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively limited, and should be improved with further developments in the classification algorithm.

Test-retest reliability, validity, and responsiveness of a textile-based wearable sensor for real-time assessment of physical fatigue in construction bar-benders

While recent studies have shown that wearable sensing technology has the potential to facilitate the evaluation of physical fatigue, the reliability and validity of such measurements during construction tasks have not been reported. Thus, the primary objective of the current study is to establish absolute and relative reliability of textile-based wearable sensors to monitor physical fatigue during bar bending and fixing construction tasks. The secondary objective is to establish correlations between physiological parameters and subjective fatigue scores or blood lactate levels in order to demonstrate the convergent validity. Physiological parameters such as heart rate, breathing rate, and skin temperature were evaluated using textile-based wearable sensors. The test-retest reliability (intra-class correlation coefficient - ICC) values of the measured resting and working heart rate (ICC = 0.73 and 0.85), breathing rate (ICC = 0.78 and 0.82), and skin temperature (ICC = 0.68 and 0.77) were moderate to good and good, respectively. There were moderate to excellent correlations (r-values ranging from 0.414 to 0.940) between physiological parameters and subjective fatigue scores, although there were no correlations between any physiological parameters and blood lactate levels. Both laboratory and field data substantiated that the wearable sensing system has the potential to be a reliable noninvasive device to monitor physical fatigue (especially among workers at risk of sustaining fatigue-related injury due to advanced age, poor health, or job nature. However, because the current study validated the system exclusively in bar benders, additional research is necessary to confirm the findings in other construction workers.

Perspectives on the Calibration and Validation of Low-Cost Air Quality Sensors.

Perspectives on the Calibration and Validation of Low-Cost Air Quality Sensors.

Can Wearable Sweat Lactate Sensors Contribute to Sports Physiology?

The rise of wearable sensors to measure lactate content in human sweat during sports activities has attracted the attention of physiologists given the potential of these “analytical tools” to provide real-time information. Beyond the assessment of the sensing technology per se, which, in fact, has not rigorously been validated yet in controlled conditions, there are many open questions about the true usefulness of such wearable sensors in real scenarios. On the one hand, the evidence for the origin of sweat lactate (e.g., via the sweat gland, derivation from blood, or other alternative mechanisms), its high concentration (1–25 mM or even higher) compared to levels in the blood, and the possible correlation between different biofluids (particularly blood) is rather contradictory and generates vivid debate in the field. On the other hand, it is important to point out that accurate detection of sweat lactate is highly dependent on the procedure used to collect and/or reach the fluid, and this can likely explain the large discrepancies reported in the literature. In brief, this paper provides our vision of the current state of the field and a thoughtful evaluation of the possible reasons for present controversies, together with an analysis of the impact of wearable sweat lactate sensors in the physiological context. Finally, although there is not yet overwhelming scientific evidence to provide an unequivocal answer to whether wearable sweat lactate sensors can contribute to sports physiology, we still understand the importance to bring this challenging question up-front to create awareness and guidance in the development, validation, and implementation of wearable sensors.

Design and Validation of a Miniature Fiber Bragg Grating-Enabled High-Sensitivity Torque Sensor

This paper presents a Fiber Bragg Grating (FBG)-based torque sensor with high sensitivity and miniature size for MIS instruments and multi-finger hands. The proposed sensor design mainly consists of a torque-sensitive flexure structure and two diagonally arranged optical fibers embedded with FBG sensing elements. An improved hollow hexaform structure with flexible hinges has been utilized to design the torque-sensitive flexure with an excellent linear measurement range and a solid capacity to resist non-torsion loading. Two optical fibers with an FBG inscribed each adopt a two-point gluing configuration with a tightly stretched status, and they are arranged diagonally along the hyperboloid surface. This configuration can obtain improved sensitivity and resolution and avoid the drawbacks of FBG chirping and low repeatability. Finite element modeling (FEM)-enabled simulation has been implemented to perform design optimization for further sensor sensitivity improvement and performance investigation. The prototyped sensor has been calibrated and achieves a high resolution of 0.062N ·mm within [-100, 100N ·mm] and excellent linearity with a small linearity error of 0.68%. The crosstalk experiments on the bending moment of Mx and My and the axial force have been conducted to validate the anti-interference capacity on the basis of the small values of 0.47%, 0.31%, and 1.78%. The proposed design also supports convenient customization to achieve the adjustable measuring range and torque sensitivity, which has been validated by another sensor version with a resolution of 0.26 N·mm within [-500, 500N ·mm].

The Validity and Reliability of a Tire Pressure-Based Power Meter for Indoor Cycling.

The purpose of this study was to evaluate the validity and reliability of a tire pressure sensor (TPS) cycling power meter against a gold standard (SRM) during indoor cycling. Twelve recreationally active participants completed eight trials of 90 s of cycling at different pedaling and gearing combinations on an indoor hybrid roller. Power output (PO) was simultaneously calculated via TPS and SRM. The analysis compared the paired 1 s PO and 1 min average PO per trial between devices. Agreement was assessed by correlation, linear regression, inferential statistics, effect size, and Bland–Altman LoA. Reliability was assessed by ICC and CV comparison. TPS showed near-perfect correlation with SRM in 1 s (rs = 0.97, p < 0.001) and 1-min data (rs = 0.99, p < 0.001). Differences in paired 1 s data were statistically significant (p = 0.04), but of a trivial magnitude (d = 0.05). There was no significant main effect for device (F(1,9) = 0.05, p = 0.83, = 0.97) in 1 min data and no statistical differences between devices by trial in post hoc analysis (p < 0.01–0.98; d < 0.01–0.93). Bias and LoA were −0.21 ± 16.77 W for the 1 min data. Mean TPS bias ranged from 3.37% to 7.81% of the measured SRM mean PO per trial. Linear regression SEE was 7.55 W for 1 min TPS prediction of SRM. ICC3,1 across trials was 0.96. No statistical difference (p = 0.09–0.11) in TPS CV (3.6–5.0%) and SRM CV (4.3–4.7%). The TPS is a valid and reliable power meter for estimating average indoor PO for time periods equal to or greater than 1 min and may have acceptable sensitivity to detect changes under less stringent criteria (±5%).