Low-Cost Sensor Node for Air Quality Monitoring: Field Tests and Validation of Particulate Matter Measurements.

Air pollution is still a major public health issue, which makes monitoring air quality a necessity. Mobile, low-cost air quality measurement devices can potentially deliver more coherent data for a region or municipality than stationary measurement stations are capable of due to their improved spatial coverage. In this study, air quality measurements obtained during field tests of our low-cost air quality sensor node (sensor-box) are presented and compared to measurements from the regional air quality monitoring network. The sensor-box can acquire geo-tagged measurements of several important pollutants, as well as other environmental quantities such as light and sound. The field test consists of sensor-boxes mounted on utility vehicles operated by municipalities located in Central Switzerland. Validation is performed against a measurement station that is part of the air quality monitoring network of Central Switzerland. Often not discussed in similar studies, this study tests and discusses several data filtering methods for the removal of outliers and unfeasible values prior to further analysis. The results show a coherent measurement pattern during the field tests and good agreement to the reference station during the side-by-side validation test.

An Automotive LiDAR Performance Test Method in Dynamic Driving Conditions.

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high level of autonomous driving functions, as well as a standard Advanced Driver Assistance System (ADAS). LiDAR capabilities and signal repeatabilities under extreme weather conditions are of utmost concern in terms of the redundancy design of automotive sensor systems. In this paper, we demonstrate a performance test method for automotive LiDAR sensors that can be utilized in dynamic test scenarios. In order to measure the performance of a LiDAR sensor in a dynamic test scenario, we propose a spatio-temporal point segmentation algorithm that can separate a LiDAR signal of moving reference targets (car, square target, etc.), using an unsupervised clustering method. An automotive-graded LiDAR sensor is evaluated in four harsh environmental simulations, based on time-series environmental data of real road fleets in the USA, and four vehicle-level tests with dynamic test cases are conducted. Our test results showed that the performance of LiDAR sensors may be degraded, due to several environmental factors, such as sunlight, reflectivity of an object, cover contamination, and so on.

Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management.

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures.

Validation of 3-Space Wireless Inertial Measurement Units Using an Industrial Robot.

Inertial Measurement Units (IMUs) are beneficial for motion tracking as, in contrast to most optical motion capture systems, IMU systems do not require a dedicated lab. However, IMUs are affected by electromagnetic noise and may exhibit drift over time; it is therefore common practice to compare their performance to another system of high accuracy before use. The 3-Space IMUs have only been validated in two previous studies with limited testing protocols. This study utilized an IRB 2600 industrial robot to evaluate the performance of the IMUs for the three sensor fusion methods provided in the 3-Space software. Testing consisted of programmed motion sequences including 360° rotations and linear translations of 800 mm in opposite directions for each axis at three different velocities, as well as static trials. The magnetometer was disabled to assess the accuracy of the IMUs in an environment containing electromagnetic noise. The Root-Mean-Square Error (RMSE) of the sensor orientation ranged between 0.2° and 12.5° across trials; average drift was 0.4°. The performance of the three filters was determined to be comparable. This study demonstrates that the 3-Space sensors may be utilized in an environment containing metal or electromagnetic noise with a RMSE below 10° in most cases.

Wearable sensors for personal temperature exposure assessments: A comparative study.

The impacts of heat on human health has sparked research on different approaches to measure, map, and predict heat exposure at more accurate and precise spatiotemporal scales. Personal heat sensor studies rely on small sensors that can continuously measure ambient temperatures as individuals move through time and space. The comparison between different types of sensors and sensor placements have yet to be fully researched. The objective of this study is to assess the validity of personal ambient temperature sensors. To accomplish this objective, we evaluated the performance of multiple low-cost wearable sensors (HOBOs, iButton Thermochrons, iButton Hygrochrons, and Kestrel DROP D3FW Fire) for measuring ambient temperature in a (1) field exposure study by varying the placement on human subjects and in a (2) field calibration study by co-locating sensors with fixed site weather station monitors. A secondary aim involved investigating consensus between validation metrics that can be used in future sensor comparison studies. Bland-Altman analysis, correlation coefficients, and index of agreement statistics were used to quantify the difference between sensor and weather station ambient temperature measurements. Results demonstrated significant differences in measured temperatures for sensors based on sensor type and placement on participants. Future research should account for the differences in personal ambient temperature readings based on sensor type and placement.

Determination of the Porosity Distribution during an Erosion Test Using a Coaxial Line Cell.

The detection of porosity changes within a soil matrix caused by internal erosion is beneficial for a better understanding of the mechanisms that induce and maintain the erosion process. In this paper, an electromagnetic approach using Spatial Time Domain Reflectometry (STDR) and a transmission line model is proposed for this purpose. An original experimental setup consisting of a coaxial cell which acts as an electromagnetic waveguide was developed. It is connected to a transmitter/receiver device both measuring the transmitted and corresponding reflected electromagnetic pulses at the cell entrance. A gradient optimization method based on a computational model for simulating the wave propagation in a transmission line is applied in order to reconstruct the spatial distribution of the soil dielectric permittivity along the cell based on the measured signals and an inversion algorithm. The spatial distribution of the soil porosity is deduced from the dielectric permittivity profile by physically based mixing rules. Experiments were carried out with glass bead mixtures of known dielectric permittivity profiles and subsequently known spatial porosity distributions to validate and to optimize both, the proposed computational model and the inversion algorithm. Erosion experiments were carried out and porosity profiles determined with satisfying spatial resolution were obtained. The RMSE between measured and physically determined porosities varied among less than 3% to 6%. The measurement rate is sufficient to be able to capture the transient process of erosion in the experiments presented here.

Preliminary Validity and Acceptability of Motion Tape for Measuring Low Back Movement: Mixed Methods Study.

BACKGROUND: Low back pain (LBP) is a significant public health problem that can result in physical disability and financial burden for the individual and society. Physical therapy is effective for managing LBP and includes evaluation of posture and movement, interventions directed at modifying posture and movement, and prescription of exercises. However, physical therapists have limited tools for objective evaluation of low back posture and movement and monitoring of exercises, and this evaluation is limited to the time frame of a clinical encounter. There is a need for a valid tool that can be used to evaluate low back posture and movement and monitor exercises outside the clinic. To address this need, a fabric-based, wearable sensor, Motion Tape (MT), was developed and adapted for a low back use case. MT is a low-profile, disposable, self-adhesive, skin-strain sensor developed by spray coating piezoresistive graphene nanocomposites directly onto commercial kinesiology tape. OBJECTIVE: The objectives of this study were to (1) validate MT for measuring low back posture and movement and (2) assess the acceptability of MT for users. METHODS: A total of 10 participants without LBP were tested. A 3D optical motion capture system was used as a reference standard to measure low back kinematics. Retroreflective markers and a matrix of MTs were placed on the low back to measure kinematics (motion capture) and strain (MT) simultaneously during low back movements in the sagittal, frontal, and axial planes. Cross-correlation coefficients were calculated to evaluate the concurrent validity of MT strain in reference motion capture kinematics during each movement. The acceptability of MT was assessed using semistructured interviews conducted with each participant after laboratory testing. Interview data were analyzed using rapid qualitative analysis to identify themes and subthemes of user acceptability. RESULTS: Visual inspection of concurrent MT strain and kinematics of the low back indicated that MT can distinguish between different movement directions. Cross-correlation coefficients between MT strain and motion capture kinematics ranged from -0.915 to 0.983, and the strength of the correlations varied across MT placements and low back movement directions. Regarding user acceptability, participants expressed enthusiasm toward MT and believed that it would be helpful for remote interventions for LBP but provided suggestions for improvement. CONCLUSIONS: MT was able to distinguish between different low back movements, and most MTs demonstrated moderate to high correlation with motion capture kinematics. This preliminary laboratory validation of MT provides a basis for future device improvements, which will also involve testing in a free-living environment. Overall, users found MT acceptable for use in physical therapy for managing LBP.

Estimation of spatial-temporal hand motion parameters in rehabilitation using a low-cost noncontact measurement system.

Data collection and analysis are commonly used in a rehabilitation process to measure performances of the treatment. There is a lack of studies on the rehabilitation process monitored by a user-friendly interface. A low-cost system is developed in this research to assist users and therapists to measure hand motions and analyse important data of hand joints. The system consists of modules of data capturing, data analysis, and user interface. A Leap Motion sensor is used to capture joint positions of hand motions. Signal processing and wavelet de-noising methods are developed to improve accuracy of the data analysis. The user interface is designed using the Unity software to show graphical information of joint positions and motion parameters. The system has features of noncontact measurements, interactive environment, analysing and recording temporal data of motion parameters of hands. The system is validated by a gold standard motion capturing system. Case studies show effectiveness of the proposed system.

Assessment of Melamine in Different Water Samples with ZnO-doped Co3 O4 Nanoparticles on a Glassy Carbon Electrode by Differential Pulse Voltammetry.

In this investigation, a melamine electrochemical sensor has been developed by using wet-chemically synthesized low-dimensional aggregated nanoparticles (NPs) of ZnO-doped Co3 O4 as sensing substrate that were decorated onto flat glassy carbon electrode (GCE). The characterization of NPs such as UV-Vis, FTIR, XRD, XPS, EDS, and FESEM was done for detailed investigations in optical, functional, structural, elemental, and morphological analyses. The ZnO-doped Co3 O4 NPs decorated GCE was used as a sensing probe to analyze the target chemical melamine in a phosphate buffer at pH 5.7 by applying differential pulse voltammetry (DPV). It exhibited good performances in terms of sensor analytical parameters such as large linear dynamic range (LDR; 0.15-1.35 mM) of melamine detection, high sensitivity (80.6 µA mM-1 cm-2 ), low limit of detection (LOD; 0.118±0.005 mM), low limit of quantification (LOQ; 0.393 mM), and fast response time (30 s). Besides this, the good reproducibility (in several hours) and repeatability were investigated under identical conditions. Moreover, it was implemented to measure the long-time stability, electron mobility, less charge-transfer resistance, and analyzed diffusion-controlled process for the oxidation reaction of the NPs assembled working GCE electrode, which showed outstanding chemical sensor performances. For validation, real environmental samples were collected from various water sources and investigated successfully with regard to the reliability of the selective melamine detection with prepared NPs coated sensor probe. Therefore, this approach might be introduced as an alternative route in the sensor technology to detect selectively unsafe chemicals by an electrochemical method with nanostructure-doped materials for the safety of environmental, ecological, healthcare fields in a broad scale.

Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.

Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants' food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light's kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters' annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.

Validating a Commercial Device for Continuous Activity Measurement in the Older Adult Population for Dementia Management.

With the introduction of the large number of fitness devices on the market, there are numerous possibilities for their use in managing chronic diseases in older adults. For example, monitoring people with dementia using commercially available devices that measure heart rate, breathing rate, lung volume, step count, and activity level could be used to predict episodic behavioral and psychological symptoms before they become distressing or disruptive. However, since these devices are designed primarily for fitness assessment, validation of the sensors in a controlled environment with the target cohort population is needed. In this study, we present validation results using a commercial fitness tracker, the Hexoskin sensor vest, with thirty-one participants aged 65 and older. Estimated physiological measures investigated in this study are heart rate, breathing rate, lung volume, step count, and activity level of the participants. Findings indicate that while the processed step count, heart rate, and breathing rate show strong correlations to the clinically accepted gold standard values, lung volume and activity level do not. This indicates the need to proceed cautiously when making clinical decisions using such sensors, and suggests that users should focus on the three strongly correlated parameters for further analysis, at least in the older population. The use of physiological measurement devices such as the Hexoskin may eventually become a non-intrusive way to continuously assess physiological measures in older adults with dementia who are at risk for distressing behavioral and psychological symptoms.

Statistical approach of assessing the reliability of glucose sensors: the GLYCENSIT procedure.

BACKGROUND: In healthcare, patients with diabetes are instructed on how to apply intensified insulin therapy in an optimal manner. Tight blood glucose control is also performed on patients treated in the intensive care unit (ICU). Different blood glucose meters and glucose monitoring systems (GMSs) are used to achieve this goal, and some may lack reliability. METHODS: The GLYCENSIT procedure is a statistical assessment tool we are proposing for evaluating the significant difference of paired glucose measurements. The performance of the GlucoDay system in the ICU is analyzed with GLYCENSIT. RESULTS: THE GLYCENSIT ANALYSIS COMPRISES THREE PHASES: testing possible persistent measurement behavior as a function of the glycemic range, testing the number of measurement errors with respect to a standard criterion for binary assessment of glucose sensors, and computing the tolerance intervals that indicate possible test sensor deviations for new observations. The probability of the tolerance intervals directly reflects the number of samples and additionally improves current assessment techniques. The method can be tuned according to the clinician's preferences regarding significance level, tolerance level, and glycemic range cutoff values. The measurement behavior of the GlucoDay sensor is found to be persistent but inaccurate and returns wide tolerance intervals, suggesting that the GlucoDay sensor may not be sufficiently reliable for glycemia control in the ICU. CONCLUSIONS: The GLYCENSIT procedure aims to serve as statistical guide for clinicians in the assessment of glucose sensor devices.