Sensor-based Measurement Research Articles

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1hour data to cover time frequencies ranging from 1hour to 1month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Retraction Note: Validity and Reliability of Sensor-based Measures of Lower Limb Range of Motion in Soccer Players: a Cross-sectional Study.

Retraction Note: Validity and Reliability of Sensor-based Measures of Lower Limb Range of Motion in Soccer Players: a Cross-sectional Study.

PSIII-30 Development of an application programming interface (API) to automate downloading and processing of precision livestock data

Abstract Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology. For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance precision livestock technology. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on times scales relevant to researchers and livestock managers.

Perancangan Modifikasi Alat Antropometri sebagai Solusi Ketepatan Pengukuran Berbasis Sensor Ultrasonik

This research aims to create a modification of a baby anthropometric measuring tool including sensor-based measurements of the baby's length and weight as well as a head circumference measuring tool contained in one tool. 4 students from the Nursing Science and Electrical Engineering study programs participated in the development of this anthropometric tool with a sample of toddlers at the Integrated Service Post as a trial of the tool. This research uses a Research and Development (R&D) method approach. The tool used uses an electronic circuit based on an ultrasonic sensor and an Arduino nano microcontroller. This tool works automatically by connecting it to a power source then pressing the start button located outside the box marked by the monitor screen saying MODAS Baby as a sign that the test will start. When the sample is in the MODAS Baby box according to the procedure, this tool will automatically calculate the baby's length and weight. While waiting for the results to come out, you can measure the head circumference which is right under the baby's head. The results of the baby's body length and weight measurements are displayed on the LCD located on the surface of the box

Retracted Article: Validity and Reliability of Sensor-based Measures of Lower Limb Range of Motion in Soccer Players: a Cross-sectional Study.

A deficit in range of motion (ROM) is considered a risk factor for lower extremity injuries in soccer. Analog goniometers are used to measure the ROM of a joint. Sensor-based methods are increasingly being developed, but their quality of testing has not been sufficiently investigated. The purpose of this study was to determine the agreement and the intra- and inter-tester reliability of sensor-based lower extremity ROM measures in soccer players. 36 symptom-free male amateur soccer players (age: 26.3±4.7 years) were included in the study. Three out of five physiotherapists were randomly selected to perform the measurements. Two testers performed the measurements (1. ROM knee from sitting; 2. extension deficit knee from sitting; 3. ROM knee from standing; 4. ROM ankle dorsiflexion [DF] during lunge; 5. ROM ankle plantar flexion [PF] while sitting on a chair) with the digital sensor (index test). The third examiner performed the measurements with the analog goniometer using the neutral-zero method with the subjects in the supine position (reference standard). Pearson's correlation coefficient r, Bland-Altman analysis (BAA), and intraclass correlation coefficient (ICC) were used for statistical analysis (p≤0.05). Only measurements 4 and 5 showed acceptable mean differences of 8.4° (DF) and -10.2° (PF) in the BAA. Measurement 1 showed a moderate correlation (r=0.582). The sensor-based measurements of knee and ankle ROM revealed excellent intra- and inter-tester reliability (ICC =0.949-0.986; ICC =0.895-0.968). However, they showed limited agreement with the established reference standard used here, which can be explained by the different starting positions between the index test and the reference standard.

A DIC-UAV based displacement measurement technique for bridge field testing

Load rating of old or deteriorated bridges is performed either analytically or experimentally to determine their safe passing loads. Due to high costs of field operations and testing, analytical load rating is currently the preferred method of practice. To promote experimental load rating through bridge field testing, a novel displacement measurement technique as an alternative to conventional sensor-based measurements is proposed in the present study incorporating digital image correlation (DIC), unmanned aerial vehicle (UAV or drone), opensource computer programs, and cost-effective cameras. The development of the proposed DIC-UAV displacement measurement method including mission strategy, camera system trade-off, DIC software, drone, and payload configuration is discussed. Furthermore, the accuracy of the proposed technique is evaluated by performing more than 70 tests on a scaled truss bridge in a laboratory. The proposed DIC-UAV based displacement measurement strategy and computational tools were found feasible with submillimeter accuracies, significantly advancing the state-of-the-art methodologies.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

Measurement Of Ethanol Concentration in Liquid Organic Fertilizer Fermentation Emissions Using The MQ-8 Sensor

Ethanol is beneficial for the sterilization process and increases the rate of photosynthesis. Measurement of ethanol concentration is necessary because ethanol in high concentrations can cause several health problems, such as eye irritation, coughing, lacerations, and ineffective plant sterilization processes. Therefore, a sensor-based measurement system is needed to measure the total gas concentration in liquid organic fertilizer. This research aims to characterize the MQ-8 sensor and analyze the ethanol concentration for the fermentation process. The results of this study indicate that the MQ-8 sensor can work well in measuring the concentration of a single ethanol compound with an accuracy of 91%. The total concentration of ethanol gas in each sample liquid organic fertilizer is measured between 216 ppm to 1583 ppm, depending on the fermented plant.

Evaluation of sensor-based health monitoring in dairy cows: Exploiting rumination times for health alerts around parturition

The use of sensor-based measures of rumination time as a parameter for early disease detection has received significant attention in scientific research. This study aimed to assess the accuracy of health alerts triggered by a sensor-based accelerometer system within 2 different management strategies on a commercial dairy farm. Multiparous Holstein cows were enrolled during the dry-off period and randomly allocated to conventional (CON) or sensor-based (SEN) management groups at calving. All cows were monitored for disorders for a minimum of 10 DIM following standardized operating procedures (SOPs). The CON group (n = 199) followed an established monitoring protocol on the farm. The health alerts of this group were not available during the study but were later included in the analysis. The SEN group (n = 197) was only investigated when the sensor system triggered a health alert, and a more intensive monitoring approach according to the SOPs was implemented. To analyze the efficiency of the health alerts in detecting disorders, the sensitivity (SE) and specificity (SP) of health alerts were determined for the CON group. In addition, all cows were divided into 3 subgroups based on the status of the health alerts and their health status, to retrospectively compare the course of rumination time. Most health alerts (87%, n = 217) occurred on DIM 1. For the confirmation of diagnoses, health alerts showed SE and SP levels of 71% and 47% for CON cows. In SEN cows, a SE of 71% and 75% and SP of 48% and 43% were found for the detection of ketosis and hypocalcemia, respectively. The rumination time of the subgroups was affected by DIM and the interaction between DIM and the status of health alert and health condition.

Sensor-Based Measurement Method to Support the Assessment of Robot-Assisted Radiofrequency Ablation.

Digital surgery technologies, such as interventional robotics and sensor systems, not only improve patient care but also aid in the development and optimization of traditional invasive treatments and methods. Atrial Fibrillation (AF) is the most common cardiac arrhythmia with critical clinical relevance today. Delayed intervention can lead to heart failure, stroke, or sudden cardiac death. Although many advances have been made in the field of radiofrequency (RF) catheter ablation (CA), it can be further developed by incorporating sensor technology to improve its efficacy and safety. Automation can be utilized to shorten the duration of RF ablation, provided that the interactions between the tissue and the RF tools are well understood and adequately modeled. Further research is needed to develop the optimal catheter design. This paper describes the systematic methodology developed to support robot-assisted RF CA characterization measurements. The article describes the custom instruments developed for the experiments, particularly the contact force limiter, the measurement procedure, and the evaluation of the results, as enablers for new results. The aim was to establish an objective, repeatable, robust measurement method and adjacent procedure.

Measurement of Transient Overvoltages by Capacitive Electric Field Sensors.

The accurate measurement and the investigation of electromagnetic transients are becoming more important, especially with the increasing integration of renewable energy sources into the power grid. These sources introduce new transient phenomena due to the extensive use of power electronics. To achieve this, the measurement devices must have a broadband response capable of measuring fast transients. This paper presents a capacitive electric field sensor-based measurement system to measure transient overvoltages in high-voltage substations. The concept and design of the measurement system are first presented. Then, the design and concept are validated using tests performed in a high-voltage laboratory. Afterwards, two different calibration techniques are discussed: the simplified method (SM) and the coupling capacitance compensation (CCC) method. Finally, three recorded transients are evaluated using the calibration methods. The investigation revealed that the SM tends to overestimate the maximum overvoltage, highlighting the CCC method as a more suitable approach for calibrating transient overvoltage measurements. This measurement system has been validated using various measurements and can be an efficient and flexible solution for the long-term monitoring of transient overvoltages in high-voltage substations.

Assessment of Self-report, Palpation, and Surface Electromyography Dataset During Isometric Muscle Contraction

Measuring muscle fatigue involves assessing various components within the motor system. While subjective and sensor-based measures have been proposed, a comprehensive comparison of these assessment measures is currently lacking. This study aims to bridge this gap by utilizing three commonly used measures: participant self-reported perceived muscle fatigue scores, a sports physiotherapist’s manual palpation-based muscle tightness scores, and surface electromyography sensors. Compensatory muscle fatigue occurs when one muscle group becomes fatigued, leading to the involvement and subsequent fatigue of other muscles as they compensate for the workload. The evaluation of compensatory muscle fatigue focuses on nine different upper body muscles selected by the sports physiotherapist. With a cohort of 30 male subjects, this study provides a valuable dataset for researchers and healthcare practitioners in sports science, rehabilitation, and human performance. It enables the exploration and comparison of diverse methods for evaluating different muscles in isometric contraction.

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients’ Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r2, 0.692; RMSE, 1.33). The ability to measure the impact of the disease during daily life—through objective and remote digital outcomes—paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials.

Inertial Sensor Gait Analysis of Trendelenburg Gait in Patients Who Have Hip Osteoarthritis

BackgroundGait abnormalities such as Trendelenburg gait (TG) in patients who have hip osteoarthritis (OA) have traditionally been evaluated using clinicians’ visual assessment. Recent advances in portable inertial gait sensors offer more sensitive, quantitative methods for gait assessment in clinical settings. This study sought to compare sensor-derived metrics in a cohort of hip OA patients when stratified by clinical TG severity. MethodsThere were 42 patients who had hip OA and were grouped by TG severity (mild, moderate, and severe) through visual assessment by a single arthroplasty surgeon who had > 30 years of experience. After informed consent, wireless inertial sensors placed at the midpoint of the intercristal line collected gait parameters including pelvic shift, support time, toe-off symmetry, impact, and cadence. Clinical data on hip strength, range of motion, and Kellgren-Lawrence grade were collected. ResultsWorsening TG severity had a higher mean Kellgren-Lawrence grade (2.5 versus 3.2 versus 3.4; P = .014) and reduced passive hip abduction (P = .004). Severe TG group demonstrated predominantly contralateral pelvic shift (n = 9 of 10, 90.0%), while ipsilateral shift was more frequently detected in moderate (n = 10 of 18, 55.6%) and mild groups (n = 9 of 14, 64.3%; P = .021). Contralateral single support time bias was greatest in severe TG (35.7% versus 50.0 versus 90.0%; P = .027). Asymmetric toe-off, impact, and support times were observed in all groups. ConclusionsTraditional understanding of TG is that truncal shift occurs to the ipsilateral side. Using sensor-based measurements, the present study demonstrates a shift of the weight-bearing axis toward the contralateral side with increasing TG severity, which has not been previously described. Inertial sensors are feasible, quantitative gait measuring tools, and may reveal subtle patterns not readily discernible by traditional methods.

Identifying Clinical Phenotypes in People Who Are Hispanic/Latino With Chronic Low Back Pain: Use of Sensor-Based Measures of Posture and Movement, Pain, and Psychological Factors.

The aim of this study was to identify clinical phenotypes using sensor-based measures of posture and movement, pain behavior, and psychological factors in Hispanic/Latino people with chronic low back pain (CLBP). Baseline measures from an ongoing clinical trial were analyzed for 81 Hispanic/Latino people with CLBP. Low back posture and movement were measured using commercial sensors during in-person testing and 8hours of ecological monitoring. Magnitude, frequency, and duration of lumbar movements, sitting and standing postures were measured. Movement-evoked pain was assessed during in-person movement testing. Psychological measures included the Pain Catastrophizing Scale and the Fear Avoidance Beliefs Questionnaire. Random forest analysis was conducted to generate 2 groups and identify important variables that distinguish groups. Group differences in demographics, pain, psychological, and posture and movement variables were examined using t-tests and chi-square analyses. Two subgroups of Hispanic/Latino people with CLBP were identified with minimal error (7.4% misclassification ["out-of-bag" error]). Ecological posture and movement measures best distinguished groups, although most movement-evoked pain and psychological measures did not. Group 1 had greater height and weight, lower movement frequency, more time in sitting, and less time in standing. Group 2 had a greater proportion of women than men, longer low back pain duration, higher movement frequency, more time in standing, and less time in sitting. Two distinct clinical phenotypes of Hispanic/Latino people with CLBP were identified. One group was distinguished by greater height and weight and more sedentary posture and movement behavior; the second group had more women, longer duration of low back pain, higher lumbar spine movement frequency, and longer duration of standing postures. Ecological measures of posture and movement are important for identifying 2 clinical phenotypes in Hispanic/Latino people with CLBP and may provide a basis for a more personalized plan of care. Wearable sensors were used to measure low back posture and movement in Hispanic/Latino people with chronic low back pain. These posture and movement measures helped to identify 2 different clinical subgroups that will give physical therapists more information to better personalize treatment for chronic low back pain in Hispanic/Latino patients.

Coil Antenna Sensor-Based Measurement Method to Online Detect Partial Discharge in Distributed Power Networks

Coil Antenna Sensor-Based Measurement Method to Online Detect Partial Discharge in Distributed Power Networks

Development of an application programming interface to automate downloading and processing of precision livestock data

Abstract Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology (PLT). For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance PLT. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on time scales relevant to researchers and livestock managers.

Measuring the Macrotexture of Pavement Surface Using an Image Processing Technique

The objective of this study was to propose an image processing-based index for measuring pavement macrotexture at the network level. This index enables macrotexture to be measured easily and inexpensively using images collected at traffic speed. The study involved collecting pavement surface images at a constant traffic speed on a test section specifically designed and constructed for this purpose, with three surface course mixes that are commonly used in India, namely, bituminous concrete, stone matrix asphalt, and gap-graded rubberized bituminous mix. Additionally, macrotexture data with regard to mean texture depth (MTD) from the sand patch test and mean profile depth (MPD) from laser sensor-based measurements were obtained at the locations where the images were captured. The surface macrotexture index (SMI), which was derived from wavelet transform-based image texture analysis, was compared with the MTD and MPD data. The results showed that the SMI is an accurate indicator of pavement surface macrotexture. In addition, the study showcased the application of an unsupervised machine learning algorithm to identify and replace outliers in the SMI data that resulted from isolated spots with dirt, pavement markings, and wet surfaces. The research also established relationships between the proposed SMI and MTD/MPD. These relationships are reliable and can be used to predict the commonly used pavement surface construction quality measure MTD and the network-level skid resistance indicator MPD.

Insights from a Patent Portfolio Analysis on Sensor Technologies for Measuring Fruit Properties

A patent portfolio focusing on sensors for the measurement of fruit properties was generated and analyzed with the aim of contributing to a better understanding of the trends in the development and application of sensors intended for measuring fruit properties and their changes. A patent portfolio of 189 patents, utility models and patent applications was formed. Three groups of patents were identified: (i) sensor-based measurement of individual parameters, (ii) multisensor solutions for the simultaneous monitoring of multiple relevant aspects and (iii) solutions integrating sensor-derived data with artificial intelligence tools and techniques. The analysis of the patent portfolio pointed out the main driving forces of technology strengthening in the field of fruit property measurement. The development of sensing technologies enables the real-time, rapid and cost-effective determination of ever-increasing and more sophisticated sets of fruit properties and environmental conditions. Solutions integrating different sensing technologies into multisensor systems for monitoring fruit quality, ripening or freshness as holistic concepts opens avenues for the introduction of a new approach to fresh produce management. Increasing numbers of solutions introducing the application of artificial intelligence tools such as computer vision, machine learning and deep learning into the fresh produce supply chain contribute to the possibilities of substituting human decision-making at points of relevance for fresh produce management with optimal evidence-based solutions.

An explanation for the accuracy of sensor-based measures of energy intake: Amount of food consumed matters more than dietary composition

Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.g., doubly labeled water). Thus, researchers have sought to leverage inexpensive and low-burden technologies such as wearable sensors for EI estimation. Paradoxically, one such methodology that estimates EI via smartwatch-based bite counting has demonstrated high accuracy in laboratory and free-living studies, despite only measuring the amount, not the composition, of food consumed. This secondary analysis sought to further explore this phenomenon by evaluating the degree to which EI can be explained by a sensor-based estimate of the amount consumed versus the energy density (ED) of the food consumed. Data were collected from 82 adults in free-living conditions (51.2% female, 31.7% racial and/or ethnic minority; Mage = 33.5, SD = 14.7) who wore a bite counter device on their wrist and used smartphone app to implement the Remote Food Photography Method (RFPM) to assess EI and ED for two weeks. Bite-based estimates of EI were generated via a previously validated algorithm. At a per-meal level, linear mixed effect models indicated that bite-based EI estimates accounted for 23.4% of the variance in RFPM-measured EI, while ED and presence of a beverage accounted for only 0.2% and 0.1% of the variance, respectively. For full days of intake, bite-based EI estimates and ED accounted for 41.5% and 0.2% of the variance, respectively. These results help to explain the viability of sensor-based EI estimation even in the absence of information about dietary composition.