Triaxial Sensor Research Articles

Analysis of orientation errors in triaxial fluxgate sensors and research on their calibration methods

Abstract. Three-axis magnetic flux gate sensors are widely used in Chinese geomagnetic observatories, but due to their directional errors it is necessary to study error correction methods to improve measurement accuracy. Firstly, the mechanism of directional errors produced by three-axis magnetic flux gate sensors is analyzed, followed by the development of measurement tools for conducting directional error measurement experiments on the high-precision three-axis magnetic flux gate sensors of the Chinese FGM-01 series. Experimental results show that correcting the Z-axis and D-axis directional errors is essential. The observation data after error correction, whether in terms of the standard deviation of its all-day baseline values or the relative difference magnitude with the reference instrument, significantly decrease, demonstrating the clear correction effect and proving the effectiveness of this correction method.

A Robust Tri-Electromagnet-Based 6-DoF Pose Tracking System Using an Error-State Kalman Filter.

Magnetic pose tracking is a non-contact, accurate, and occlusion-free method that has been increasingly employed to track intra-corporeal medical devices such as endoscopes in computer-assisted medical interventions. In magnetic pose-tracking systems, a nonlinear estimation algorithm is needed to recover the pose information from magnetic measurements. In existing pose estimation algorithms such as the extended Kalman filter (EKF), the 3-DoF orientation in the S3 manifold is normally parametrized as unit quaternions and simply treated as a vector in the Euclidean space, which causes a violation of the unity constraint of quaternions and reduces pose tracking accuracy. In this paper, a pose estimation algorithm based on the error-state Kalman filter (ESKF) is proposed to improve the accuracy and robustness of electromagnetic tracking systems. The proposed system consists of three electromagnetic coils for magnetic field generation and a tri-axial magnetic sensor attached to the target object for field measurement. A strategy of sequential coil excitation is developed to separate the magnetic fields from different coils and reject magnetic disturbances. Simulation and experiments are conducted to evaluate the pose tracking performance of the proposed ESKF algorithm, which is also compared with standard EKF and constrained EKF. It is shown that the ESKF can effectively maintain the quaternion unity and thus achieve a better tracking accuracy, i.e., a Euclidean position error of 2.23 mm and an average orientation angle error of 0.45°. The disturbance rejection performance of the electromagnetic tracking system is also experimentally validated.

The Short Physical Performance Battery does not correlate with daily life gait quality and quantity in community-dwelling older adults with an increased fall risk

BackgroundBoth the Short Physical Performance Battery (SPPB) and daily life gait quality and quantity obtained from wearable sensors are used to measure functional status in older adults. It is generally assumed that they are interrelated and exchangeable, but this has not yet been established. Interchangeability of these measures would pave the way for remote monitoring of functional status. Research QuestionAre the SPPB and daily life gait quality and quantity measures correlated in community-dwelling older adults? MethodsThe SPPB and gait quality and quantity data of 229 community-dwelling adults of 65 years or older were collected. The SPPB is a combined score of the Three Stage Balance test, Four Meter Walk test, and Five Times Sit to Stand test and ranges from 0 to 12. Participants wore a tri-axial inertial sensor for one week to assess gait quality (e.g. gait stability and smoothness) and quantity (e.g. number of strides). Correlation coefficients between SPPB scores and gait quality and quantity measures were assessed using Spearman’s correlation. ResultsThe median age of the study population was 76.2 years (IQR 72.6–81.0), and 76 % were women (n=175). The median SPPB score was 10 (IQR 8–11). Spearman's correlation coefficients between the SPPB and gait quality and quantity measures were all below 0.3. SignificanceA possible explanation for the observed weak correlations is that the SPPB reflects one’s maximal capacity, while gait quality and quantity reflect the submaximal performance in daily life. The SPPB and gait quality and quantity seem therefore distinct constructs with complementary value, rather than interchangeable. A more comprehensive understanding of functional status might be achieved by combining the SPPB assessment of standardized activities with the evaluation of inertial sensor measurements obtained during daily life activities.

Sea Horse Optimization-Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition.

Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients' medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization-Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.

High-impact dynamic loading method for calibration of triaxial acceleration sensors

Abstract In this study, a high-impact dynamic loading device was designed to generate a three-dimensional pulse excitation signal with high intensity shock acceleration and achieve triaxial synchronous calibration of a triaxial acceleration sensor. A light-gas gun interior ballistic model and a sensor mechanical response model were developed, and the relationships between the bullet impact velocity, barrel length, and initial chamber pressure were obtained. Additionally, the transformation relationship of the sensor’s triaxial acceleration in different coordinate systems was derived. Based on the stress wave theory and the finite element method, the influence of the bullet impact velocity on the variation pulse, and different slope and deflection angles on the triaxial acceleration were analyzed. By optimizing the parameter design, machining the prototype, and conducting high-impact dynamic loading tests, the results showed that the deviation between theoretical and measured values of the generated triaxial acceleration signal was small, and the maximum deviation was less than 4%. This indicated that the proposed high-impact dynamic loading device satisfied the calibration requirements for calibrating triaxial acceleration sensors, which can generate a three-dimensional acceleration with a peak value of not less than 700 000 m s−2.

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals

People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning.

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Cavitation fault diagnosis of centrifugal pump based on RIME-SDAE

In order to fully extract the fault characteristics of different cavitation fault states of centrifugal pumps, reduce the influence of hyperparameter settings on the identification and classification results of machine learning algorithms, this paper designs a network model based on the rime optimization algorithm (RIME) to improve the stacked denoising autoencoder (SDAE) network, which is named as RIME-SDAE. First of all, Singular Value Decomposition (SVD) is used to denoise the X, Y and Z signals of the triaxial vibration sensor, and time-domain, frequency-domain and time-frequency features are extracted to construct a signal feature set. Secondly, the three-dimensional feature indicators are analyzed and selected to be merged into the input dataset, and SDAE is trained with the RIME is used to determine the model parameters of SDAE synchronously. The feasibility of the proposed method is verified by the signals collected in the actual test, and the test results show that the accuracy on the test set reaches more than 98 %.

THE IMPACT OF ACCELEROMETER MOUNTING ON THE CORRECTNESS OF THE RESULTS OBTAINED IN NDT-TYPE TESTS

The paper attempts to evaluate the effect of acceleration sensor mounting on the recorded vibration time course. The study used a prepared model of a railroad rail and triaxial acceleration sensors. Three non-invasive methods of mounting the vibration acceleration transducers were selected for analysis: mounting with cyanoacrylate glue, mounting with a magnet, and mounting with wax. The information capacity of the signals was analyzed based on the recorded time waveforms, which totaled more than 90, and their vibration signals. The analysis compared both the basic parameters of the signals (maximum amplitudes and root mean square values) and a comprehensive analysis of the signals using the short-time Fourier transform method, as well as the wavelet transform. The results show significant differences in the recorded signal parameters depending on how the acceleration sensor is mounted, as well as the axis analyzed. The differences can negatively affect the correctness of the measurements made and falsify the picture of the real condition.

Sensor-Based Quantitative Assessment of Children's Fine Motor Competence: An Instrumented Version of the Placing Bricks Test.

The assessment of fine motor competence plays a pivotal role in neuropsychological examinations for the identification of developmental deficits. Several tests have been proposed for the characterization of fine motor competence, with evaluation metrics primarily based on qualitative observation, limiting quantitative assessment to measures such as test durations. The Placing Bricks (PB) test evaluates fine motor competence across the lifespan, relying on the measurement of time to completion. The present study aims at instrumenting the PB test using wearable inertial sensors to complement PB standard assessment with reliable and objective process-oriented measures of performance. Fifty-four primary school children (27 6-year-olds and 27 7-year-olds) performed the PB according to standard protocol with their dominant and non-dominant hands, while wearing two tri-axial inertial sensors, one per wrist. An ad hoc algorithm based on the analysis of forearm angular velocity data was developed to automatically identify task events, and to quantify phases and their variability. The algorithm performance was tested against video recordings in data from five children. Cycle and Placing durations showed a strong agreement between IMU- and Video-derived measurements, with a mean difference <0.1 s, 95% confidence intervals <50% median phase duration, and very high positive correlation (ρ > 0.9). Analyzing the whole population, significant differences were found for age, as follows: six-year-olds exhibited longer cycle durations and higher variability, indicating a stage of development and potential differences in hand dominance; seven-year-olds demonstrated quicker and less variable performance, aligning with the expected maturation and the refined motor control associated with dominant hand training during the first year of school. The proposed sensor-based approach allowed the quantitative assessment of fine motor competence in children, providing a portable and rapid tool for monitoring developmental progress.

Monocular vision-based dynamic calibration method for determining the sensitivities of low-frequency tri-axial vibration sensors.

Low-frequency vibrations exist widely in the natural environment and in human activities. Low-frequency tri-axial vibration sensors are enormously applied in the fields of seismic monitoring, building structure health monitoring, aerospace navigating, etc. Their sensitivity calibration accuracy directly determines whether their applications can work reliably. Although the laser interferometry recommended by the International Standardization Organization (ISO) is commonly used to achieve the vibration calibration, it suffers from the shortages of low-frequency range, high cost, low efficiency, and limited applicable environment. In this study, a novel monocular vision-based dynamic calibration method is proposed, which determines the whole sensitivities of tri-axial sensors by the monocular vision method to accurately measure the spatial input excitation. This method improves the calibration performance by eliminating the installation error and enhancing calibration efficiency via decreasing reinstallations. The experimental results compared with the laser interferometry demonstrate that the investigated method can obtain similar calibration accuracy in the range of 0.16-2 Hz with more efficiency. The corresponding maximum relative deviations of X-, Y-, and Z-axial sensitivities were approximately 2.5%, 1.8%, and 0.4%. In addition, the maximum relative standard deviation of the investigated method was only about 0.3% in this range.

IMU-Based Approach to Detect Spastic Cerebral Palsy in Infants at Early Stages

INTRODUCTION: Cerebral Palsy (CP) is a non-progressive neurological disorder affecting muscle control in early childhood, leading to permanent alterations in body posture and movement. Early identification is crucial for accurate diagnosis and therapy-based interventions. In recent years, an automated monitoring system has been developed to facilitate the health assessment of infants, enabling early recognition of neurological dysfunctions in high-risk infants. However, the interpretation of these assessments lacks standardization and is subject to examiner bias.&#x0D; OBJECTIVES: Many infants with CP exhibit increased tonic stretch reflexes due to Upper Motor Neuron Syndrome (UMNS), resulting from motor neuron damage that disrupts muscle signalling.&#x0D; METHOD: To detect abnormal muscle reactions, our team employed an Inertial Measurement Unit (IMU) sensor, comprising three tri-axial sensors (accelerometer, gyroscope, magnetometer) that capture movement data continuously and unobtrusively. IMU sensors are compact, cost-effective, and have low processing requirements, requiring attachment to the infant's body to measure inter-body part angles. Our team analyzed muscle activity and posture using IMU sensors, collecting tri-axial data from 43 infants in real-time. Additional factors like age, stride length, and leg length were incorporated into the dataset.&#x0D; RESULTS: Our team has applied various supervised machine learning approaches to predict CP in infants due to the limited dataset size, validating models through k-fold cross-validation. Among the models, Naive Bayes (NB) outperformed Logistic Regression (LR), Decision Tree (DT), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM), achieving an accuracy of 88%. CONCLUSION: This research contributes to the early detection and intervention of CP in infants, potentially improving their long-term outcomes.

Calibration System and Algorithm Design for a Soft Hinged Micro Scanning Mirror With a Triaxial Hall Effect Sensor

Calibration System and Algorithm Design for a Soft Hinged Micro Scanning Mirror With a Triaxial Hall Effect Sensor

Calibration of Tri-Axial Sensor Coil for Magnetic Tracking

Calibration of Tri-Axial Sensor Coil for Magnetic Tracking

Innovative use of sensor technology to study grazing behaviour and its associations with parasitic resistance in sheep

Gastrointestinal nematodes (GINs) are a concern for sheep production and are associated with significant economic losses globally, driving the need for effective management strategies. The aims of this research were first to determine the influence of GIN infections on sheep grazing behaviours, specifically grazing time (GT) and grazing event length (GL), and then to estimate the genetic and phenotypic correlation between faecal egg counts (FEC) and salivary specific IgA activity vs GT and GL. Data were collected from 146 Merino sheep on a commercial farm in Victoria, Australia. Faecal, saliva and body weight (BW) were collected at four time points (A: Oct 2019, B: early May 2020, C: late May 2020, and D: Jul 2020), while tri-axial accelerometer sensors recorded GT and GL for the length of 26 days between B and C. Univariate linear regression and Bayesian Generalised Linear Mixed Model, estimated using Markov Chain Monte Carlo, were used to assess the relationships between the behaviours and infection indicators. We found an increase in the logarithm of FEC was associated with a significant rise in grazing time (p < 0.05), suggesting compensatory behaviour in infected sheep. GT also accounted for 6% of the FEC variation. In contrast, the effect of IgA on GT was not significant (p > 0.05). There was a positive genetic and phenotypic correlation estimated between GT and FEC, however, the High Posterior Density interval for the genetic and phenotypic correlations between GT and FEC indicates some uncertainty in these estimates. These results imply that sheep may modify their grazing strategies as an adaptive response to parasitic infections to ease the impact on their nutritional intake. This study improves our understanding of how natural GIN infections affect sheep behaviour and highlights the potential to use animal behaviours as a precision livestock farming tool and in breeding for resilience against parasites.

Effects of Different Wearable Resistance Placements on Running Stability.

Stability during running has been recognized as a crucial factor contributing to running performance. This study aimed to investigate the effects of wearable equipment containing external loads on different body parts on running stability. Fifteen recreational male runners (20.27 ± 1.23 years, age range 19-22 years) participated in five treadmill running conditions, including running without loads and running with loads equivalent to 10% of individual body weight placed on four different body positions: forearms, lower legs, trunk, and a combination of all three (forearms, lower legs, and trunk). A tri-axial accelerometer-based smartphone sensor was attached to the participants' lumbar spine (L5) to record body accelerations. The largest Lyapunov exponent (LyE) was applied to individual acceleration data as a measure of local dynamic stability, where higher LyE values suggest lower stability. The effects of load distribution appear in the mediolateral (ML) direction. Specifically, running with loads on the lower legs resulted in a lower LyE_ML value compared to running without loads (p = 0.001) and running with loads on the forearms (p < 0.001), trunk (p = 0.001), and combined segments (p = 0.005). These findings suggest that running with loads on the lower legs enhances side-to-side local dynamic stability, providing valuable insights for training.

Features gradient-based signals selection algorithm of linear complexity for convolutional neural networks

&lt;abstract&gt;&lt;p&gt;Recently, convolutional neural networks (CNNs) for classification by time domain data of multi-signals have been developed. Although some signals are important for correct classification, others are not. The calculation, memory, and data collection costs increase when data that include unimportant signals for classification are taken as the CNN input layer. Therefore, identifying and eliminating non-important signals from the input layer are important. In this study, we proposed a features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing non-important signals for classification by utilizing features gradient obtained by the process of gradient-weighted class activation mapping (grad-CAM). When we defined $ n_ \mathrm{s} $ as the number of signals, the computational complexity of FG-SSA is the linear time $ \mathcal{O}(n_ \mathrm{s}) $ (i.e., it has a low calculation cost). We verified the effectiveness of the algorithm using the OPPORTUNITY dataset, which is an open dataset comprising of acceleration signals of human activities. In addition, we checked the average of 6.55 signals from a total of 15 signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, FG-SSA can find and remove signals that are not important for CNN-based classification. In the process of FG-SSA, the degree of influence of each signal on each class estimation is quantified. Therefore, it is possible to visually determine which signal is effective and which is not for class estimation. FG-SSA is a white-box signal selection algorithm because it can understand why the signal was selected. The existing method, Bayesian optimization, was also able to find superior signal sets, but the computational cost was approximately three times greater than that of FG-SSA. We consider FG-SSA to be a low-computational-cost algorithm.&lt;/p&gt;&lt;/abstract&gt;

Research on the optimization model of personalized training for athletes based on machine learning in college physical education

Abstract We use deep learning to extract and identify the physiological indicators, sports, and data analysis parameters, providing scientific references for athletes’ personalized training. This paper aims to construct an optimization system for personalized training for athletes based on machine learning. Initially, we designed the system’s overall structure, hardware, and software scheme, and then we modeled the algorithms for measuring four physiological parameters. The NTC thermistor thermometry method was utilized to measure body temperature, the photoelectric volumetric method for determining blood oxygen and heart rate, and the ECG signal method for calculating blood pressure. The adaptive threshold method uses the human body’s steady-state recognition algorithm, based on a single triaxial acceleration sensor, to recognize steady-state motion states such as resting, running, and walking. After the construction of the system was completed, it was applied to the sports team of a university in Guangzhou, and a semester of personalized training optimization experiments for athletes was conducted. The system found a maximum error of 0.02 °C between the measured body temperature and the results from the medical temperature gun, ensuring stable performance during short-term body temperature changes like training. Except for B, C, D, and G errors of 1%, the rest of the athletes monitored by the system in this paper’s blood oxygen results are consistent with the oximeter. The accuracy of recognizing each sport is above 90%. In the eight swimming events that need to be assessed, the average score before the beginning of the semester was 65.43, and after the experiment, it was 87.14, an improvement of 21.71 points. The system in this paper has a great auxiliary effect on the optimization of personalized training for athletes in terms of physical function monitoring, sports status statistics, and training intensity setting, which provides new ideas and methods for the integration of personalized training for athletes with cutting-edge information technology.