This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using m...This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using machine learning(ML)algorithms through accelerometer sensors.However,behavioral analysis poses challenges due to the complexity of cow activities.The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints.Shorter windows may lack sufficient information,reducing algorithm performance.Additionally,the sensor’s position on the cowsmay shift during practical use,altering the collected accelerometer data.This study addresses these challenges by employing a 3-s data window to analyze cow behaviors,specifically Feeding,Lying,Standing,and Walking.Data synchronization between accelerometer sensors placed on the neck and leg compensates for the lack of information in short data windows.Features such as the Vector of Dynamic Body Acceleration(VeDBA),Mean,Variance,and Kurtosis are utilized alongside the Decision Tree(DT)algorithm to address energy efficiency and ensure computational effectiveness.This study also evaluates the impact of sensor misalignment on behavior classification.Simulated datasets with varying levels of sensor misalignment were created,and the system’s classification accuracy exceeded 0.95 for the four behaviors across all datasets(including original and simulated misalignment datasets).Sensitivity(Sen)and PPV for all datasets were above 0.9.The study provides farmers and the dairy industry with a practical,energy-efficient system for continuously monitoring cattle behavior to enhance herd productivity while reducing labor costs.展开更多
A displacement sensor based on the fiber Fabry-Perot (F-P) cavity was proposed in this paper. Theoretical and experimental analyses were presented. Displacement resolution was demonstrated by spectrum-domain experimen...A displacement sensor based on the fiber Fabry-Perot (F-P) cavity was proposed in this paper. Theoretical and experimental analyses were presented. Displacement resolution was demonstrated by spectrum-domain experiments to obtain the dynamic range of the F-P sensor, and a piezoelectric crystal unit (PZT) was used as the driver. The output signal was modulated by a piezoelectric ceramic ring and demodulated by a phase-locked oscillator. The experimental results show that the displacement resolution of the F-P sensor is less than 5 nm and the dynamic range is more than 100 μm. As acceleration is the second-order differential of displacement, an accelerometer model was proposed using the finite element method (FEM) nd ANSYS software.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications.A known mathematical calibration mo...This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications.A known mathematical calibration model has been adopted.The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude,heading,and tilt angles estimation that are of interest to geomatics applications.The evaluation method consists of two stages.First,the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field.Second,the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model.A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.展开更多
A novel capacitive biaxial microaccelerometer with a highly symmetrical microstructure is developed. The sensor is composed of a single seismic mass, grid strip, supporting beam, joint beam, and damping adjusting comb...A novel capacitive biaxial microaccelerometer with a highly symmetrical microstructure is developed. The sensor is composed of a single seismic mass, grid strip, supporting beam, joint beam, and damping adjusting combs. The sensing method of changing capacitance area is used in the design,which depresses the requirement of the DRIE process, and de- creases electronic noise by increasing sensing voltage to improve the resolution. The parameters and characteristics of the biaxial microaccelerometer are discussed with the FEM tool ANSYS. The simulated results show that the transverse sensitivity of the sensor is equal to zero. The testing devices based on the slide-film damping effect are fabricated, and the testing quality factor is 514, which shows that the designed structure can improve the resolution and proves the feasibility of the designed process.展开更多
For the purpose of improving the precision of the inertial guidance system,it is necessary to enhance the accuracy of the accelerometer.Combining the micro-fabrication processes with resonant sensor technology,a high-...For the purpose of improving the precision of the inertial guidance system,it is necessary to enhance the accuracy of the accelerometer.Combining the micro-fabrication processes with resonant sensor technology,a high-resolution inertial-grade novel micro resonant accelerometer is studied.Based on the detecting theory of the resonant sensors,the accelerometer is designed,fabricated,and tested.The accelerometer consists of one proofmass,two micro leverages and two double-ended-tuning-fork （DETF） resonators.The sensing principle of this accelerometer is based on that the natural frequency of the DETF resonator shifts with its axial load which is caused by inertial force.The push-pull configuration of the DETF is for temperature compensation.The two-stage micro leverage mechanisms are employed to amplify the force and increase the sensitivity of the accelerometer.The micro leverage and the resonator are modeled for static analysis and nonlinear modal analysis via theory method and finite element method （FEM）,respectively.The geometrical parameters of them are optimized.The amplification factor of the leverage is 102,and the sensitivity of the resonator on theory is about 62 Hz/g.The samples of the accelerometer are fabricated with deep reactive ion etching （DRIE） technology which can get a high-aspect ratio structure for contributing a greater sensing-capacitance.The measuring results of the samples by scanning electron microscopy （SEM） show that the process is feasible,because of the complete structure,the sound combs and micro leverages,and the acceptable errors.The frequency of the resonator and the sensitivity of the accelerometer are tested via printed circuit board （PCB）,respectively.The result of the test shows that the frequency of the push-resonator is about 54 530 Hz and the sensitivity of the accelerometer is about 55 Hz/g.The amplification factor of the leverage is calculated more accurately because the coupling of the two stages leverage is considered during derivation of the analysis formula.In addition,the novel differential structure of the accelerometer can greatly improve the sensitivity of the accelerometers.展开更多
A distributed feedback fiber laser based Bragg grating vibration sensor system is proposed.Demodulated by using an unbalanced M-Z interferometer,experiment demonstrates that the system runs at a sensing sensitivity of...A distributed feedback fiber laser based Bragg grating vibration sensor system is proposed.Demodulated by using an unbalanced M-Z interferometer,experiment demonstrates that the system runs at a sensing sensitivity of about 257.2 rad·s2/m and a resolution of 4.2×10-5 m/s2 for monitoring acceleration.Experimental results show that the phase-shift changes with the acceleration linearly.展开更多
Vector accelerometer has attracted much attention for its great application potential in underground seismic signal measurement. We propose and demonstrate a novel vector accelerometer based on the three fiber Bragg g...Vector accelerometer has attracted much attention for its great application potential in underground seismic signal measurement. We propose and demonstrate a novel vector accelerometer based on the three fiber Bragg gratings(FBGs)embedded in a silicone rubber compliant cylinder at 120° distributed uniformly. The accelerometer is capable of detecting the orientation of vibration with a range of 0°–360° and the acceleration through monitoring the central wavelength shifts of three FBGs simultaneously. The experimental results show that the natural frequency of the accelerometer is about 85 Hz, and the sensitivity is 84.21 pm/g in the flat range of 20 Hz–60 Hz. Through experimental calibration, the designed accelerometer can accurately obtain vibration vector information, including vibration orientation and acceleration. In addition, the range of resonant frequency and sensitivity can be expanded by adjusting the hardness of the silicone rubber materials. Due to the characteristics of small size and orientation recognition, the accelerometer can be applied to low-frequency vibration acceleration vector measurement in narrow spaces.展开更多
The performance of any inertially stabilized platform （ISP） is strongly related to the bandwidth and accuracy of the angular velocity signals. This paper discusses the development of an optimal state estimator for s...The performance of any inertially stabilized platform （ISP） is strongly related to the bandwidth and accuracy of the angular velocity signals. This paper discusses the development of an optimal state estimator for sensing inertial velocity using low-cost micro-electro-mechanical systems （MEMS） sensors. A low-bandwidth gyroscope is used alone with two low-performance accelerometers to obtain the estimation. The gyroscope has its own limited dynamics and mainly contributes to the low-frequency components of the estimation. The accelerometers have inherent biases and mainly contribute to the high-frequency components of the estimation. Extensive experimental results show that the state estimator can achieve high-performance signals over a wide range of velocities without drifts in both the t- and s-domains. Furthermore, with applications in miniature inertially stabilized platforms, the control characteristic presents a significantly improvement over the existing methods. The method can be also applied to robotics, attitude estimation, and friction compensation.展开更多
Many different forms of sensor fusion have been proposed each with its own niche.We propose a method of fusing multiple different sensor types.Our approach is built on the discrete belief propagation to fuse photogram...Many different forms of sensor fusion have been proposed each with its own niche.We propose a method of fusing multiple different sensor types.Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional(3D)point clouds.We propose using a non-parametric belief propagation similar to Sudderth et al’s work to fuse different sensors.This technique allows continuous variables to be used,is trivially parallel making it suitable for modern many-core processors,and easily accommodates varying types and combinations of sensors.By defining the relationships between common sensors,a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors.This allows the use of unreliable sensors which firstly,may start and stop providing data at any time and secondly,the integration of new sensor types simply by defining their relationship with existing sensors.These features allow a flexible framework to be developed which is suitable for many tasks.Using an abstract algorithm,we can instead focus on the relationships between sensors.Where possible we use the existing relationships between sensors rather than developing new ones.These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network.In this paper,we present the initial results from this technique and the intended course for future work.展开更多
This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerome ：e...This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerome ：er output and causes a degradation of attitude steady accuracy. In order to improve control accuracy, this paper proposes a proportional multiple-integral observer- based control strategy to estimate and compensate the accelerometer error. The basic idea of this paper is to approximate the error property by using a q-order polynomial function and extend the error and its derivatives as augmented states. Then a proportional multiple-integral observer is developed to estimate the error, with which the relationship between the error and the imbalance torque is formulated. The estimated value is compared to an angle threshold, the result of which is used to compen- sate the accelerometer output. Through static and vehicle-mounted experiments, it is demonstrated that compared with the tra- ditional method, the proposed method can improve the attitude steady accuracy effectively.展开更多
The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Thera...The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.展开更多
Using fine electromagnetic signals to measure observables of other fields like curvature and torsion of a space, and the corresponding value of their integrals of the action of perception of curvature through electron...Using fine electromagnetic signals to measure observables of other fields like curvature and torsion of a space, and the corresponding value of their integrals of the action of perception of curvature through electronic signals that detect curvature on a curved surface, it is designed and constructed a sensor of curvature of accelerometer type that detects and curvature measures in 2 and 3-dimensional spaces using the programming of shape operators on spheres and the value of their integrals along the curves and geodesics in their principal directions.展开更多
This paper designs a wireless sensor network based on CC2530.The sensor nodes consist of multi-ranged accelerometer and CC2530,covering all the ranges of the acceleration signals which can be measured.The designed sys...This paper designs a wireless sensor network based on CC2530.The sensor nodes consist of multi-ranged accelerometer and CC2530,covering all the ranges of the acceleration signals which can be measured.The designed system solves the problems such as cable installation trouble of testing system,vulnerability to interference and complexity of circuit.Test results show that the designed wireless sensor network can transmit the signals that multi-ranged micro-accelerometer emits without spoilage,thus the measurement of acceleration of the covering region is completed.展开更多
Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Impe...Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and <span style="font-family:Verdana;">K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.</span>展开更多
Gait analysis is a process of learning the motion of human and animal by using wearable sensor approach and vision approach. This analysis is mainly used in medical and sports field where the study of body parts is cr...Gait analysis is a process of learning the motion of human and animal by using wearable sensor approach and vision approach. This analysis is mainly used in medical and sports field where the study of body parts is crucial. 3-space sensor is a sensor consists of accelerometer, gyroscope sensor and compass sensor, built in one device. In this study, 3-space sensor is used to collect data of walking and jogging motion, of a test subject running on a treadmill. Angular velocity of the test subject’s arm and the angle of subject’s leaping motion are the two main components under investigation. Data are analyzed and processed with Principal of Component Analysis (PCA) technique. This method aims to combine and reduce the number of variables of the raw data. The Quiver function is used in order to generate feature vectors for both motions. Furthermore, the output of the process was used to create a system that can recognize human motion on any given data. The system is highly able to differentiate both of the motions.展开更多
The chattering noise problem of reed switch sensor signal for Automatic Meter Reading system was analyzed experimentally under various types of external vibrations and shocks. The external vibration level amplitude wa...The chattering noise problem of reed switch sensor signal for Automatic Meter Reading system was analyzed experimentally under various types of external vibrations and shocks. The external vibration level amplitude was measured with an accelerometer. To apply for water flow measurement devices, the reed switch sensors should keep high reliability. But the measured digital meter data are occurred difference or errors by chattering noise. The reed switch contains chattering error by itself at the force equivalent position. The vibrations such as passing vehicle near to the reed switch installed location causes chattering. In order to reduce chattering error, most system uses just software methods, for example using digital filter algorithm and also statistical calibration methods. However software approaches were implemented for reducing chattering error, there has still generated chattering error due to external mechanical vibrations and magnetic field. The chattering errors can be reduced by changing leaf spring structure using mechanical hysteresis characteristics.展开更多
This paper presents a literature review exploring the potential of piezoelectric field-effect transistors(piezo-FETs)as bionic microelectromechanical systems(MEMS).First,piezo-FETs are introduced as bionic counterpart...This paper presents a literature review exploring the potential of piezoelectric field-effect transistors(piezo-FETs)as bionic microelectromechanical systems(MEMS).First,piezo-FETs are introduced as bionic counterparts to natural mechanoreceptors,highlighting their classic configuration and working principles.Then,this paper summarizes the existing research on piezo-FETs as sensors for pressure,inertial,and acoustic sensors.Material selections,design characteristics,and key performance metrics are reviewed to demonstrate the advantage of piezo-FETs over traditional piezoelectric sensors.After identifying the limitations in these existing studies,this paper proposes using bionic piezoelectric coupling structures in piezo-FETs to further enhance the sensing capabilities of these artificial mechanoreceptors.Experimentally validated manufacturing methods for the newly proposed piezo-FET structures are also reviewed,pointing out a novel,feasible,and impactful research direction on these bionic piezoelectric MEMS sensors.展开更多
Objectives:Valid estimation of energy expenditure remains a challenge,particularly when using ankle-and thighworn devices.The Move 4 is a research-grade accelerometer previously tested for predicting metabolic equival...Objectives:Valid estimation of energy expenditure remains a challenge,particularly when using ankle-and thighworn devices.The Move 4 is a research-grade accelerometer previously tested for predicting metabolic equivalents(METs)when worn at the waist or wrist.This study aimed to calibrate and evaluate regression models to estimate METs from Move 4 data when worn at the ankle and thigh.Methods:Participants completed walking and jogging tasks under laboratory conditions while wearing Move 4 sensors and with indirect calorimetry as a reference measure.Models were calibrated using study 1(n=160)and evaluated in an independent dataset(study 2;n=15).Performance was assessed using mean absolute error(MAE),root mean square error(RMSE),and Bland-Altman analyses.Results:The MET models demonstrated strong agreement across both locations and datasets.For the thigh position,the MAE ranged from 0.60 METs(walking)to 1.38 METs(jogging),with RMSE of 0.82 and 1.70 in the evaluation data.Calibration metrics were comparable(jogging:MAE=1.24,RMSE=1.63).The ankle models showed similar accuracy,with MAEs of 0.66(walking)and 1.39(jogging),and RMSEs of 0.85 and 1.67,respectively.Systematic bias remained low(mean differences between−0.34 and−0.01 METs).Conclusions:This study provides the first calibration and evaluation for estimating METs from ankle-and thigh-worn Move 4 accelerometers.The model indicated accurate,highresolution MET estimation for walking and jogging.Future work should expand independent performance evaluations,including diverse activities such as static activities,and diverse samples under free-living conditions.展开更多
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number:02/2022/TN.
文摘This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using machine learning(ML)algorithms through accelerometer sensors.However,behavioral analysis poses challenges due to the complexity of cow activities.The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints.Shorter windows may lack sufficient information,reducing algorithm performance.Additionally,the sensor’s position on the cowsmay shift during practical use,altering the collected accelerometer data.This study addresses these challenges by employing a 3-s data window to analyze cow behaviors,specifically Feeding,Lying,Standing,and Walking.Data synchronization between accelerometer sensors placed on the neck and leg compensates for the lack of information in short data windows.Features such as the Vector of Dynamic Body Acceleration(VeDBA),Mean,Variance,and Kurtosis are utilized alongside the Decision Tree(DT)algorithm to address energy efficiency and ensure computational effectiveness.This study also evaluates the impact of sensor misalignment on behavior classification.Simulated datasets with varying levels of sensor misalignment were created,and the system’s classification accuracy exceeded 0.95 for the four behaviors across all datasets(including original and simulated misalignment datasets).Sensitivity(Sen)and PPV for all datasets were above 0.9.The study provides farmers and the dairy industry with a practical,energy-efficient system for continuously monitoring cattle behavior to enhance herd productivity while reducing labor costs.
基金Project (No. 111303-8112D2) supported by the National DefenseResearch Foundation of Zhejiang University, China
文摘A displacement sensor based on the fiber Fabry-Perot (F-P) cavity was proposed in this paper. Theoretical and experimental analyses were presented. Displacement resolution was demonstrated by spectrum-domain experiments to obtain the dynamic range of the F-P sensor, and a piezoelectric crystal unit (PZT) was used as the driver. The output signal was modulated by a piezoelectric ceramic ring and demodulated by a phase-locked oscillator. The experimental results show that the displacement resolution of the F-P sensor is less than 5 nm and the dynamic range is more than 100 μm. As acceleration is the second-order differential of displacement, an accelerometer model was proposed using the finite element method (FEM) nd ANSYS software.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
文摘This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications.A known mathematical calibration model has been adopted.The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude,heading,and tilt angles estimation that are of interest to geomatics applications.The evaluation method consists of two stages.First,the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field.Second,the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model.A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.
文摘A novel capacitive biaxial microaccelerometer with a highly symmetrical microstructure is developed. The sensor is composed of a single seismic mass, grid strip, supporting beam, joint beam, and damping adjusting combs. The sensing method of changing capacitance area is used in the design,which depresses the requirement of the DRIE process, and de- creases electronic noise by increasing sensing voltage to improve the resolution. The parameters and characteristics of the biaxial microaccelerometer are discussed with the FEM tool ANSYS. The simulated results show that the transverse sensitivity of the sensor is equal to zero. The testing devices based on the slide-film damping effect are fabricated, and the testing quality factor is 514, which shows that the designed structure can improve the resolution and proves the feasibility of the designed process.
文摘For the purpose of improving the precision of the inertial guidance system,it is necessary to enhance the accuracy of the accelerometer.Combining the micro-fabrication processes with resonant sensor technology,a high-resolution inertial-grade novel micro resonant accelerometer is studied.Based on the detecting theory of the resonant sensors,the accelerometer is designed,fabricated,and tested.The accelerometer consists of one proofmass,two micro leverages and two double-ended-tuning-fork （DETF） resonators.The sensing principle of this accelerometer is based on that the natural frequency of the DETF resonator shifts with its axial load which is caused by inertial force.The push-pull configuration of the DETF is for temperature compensation.The two-stage micro leverage mechanisms are employed to amplify the force and increase the sensitivity of the accelerometer.The micro leverage and the resonator are modeled for static analysis and nonlinear modal analysis via theory method and finite element method （FEM）,respectively.The geometrical parameters of them are optimized.The amplification factor of the leverage is 102,and the sensitivity of the resonator on theory is about 62 Hz/g.The samples of the accelerometer are fabricated with deep reactive ion etching （DRIE） technology which can get a high-aspect ratio structure for contributing a greater sensing-capacitance.The measuring results of the samples by scanning electron microscopy （SEM） show that the process is feasible,because of the complete structure,the sound combs and micro leverages,and the acceptable errors.The frequency of the resonator and the sensitivity of the accelerometer are tested via printed circuit board （PCB）,respectively.The result of the test shows that the frequency of the push-resonator is about 54 530 Hz and the sensitivity of the accelerometer is about 55 Hz/g.The amplification factor of the leverage is calculated more accurately because the coupling of the two stages leverage is considered during derivation of the analysis formula.In addition,the novel differential structure of the accelerometer can greatly improve the sensitivity of the accelerometers.
基金supported by the Science Fund for Young Scholars of Heilongjiang University,China(No.QL200901)
文摘A distributed feedback fiber laser based Bragg grating vibration sensor system is proposed.Demodulated by using an unbalanced M-Z interferometer,experiment demonstrates that the system runs at a sensing sensitivity of about 257.2 rad·s2/m and a resolution of 4.2×10-5 m/s2 for monitoring acceleration.Experimental results show that the phase-shift changes with the acceleration linearly.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61927812, 61735014, and 62105261)。
文摘Vector accelerometer has attracted much attention for its great application potential in underground seismic signal measurement. We propose and demonstrate a novel vector accelerometer based on the three fiber Bragg gratings(FBGs)embedded in a silicone rubber compliant cylinder at 120° distributed uniformly. The accelerometer is capable of detecting the orientation of vibration with a range of 0°–360° and the acceleration through monitoring the central wavelength shifts of three FBGs simultaneously. The experimental results show that the natural frequency of the accelerometer is about 85 Hz, and the sensitivity is 84.21 pm/g in the flat range of 20 Hz–60 Hz. Through experimental calibration, the designed accelerometer can accurately obtain vibration vector information, including vibration orientation and acceleration. In addition, the range of resonant frequency and sensitivity can be expanded by adjusting the hardness of the silicone rubber materials. Due to the characteristics of small size and orientation recognition, the accelerometer can be applied to low-frequency vibration acceleration vector measurement in narrow spaces.
基金Foundation item： National Natural Science Foundation of China （50805144）
文摘The performance of any inertially stabilized platform （ISP） is strongly related to the bandwidth and accuracy of the angular velocity signals. This paper discusses the development of an optimal state estimator for sensing inertial velocity using low-cost micro-electro-mechanical systems （MEMS） sensors. A low-bandwidth gyroscope is used alone with two low-performance accelerometers to obtain the estimation. The gyroscope has its own limited dynamics and mainly contributes to the low-frequency components of the estimation. The accelerometers have inherent biases and mainly contribute to the high-frequency components of the estimation. Extensive experimental results show that the state estimator can achieve high-performance signals over a wide range of velocities without drifts in both the t- and s-domains. Furthermore, with applications in miniature inertially stabilized platforms, the control characteristic presents a significantly improvement over the existing methods. The method can be also applied to robotics, attitude estimation, and friction compensation.
文摘Many different forms of sensor fusion have been proposed each with its own niche.We propose a method of fusing multiple different sensor types.Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional(3D)point clouds.We propose using a non-parametric belief propagation similar to Sudderth et al’s work to fuse different sensors.This technique allows continuous variables to be used,is trivially parallel making it suitable for modern many-core processors,and easily accommodates varying types and combinations of sensors.By defining the relationships between common sensors,a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors.This allows the use of unreliable sensors which firstly,may start and stop providing data at any time and secondly,the integration of new sensor types simply by defining their relationship with existing sensors.These features allow a flexible framework to be developed which is suitable for many tasks.Using an abstract algorithm,we can instead focus on the relationships between sensors.Where possible we use the existing relationships between sensors rather than developing new ones.These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network.In this paper,we present the initial results from this technique and the intended course for future work.
基金supported by the National Natural Science Foundation of China(Grant Nos.61174121,61333005 and 61121003)the Ph.D Programs Foundations of the Ministry of Education China
文摘This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerome ：er output and causes a degradation of attitude steady accuracy. In order to improve control accuracy, this paper proposes a proportional multiple-integral observer- based control strategy to estimate and compensate the accelerometer error. The basic idea of this paper is to approximate the error property by using a q-order polynomial function and extend the error and its derivatives as augmented states. Then a proportional multiple-integral observer is developed to estimate the error, with which the relationship between the error and the imbalance torque is formulated. The estimated value is compared to an angle threshold, the result of which is used to compen- sate the accelerometer output. Through static and vehicle-mounted experiments, it is demonstrated that compared with the tra- ditional method, the proposed method can improve the attitude steady accuracy effectively.
文摘The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.
文摘Using fine electromagnetic signals to measure observables of other fields like curvature and torsion of a space, and the corresponding value of their integrals of the action of perception of curvature through electronic signals that detect curvature on a curved surface, it is designed and constructed a sensor of curvature of accelerometer type that detects and curvature measures in 2 and 3-dimensional spaces using the programming of shape operators on spheres and the value of their integrals along the curves and geodesics in their principal directions.
基金National Natural Science Foundation of China(No.51075374)
文摘This paper designs a wireless sensor network based on CC2530.The sensor nodes consist of multi-ranged accelerometer and CC2530,covering all the ranges of the acceleration signals which can be measured.The designed system solves the problems such as cable installation trouble of testing system,vulnerability to interference and complexity of circuit.Test results show that the designed wireless sensor network can transmit the signals that multi-ranged micro-accelerometer emits without spoilage,thus the measurement of acceleration of the covering region is completed.
文摘Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and <span style="font-family:Verdana;">K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.</span>
文摘Gait analysis is a process of learning the motion of human and animal by using wearable sensor approach and vision approach. This analysis is mainly used in medical and sports field where the study of body parts is crucial. 3-space sensor is a sensor consists of accelerometer, gyroscope sensor and compass sensor, built in one device. In this study, 3-space sensor is used to collect data of walking and jogging motion, of a test subject running on a treadmill. Angular velocity of the test subject’s arm and the angle of subject’s leaping motion are the two main components under investigation. Data are analyzed and processed with Principal of Component Analysis (PCA) technique. This method aims to combine and reduce the number of variables of the raw data. The Quiver function is used in order to generate feature vectors for both motions. Furthermore, the output of the process was used to create a system that can recognize human motion on any given data. The system is highly able to differentiate both of the motions.
文摘The chattering noise problem of reed switch sensor signal for Automatic Meter Reading system was analyzed experimentally under various types of external vibrations and shocks. The external vibration level amplitude was measured with an accelerometer. To apply for water flow measurement devices, the reed switch sensors should keep high reliability. But the measured digital meter data are occurred difference or errors by chattering noise. The reed switch contains chattering error by itself at the force equivalent position. The vibrations such as passing vehicle near to the reed switch installed location causes chattering. In order to reduce chattering error, most system uses just software methods, for example using digital filter algorithm and also statistical calibration methods. However software approaches were implemented for reducing chattering error, there has still generated chattering error due to external mechanical vibrations and magnetic field. The chattering errors can be reduced by changing leaf spring structure using mechanical hysteresis characteristics.
文摘This paper presents a literature review exploring the potential of piezoelectric field-effect transistors(piezo-FETs)as bionic microelectromechanical systems(MEMS).First,piezo-FETs are introduced as bionic counterparts to natural mechanoreceptors,highlighting their classic configuration and working principles.Then,this paper summarizes the existing research on piezo-FETs as sensors for pressure,inertial,and acoustic sensors.Material selections,design characteristics,and key performance metrics are reviewed to demonstrate the advantage of piezo-FETs over traditional piezoelectric sensors.After identifying the limitations in these existing studies,this paper proposes using bionic piezoelectric coupling structures in piezo-FETs to further enhance the sensing capabilities of these artificial mechanoreceptors.Experimentally validated manufacturing methods for the newly proposed piezo-FET structures are also reviewed,pointing out a novel,feasible,and impactful research direction on these bionic piezoelectric MEMS sensors.
基金funded by the German Research Foundation[Grant Number:496846758].
文摘Objectives:Valid estimation of energy expenditure remains a challenge,particularly when using ankle-and thighworn devices.The Move 4 is a research-grade accelerometer previously tested for predicting metabolic equivalents(METs)when worn at the waist or wrist.This study aimed to calibrate and evaluate regression models to estimate METs from Move 4 data when worn at the ankle and thigh.Methods:Participants completed walking and jogging tasks under laboratory conditions while wearing Move 4 sensors and with indirect calorimetry as a reference measure.Models were calibrated using study 1(n=160)and evaluated in an independent dataset(study 2;n=15).Performance was assessed using mean absolute error(MAE),root mean square error(RMSE),and Bland-Altman analyses.Results:The MET models demonstrated strong agreement across both locations and datasets.For the thigh position,the MAE ranged from 0.60 METs(walking)to 1.38 METs(jogging),with RMSE of 0.82 and 1.70 in the evaluation data.Calibration metrics were comparable(jogging:MAE=1.24,RMSE=1.63).The ankle models showed similar accuracy,with MAEs of 0.66(walking)and 1.39(jogging),and RMSEs of 0.85 and 1.67,respectively.Systematic bias remained low(mean differences between−0.34 and−0.01 METs).Conclusions:This study provides the first calibration and evaluation for estimating METs from ankle-and thigh-worn Move 4 accelerometers.The model indicated accurate,highresolution MET estimation for walking and jogging.Future work should expand independent performance evaluations,including diverse activities such as static activities,and diverse samples under free-living conditions.
