WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning...WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter （RBPF） is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are com- bined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.展开更多
Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which pro...Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.展开更多
Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic acc...Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic accelerometer, where the test mass is suspended by a fiber to compensate for its weight, and this scheme demonstrates an advantage, compared with the high-voltage levitation scheme, in which the effect of the seismic noise can be suppressed for a few orders of magnitude in low frequencies. In this work, the capacitive electrode cage is proposed to be suspended by another pendulum, and theoretical analysis shows that the effects of the seismic noise can be further suppressed for more than one order by suspending the electrode cage.展开更多
Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsens...Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.展开更多
Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional ...Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.展开更多
This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generali...This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.展开更多
With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This ...With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.展开更多
The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new wor...The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.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
The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quant...The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.展开更多
Inertial navigation represents a unique method of navigation,in which there is no dependency on external sources of information.As opposed to other position fixing navigation techniques,inertial navigation performs th...Inertial navigation represents a unique method of navigation,in which there is no dependency on external sources of information.As opposed to other position fixing navigation techniques,inertial navigation performs the navigation in a relative sense with respect to the initial navigation state of the moving platform.Hence,inertial navigation systems are not prone to jamming,or spoofing.Inertial navigation systems have developed vastly,from their occurrence in the 1940s up to date.The accuracy of the inertial sensors has improved over time,making inertial sensors sufficient in terms of size,weight,cost,and accuracy for navigation and guidance applications.Within the past few years,inertial sensors have developed from being purely mechanical into incorporating various technologies and taking advantage of numerous physical phenomena,from which the dynamic forces exerted on a moving body could be computed accurately.Besides,the evolution of inertial navigation scheme involved the evolution from stable-platform inertial navigation system,which were mechanically complicated,to computationally demanding strap-down inertial navigation systems.Optical sensory technologies have provided highly accurate inertial sensors,at smaller sizes.Besides,the vibratory inertial navigation technologies enabled the production of Micro-electro-machined inertial sensors that are extremely low-cost,and offer extremely low size,weight and power consumption,making them suitable for a wide range of day-to-day navigation applications.Recently,advanced inertial sensor technologies have been introduced to the industry such as nuclear magnetic resonance technology,coldatom technology,and the reintroduction of fluid-based inertial sensors.On another note,inertial sensor errors constitute a huge research aspect in which it is intended for inertial sensors to reach level in which they could operate for substantially long operation times in the absence of updates from aiding sensors,which would be a huge leap.Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors.In this review,the inertial sensor technologies are covered extensively,along the future trends in the inertial sensors’technologies.Besides,this review covers a brief overview on the inertial error modeling techniques used to enhance the performance of low-cost sensors.展开更多
This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit(IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is...This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit(IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is that the information output from the IMU is incomplete. For example, only two tilt information can be read from the gravity sensor of a smart phone. Despite incomplete inertial information, there are strong restrictions between the IMU and camera coordinate systems. This paper addresses the incomplete information based IMUcamera calibration problem by exploiting the intrinsic restrictions among the coordinate transformations. First,the IMU transformation between two poses is formulated with the unknown IMU information. Then the defective IMU information is restored using the complementary visual information. Finally, the Levenberg-Marquardt(LM)algorithm is applied to estimate the optimal calibration result in noisy environments. Experiments on both synthetic and real data show the validity and robustness of our algorithm.展开更多
A novel MEMS inertial sensor with enhanced sensing capacitors is developed. The designed fabricated process of the sensor is a deep RIE process, which can increase the mass of the seismic to reduce the mechanical nois...A novel MEMS inertial sensor with enhanced sensing capacitors is developed. The designed fabricated process of the sensor is a deep RIE process, which can increase the mass of the seismic to reduce the mechanical noise, and the designed capacitance sensing method is changing the capacitance area, which can reduce the air damping between the sensing capacitor plates and reduce the requirement for the DRIE process precision, and reduce the electronic noise by increasing the sensing voltage to improve the resolution. The design and simulation are also verified by using the FEM tool ANSYS. The simulated results show that the transverse sensitivity of the sensor is approximately equal to zero. Finally, the fabricated process based on silicon-glass bonding and the preliminary test results of the device for testing grid capacitors and the novel inertial sensor are presented. The testing quality factor of the testing device based on the slide-film damping effect is 514, which shows that the enhanced capacitors can reduce mechanical noise. The preliminary testing result of the sensitivity is 0.492pf/g.展开更多
To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied sig...To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of INS errors.The principle of the RMT was introduced and the error propagating functions were derived from the rotary navigation equation.Effects of the measurement error for the rotation angle of the platform on the system precision were analyzed.The simulation and experimental results show that the precision of INS was ① dramatically improved with the use of the RMT,and ② hardly reduced when the measurement error for the rotation angle was in arc-second level.The study results offer a theoretical basis for engineering design of rotary INS.展开更多
Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitne...Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the game.Further,the sensor data is sent to a machine learning model,which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational sector.Extensive experimentation proved the validity of the proposed system.展开更多
The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are co...The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.展开更多
This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several e...This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several experiments were carried out using a motion capture system（VICON） and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units（IMUs） fused through an adaptive neuro-fuzzy inference systems（ANFIS） algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom（2-DOF） planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.展开更多
MEMS gyroscope is a new inertial navigation sensor,which can measure the input angular rate of sensitive axis using Coriolis effect.Compared to the conventional gyroscope,it owns many unique advantages.A novel structu...MEMS gyroscope is a new inertial navigation sensor,which can measure the input angular rate of sensitive axis using Coriolis effect.Compared to the conventional gyroscope,it owns many unique advantages.A novel structure of vibrating ring gyroscope is proposed and the finite element model of the oscillator is established based on MEMS technology.Through the modal analysis,the natural frequency and mode shapes of the oscillator are obtained.By analyzing the effects of the structural parameters on the mode shapes and frequency of the harmonic oscillator,the optimal design parameters are got.The frequency difference between the operating mode and the other modes is greater than 1kHz after optimization,which can avoid the frequency coupling of the operating mode and other vibrating modes of the oscillator.The simulation results show that the performance parameters of the ring structure meet the design requirements and have obvious advantages.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
A Kalman filter used in strapdown AHRS (Attitude Heading Reference System) based on micro machined inertial sensors is introduced. The composition and principle of the system are described. The attitude algorithm and ...A Kalman filter used in strapdown AHRS (Attitude Heading Reference System) based on micro machined inertial sensors is introduced. The composition and principle of the system are described. The attitude algorithm and error model of the system are derived based on the quaternion formulation. The real time quaternion based Kalman filter is designed. Simulation results show that accuracy of the system is better than 0.04 degree without disturbance of lateral acceleration and reduced to 0.44 degree with l...展开更多
文摘WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter （RBPF） is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are com- bined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.
基金supported in part by National Natural Science Foundation of China Grant 61202360, 61033001, 61361136003the National Basic Research Program of China Grant 2011CBA00300, 2011CBA00302
文摘Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.
基金Supported by the National Natural Science Foundation of China under Grant No 11235004
文摘Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic accelerometer, where the test mass is suspended by a fiber to compensate for its weight, and this scheme demonstrates an advantage, compared with the high-voltage levitation scheme, in which the effect of the seismic noise can be suppressed for a few orders of magnitude in low frequencies. In this work, the capacitive electrode cage is proposed to be suspended by another pendulum, and theoretical analysis shows that the effects of the seismic noise can be further suppressed for more than one order by suspending the electrode cage.
基金supported by a grant (2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation (NRF)funded by the Ministry of Education,Republic of Korea.
文摘Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.
基金supported by the National Natural Science Foundation of China under(Grant No.52175531)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant(Grant Nos.KJQN202000605 and KJZD-M202000602)。
文摘Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.
基金supported by the National Science Foundation of China(Nos.42274037,41874034)the Beijing Natural Science Foundation(No.4202041)the National Key Research and Development Program of China(No.2020YFB0505804).
文摘This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.
基金supported partly by the Natural Science Foundation of Zhejiang Province,China(LGF21F020017).
文摘With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.
基金supported by University of Phayao(Grant No.FF66-UoE001)Thailand Science Research and Innovation Fund+1 种基金National Science,Research and Innovation Fund(NSRF)King Mongkut’s University of Technology North Bangkok with Contract No.KMUTNB-FF-65-27.
文摘The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.
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The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.
基金Dr.Naser El-Sheimy research funds from NSERC and Canada Research Chairs programs(Grant No.RT691875).
文摘Inertial navigation represents a unique method of navigation,in which there is no dependency on external sources of information.As opposed to other position fixing navigation techniques,inertial navigation performs the navigation in a relative sense with respect to the initial navigation state of the moving platform.Hence,inertial navigation systems are not prone to jamming,or spoofing.Inertial navigation systems have developed vastly,from their occurrence in the 1940s up to date.The accuracy of the inertial sensors has improved over time,making inertial sensors sufficient in terms of size,weight,cost,and accuracy for navigation and guidance applications.Within the past few years,inertial sensors have developed from being purely mechanical into incorporating various technologies and taking advantage of numerous physical phenomena,from which the dynamic forces exerted on a moving body could be computed accurately.Besides,the evolution of inertial navigation scheme involved the evolution from stable-platform inertial navigation system,which were mechanically complicated,to computationally demanding strap-down inertial navigation systems.Optical sensory technologies have provided highly accurate inertial sensors,at smaller sizes.Besides,the vibratory inertial navigation technologies enabled the production of Micro-electro-machined inertial sensors that are extremely low-cost,and offer extremely low size,weight and power consumption,making them suitable for a wide range of day-to-day navigation applications.Recently,advanced inertial sensor technologies have been introduced to the industry such as nuclear magnetic resonance technology,coldatom technology,and the reintroduction of fluid-based inertial sensors.On another note,inertial sensor errors constitute a huge research aspect in which it is intended for inertial sensors to reach level in which they could operate for substantially long operation times in the absence of updates from aiding sensors,which would be a huge leap.Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors.In this review,the inertial sensor technologies are covered extensively,along the future trends in the inertial sensors’technologies.Besides,this review covers a brief overview on the inertial error modeling techniques used to enhance the performance of low-cost sensors.
基金Project supported by the National Natural Science Foundation of China(Nos.61340046,60875050,and 60675025)the National High-Tech R&D Program(863) of China(No.2006AA04Z247)+1 种基金the Science and Technology Innovation Commission of Shenzhen Municipality(Nos.JCYJ20120614152234873,CXC201104210010A,JCYJ20130331144631730,and JCYJ20130331144716089)the Specialized Research Fund for the Doctoral Programme of Higher Education of China(No.20130001110011)
文摘This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit(IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is that the information output from the IMU is incomplete. For example, only two tilt information can be read from the gravity sensor of a smart phone. Despite incomplete inertial information, there are strong restrictions between the IMU and camera coordinate systems. This paper addresses the incomplete information based IMUcamera calibration problem by exploiting the intrinsic restrictions among the coordinate transformations. First,the IMU transformation between two poses is formulated with the unknown IMU information. Then the defective IMU information is restored using the complementary visual information. Finally, the Levenberg-Marquardt(LM)algorithm is applied to estimate the optimal calibration result in noisy environments. Experiments on both synthetic and real data show the validity and robustness of our algorithm.
基金supported by the National Natural Science Foundation of China(No.60506015)the Zhejiang Provincial Natural Science Foundation of China(No.Y107105)
文摘A novel MEMS inertial sensor with enhanced sensing capacitors is developed. The designed fabricated process of the sensor is a deep RIE process, which can increase the mass of the seismic to reduce the mechanical noise, and the designed capacitance sensing method is changing the capacitance area, which can reduce the air damping between the sensing capacitor plates and reduce the requirement for the DRIE process precision, and reduce the electronic noise by increasing the sensing voltage to improve the resolution. The design and simulation are also verified by using the FEM tool ANSYS. The simulated results show that the transverse sensitivity of the sensor is approximately equal to zero. Finally, the fabricated process based on silicon-glass bonding and the preliminary test results of the device for testing grid capacitors and the novel inertial sensor are presented. The testing quality factor of the testing device based on the slide-film damping effect is 514, which shows that the enhanced capacitors can reduce mechanical noise. The preliminary testing result of the sensitivity is 0.492pf/g.
基金Sponsored by the National Natural Science Foundation of China(60604011)
文摘To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of INS errors.The principle of the RMT was introduced and the error propagating functions were derived from the rotary navigation equation.Effects of the measurement error for the rotation angle of the platform on the system precision were analyzed.The simulation and experimental results show that the precision of INS was ① dramatically improved with the use of the RMT,and ② hardly reduced when the measurement error for the rotation angle was in arc-second level.The study results offer a theoretical basis for engineering design of rotary INS.
基金This researchwas supported by aGrant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea。
文摘Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the game.Further,the sensor data is sent to a machine learning model,which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational sector.Extensive experimentation proved the validity of the proposed system.
基金supported by National Natural Science Foundation of China (Nos. 60805038 and 60725309)Beijing Natural Science Foundation (No. 4082032)
文摘The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.
基金supported in part by a scholarship provided by the Mission DepartmentMinistry of Higher Education of the Government of Egypt
文摘This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several experiments were carried out using a motion capture system（VICON） and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units（IMUs） fused through an adaptive neuro-fuzzy inference systems（ANFIS） algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom（2-DOF） planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.
文摘MEMS gyroscope is a new inertial navigation sensor,which can measure the input angular rate of sensitive axis using Coriolis effect.Compared to the conventional gyroscope,it owns many unique advantages.A novel structure of vibrating ring gyroscope is proposed and the finite element model of the oscillator is established based on MEMS technology.Through the modal analysis,the natural frequency and mode shapes of the oscillator are obtained.By analyzing the effects of the structural parameters on the mode shapes and frequency of the harmonic oscillator,the optimal design parameters are got.The frequency difference between the operating mode and the other modes is greater than 1kHz after optimization,which can avoid the frequency coupling of the operating mode and other vibrating modes of the oscillator.The simulation results show that the performance parameters of the ring structure meet the design requirements and have obvious advantages.
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A Kalman filter used in strapdown AHRS (Attitude Heading Reference System) based on micro machined inertial sensors is introduced. The composition and principle of the system are described. The attitude algorithm and error model of the system are derived based on the quaternion formulation. The real time quaternion based Kalman filter is designed. Simulation results show that accuracy of the system is better than 0.04 degree without disturbance of lateral acceleration and reduced to 0.44 degree with l...
