Journal articles: 'Inertial data'

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Wicke, Jason, and Genevieve A. Dumas. "Estimating Segment Inertial Parameters Using Fan-Beam DXA." Journal of Applied Biomechanics 24, no. 2 (May 2008): 180–84. http://dx.doi.org/10.1123/jab.24.2.180.

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Body segment inertial parameters are required as input parameters when the kinetics of human motion is to be analyzed. However, owing to interindividual differences in body composition, noninvasive inertial estimates are problematic. Dual-energy x-ray absorptiometry (DXA) is a relatively new imaging approach that can provide cost- and time-effective means for estimating these parameters with minimal exposure to radiation. With the introduction of a new generation of DXA machines, utilizing a fan-beam configuration, this study examined their accuracy as well as a new interpolative data-reduction process for estimating inertial parameters. Specifically, the inertial estimates of two objects (an ultra-high molecular density plastic rod and an animal specimen) and 50 participants were obtained. Results showed that the fan-beam DXA, along with the new interpolative data-reduction process, provided highly accurate estimates (0.10–0.39%). A greater variance was observed in the center of mass location and moment of inertia estimates, likely as a result of the course end-point location (1.31 cm). However, using a midpoint interpolation of the end-point locations, errors in the estimates were greatly reduced for the center of mass location (0.64–0.92%) and moments of inertia (–0.23 to –0.48%).

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Sun, Ning, Jin Wang, and Fang Hua Lei. "A New Method to Measure Inertial Parameters of Rigid Body Based on Energy Decay Theory." Advanced Materials Research 146-147 (October 2010): 151–55. http://dx.doi.org/10.4028/www.scientific.net/amr.146-147.151.

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Most measuring methods used now can only measure the rigid body’s six inertial parameters like the moment of inertia, the product of inertia and the centre of inertia toward to two-dimensional reference system. So a new method which can measure all the nine inertial parameters toward to three-dimensional reference system is proposed. The moment of inertia of object rotating the axis is obtained by energy decay method. Through using the translation and rotation transformation theory of product of inertia, the formula of moment of inertia including the information of product of inertia and centre of inertia is deduced. Then equations are built to solve all the parameters. Furthermore, a measuring instrument is designed based on the aerostatic bearing. Results show that this new method is available and by analyzing the experimental data, suggestions are made to improve this measuring method.

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Wang, Zhe, Xisheng Li, Xiaojuan Zhang, Yanru Bai, and Chengcai Zheng. "Real-time location estimation for indoor navigation using a visual-inertial sensor." Sensor Review 40, no. 4 (June 10, 2020): 455–64. http://dx.doi.org/10.1108/sr-01-2020-0014.

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Purpose The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data. Design/methodology/approach First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia. Findings In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310. Originality/value This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

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Ushaq, Muhammad, and Jian Cheng Fang. "An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems." Applied Mechanics and Materials 245 (December 2012): 323–29. http://dx.doi.org/10.4028/www.scientific.net/amm.245.323.

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Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

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Gao, Zhenyi, Jiayang Sun, Haotian Yang, Jiarui Tan, Bin Zhou, Qi Wei, and Rong Zhang. "Exploration and Research of Human Identification Scheme Based on Inertial Data." Sensors 20, no. 12 (June 18, 2020): 3444. http://dx.doi.org/10.3390/s20123444.

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The identification work based on inertial data is not limited by space, and has high flexibility and concealment. Previous research has shown that inertial data contains information related to behavior categories. This article discusses whether inertial data contains information related to human identity. The classification experiment, based on the neural network feature fitting function, achieves 98.17% accuracy on the test set, confirming that the inertial data can be used for human identification. The accuracy of the classification method without feature extraction on the test set is only 63.84%, which further indicates the need for extracting features related to human identity from the changes in inertial data. In addition, the research on classification accuracy based on statistical features discusses the effect of different feature extraction functions on the results. The article also discusses the dimensionality reduction processing and visualization results of the collected data and the extracted features, which helps to intuitively assess the existence of features and the quality of different feature extraction effects.

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Gildeh, B. S., and S. Asghari. "Inertial capability index based on fuzzy data." International Journal of Metrology and Quality Engineering 2, no. 1 (2011): 45–49. http://dx.doi.org/10.1051/ijmqe/2011008.

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Svensson, A., and J. Holst. "Integration of Navigation Data." Journal of Navigation 48, no. 1 (January 1995): 114–35. http://dx.doi.org/10.1017/s0373463300012558.

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This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.

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Zhang, Shuang, Ada Zhen, and Robert L. Stevenson. "A Dataset for Deep Image Deblurring Aided by Inertial Sensor Data." Electronic Imaging 2020, no. 14 (January 26, 2020): 379–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.14.coimg-379.

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Recent work in image deblurring aided by inertial sensor data has shown promise. Separate work has also shown that deep learning techniques are useful for the image deblurring problem. Due to a lack of a proper dataset, however, deep learning techniques have not yet to be successfully applied to image deblurring when inertial sensor data is also available. This paper proposes to generate a synthetic training and testing dataset that includes groundtruth and blurry image pairs as well as inertial sensor data recorded during the exposure time of each blurry image. To simulate the real situations, the proposed dataset called DeblurIMUDataset considers synchronization issue, rotation center shift, rolling shutter effect as well as inertial sensor data noise and image noise. This dataset is available online.

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Molinari, John, Michaela Rosenmayer, David Vollaro, and Sarah D. Ditchek. "Turbulence Variations in the Upper Troposphere in Tropical Cyclones from NOAA G-IV Flight-Level Vertical Acceleration Data." Journal of Applied Meteorology and Climatology 58, no. 3 (March 2019): 569–83. http://dx.doi.org/10.1175/jamc-d-18-0148.1.

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AbstractThe NOAA G-IV aircraft routinely measures vertical aircraft acceleration from the inertial navigation system at 1 Hz. The data provide a measure of turbulence on a 250-m horizontal scale over a layer from 12.8- to 14.8-km elevation. Turbulence in this layer of tropical cyclones was largest by 35%–40% in the inner 200 km of radius and decreased monotonically outward to the 1000-km radius. Turbulence in major hurricanes exceeded that in weaker tropical cyclones. Turbulence data points were divided among three regions of the tropical cyclone: cirrus canopy; outside the cirrus canopy; and a transition zone between them. Without exception, turbulence was greater within the canopy and weaker outside the canopy. Nighttime turbulence exceeded daytime turbulence for all radii, especially within the cirrus canopy, implicating radiative forcing as a factor in turbulence generation. A case study of widespread turbulence in Hurricane Ivan (2004) showed that interactions between the hurricane outflow channel and westerlies to the north created a region of absolute vorticity of −6 × 10−5 s−1 in the upper troposphere. Outflow accelerated from the storm center into this inertially unstable region, and visible evidence for turbulence and transverse bands of cirrus appeared radially inward of the inertially unstable region. It is argued that both cloud-radiative forcing and the development of inertial instability within a narrow outflow layer were responsible for the turbulence. In contrast, a second case study (Isabel 2003) displayed strong near-core turbulence in the presence of large positive absolute vorticity and no local inertial instability. Peak turbulence occurred 100 km downwind of the eyewall convection.

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Nugroho, FX Satriyo Dwi. "Kajian Inertial Measurement Unit Berbasis Arduino Untuk Dokumentasi Digital Motion Capture Tarian Tradisional." Journal of Animation & Games Studies 2, no. 2 (January 18, 2017): 251. http://dx.doi.org/10.24821/jags.v2i2.1423.

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Visual digital documentation of traditional dance in Indonesia is still limited to photographs and videos recording. Motion capture technology has the potential to add more depth documenting traditional dances. This technology maps the position of the model (in this case the human body) and its motion in three dimensions. There are two popular ways in recording motion capture, using Vision Based Camera and Inertial measurement unit. Inertial Measurement Unit works by combining accelerometer and gyroscope to detect changes in the rotation axis relative lateral and angular. Those changes will be interpreted Arduino micro-controller platform as functions of motions that recorded as a motion capture data. Motion capture data that was obtained from traditional dance in Indonesia can be applied for many things such as education, standardization, documentation, and preservation of cultural assetsKeywords: digital documentatuion, motion capture, inertia measurement unit, angular relative, digital heritage. Abstrak Dokumentasi digital secara visual untuk tari tradisional di Indonesia masih terbatas pada perekaman secara fotografis dan videografis. Teknologi motion capture memiliki potensi untuk menambah kekayaan dokumentasi untuk tari tradisional. Teknologi ini memetakan posisi model (dalam hal ini tubuh manusia) dan pergerakannya secara 3 dimensi. Ada dua cara yang populer dalam perekaman motion capture, menggunakan Vision Based Camera dan Inertial measurement unit. Inertial Measurement Unit bekerja dengan menggabungkan accelerometer dan gyroscope untuk mendeteksi perubahan sumbu rotasi secara lateral dan angular relative. Perubahan ini yang oleh platform mikro-kontroler Arduino akan diterjemahkan sebagai fungsi gerakan yang nantinya akan direkam sebagai data motion capture. Data dokumentasi digital motion capture yang didapat dari perekaman gerak tari tradisional di Indonesia dapat diaplikasikan untuk banyak hal seperti edukasi, standarisasi, pembuatan animasi, game, dan pelestarian aset budaya. Kata kunci: dokumentasi digital, motion capture, inertia measurement unit, angular relative, pelestarian asset budaya

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Kaushik, Keshav, Guru Deep Singh, and P. K. Jain. "Design and Development of Inertia Dynamometer for FSAE Application." International Journal of Advance Research and Innovation 7, no. 2 (2019): 125–28. http://dx.doi.org/10.51976/ijari.721919.

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This paper discusses the development process of a reliable and cost-effective inertia dynamometer for FSAE application.The testing procedure is to accelerate an inertial mass (flywheel) from rest to its maximum speedand calculate the engine power versus speed using the inertia of the flywheel and its rate of change of angular speed. The dimensions of the inertial load, bearings, shaft and foundation have been selected based on theoretical calculations and structural analysis. A Hall- effect sensor has been used to measure instantaneous speed during test. This data is logged through Arduino-Uno and processed using MATLAB and Ms-Excel. The results were satisfactorily similar to that from the power curve supplied by the OEM and engine model developed on Ricardo WAVE software hence proving the accuracy of the dynamometer.

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KUCHERENKO, Yu A., A. P. PYLAEV, S. I. BALABIN, V. D. MURZAKOV, R. I. ARDASHOVA, V. N. POPOV, O. R. KOMAROV, et al. "Behavior of turbulized mixtures at the stage of inertial motion for different atwood numbers." Laser and Particle Beams 18, no. 2 (April 2000): 163–69. http://dx.doi.org/10.1017/s0263034600182023.

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Behavior of the turbulized mixtures at the stage of their inertial motion (with removed acceleration) has been studied. Turbulized mixtures were obtained at the contact boundary of two different density fluids as a result of the Rayleigh–Taylor instability (RTI) development. At some instant of time t*, the acceleration sign was changed and conditions for the mixture separation were created. Then at the instant of time t**, the acceleration was removed and the turbulized mixture continued to move under force of inertia. Experiments have been performed at the installations EKAP and SOM. At the stage of inertial motion, the average density distribution of substance in the mixture region and the width of the turbulized mixture region have been obtained. The data on the mixture region expansion at the stage of inertial motion have been obtained as well.

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Sferlazza, Antonino, Luca Zaccarian, Giovanni Garraffa, and Filippo D’Ippolito. "Localization from Inertial Data and Sporadic Position Measurements." IFAC-PapersOnLine 53, no. 2 (2020): 5976–81. http://dx.doi.org/10.1016/j.ifacol.2020.12.1654.

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Wahlstrom, Johan, and Manon Kok. "Three Symmetries for Data-Driven Pedestrian Inertial Navigation." IEEE Sensors Journal 22, no. 6 (March 15, 2022): 5797–805. http://dx.doi.org/10.1109/jsen.2022.3146646.

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Bancroft, Jared B., and Gérard Lachapelle. "Data Fusion Algorithms for Multiple Inertial Measurement Units." Sensors 11, no. 7 (June 29, 2011): 6771–98. http://dx.doi.org/10.3390/s110706771.

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Stieber, Sebastian, Rainer Dorsch, and Christian Haubelt. "Accurate Sample Time Reconstruction of Inertial FIFO Data." Sensors 17, no. 12 (December 13, 2017): 2894. http://dx.doi.org/10.3390/s17122894.

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Caruso, John F., Sam C. Kucera, Parmeswar K. Hari, Jessica R. Mc Lagan, Nathan M. Olson, Catherine M. Shepherd, and Mallory R. Marshall. "Data Reproducibility From an Inertial Kinetic Exercise Machine." Journal of Strength and Conditioning Research 24, no. 11 (November 2010): 3081–87. http://dx.doi.org/10.1519/jsc.0b013e3181bf0211.

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Allerton, D. J., and H. Jia. "Distributed data fusion algorithms for inertial network systems." IET Radar, Sonar & Navigation 2, no. 1 (February 1, 2008): 51–62. http://dx.doi.org/10.1049/iet-rsn:20060159.

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Qian, Gang, Rama Chellappa, and Qinfen Zheng. "Robust structure from motion estimation using inertial data." Journal of the Optical Society of America A 18, no. 12 (December 1, 2001): 2982. http://dx.doi.org/10.1364/josaa.18.002982.

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Smedman, Ann-Sofi. "Some Additional Coherence Data in the Inertial Subrange." Journal of Climate and Applied Meteorology 26, no. 12 (December 1987): 1770–73. http://dx.doi.org/10.1175/1520-0450(1987)026<1770:sacdit>2.0.co;2.

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Sheffels, M. L. "A fault-tolerant air data/inertial reference unit." IEEE Aerospace and Electronic Systems Magazine 8, no. 3 (March 1993): 48–52. http://dx.doi.org/10.1109/62.199822.

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McClary, C. R. "A fault-tolerant air data/inertial reference system." IEEE Aerospace and Electronic Systems Magazine 7, no. 5 (May 1992): 19–23. http://dx.doi.org/10.1109/62.257088.

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Xia, Xi, Chengming He, and Peng Zhang. "Universality in the viscous-to-inertial coalescence of liquid droplets." Proceedings of the National Academy of Sciences 116, no. 47 (November 5, 2019): 23467–72. http://dx.doi.org/10.1073/pnas.1910711116.

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We present a theory on the coalescence of 2 spherical liquid droplets that are initially stationary. The evolution of the radius of a liquid neck formed upon coalescence was formulated as an initial value problem and then solved to yield an exact solution without free parameters, with its 2 asymptotic approximations reproducing the well-known scaling relations in the inertially limited viscous and inertial regimes. The viscous-to-inertial crossover observed in previous research is also recovered by the theory, rendering the collapse of data of different viscosities onto a single curve.

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Alvarellos, Alberto, Adrián Vázquez, and Juan Rabuñal. "Raspberry Pimu: Raspberry Pi Based Inertial Sensor Data Processing System." Proceedings 2, no. 18 (September 18, 2018): 1159. http://dx.doi.org/10.3390/proceedings2181159.

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This paper explains the architectural design and development of an application for the reception, visualization and storage of inertial sensor data provided by an inertial measurement system (IMU). The application is built to run in a Raspberry Pi equipped with a small size screen that allows the visualization of the data and the control of data recording. The IMU is connected to a Raspberry Pi through a serial port (USB-TTY).

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Zhu, Yiqi, Jinglin Zhang, Yanping Zhu, Bin Zhang, and Weize Ma. "RBCN-Net: A Data-Driven Inertial Navigation Algorithm for Pedestrians." Applied Sciences 13, no. 5 (February 25, 2023): 2969. http://dx.doi.org/10.3390/app13052969.

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Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.

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Gao, Tong, Wei Sheng, Mingliang Zhou, Bin Fang, and Liping Zheng. "MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 14 (May 18, 2020): 2059048. http://dx.doi.org/10.1142/s021800142059048x.

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In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

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Cui, Langfu, Qingzhen Zhang, Liman Yang, and Chenggang Bai. "A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform." Remote Sensing 13, no. 23 (November 30, 2021): 4864. http://dx.doi.org/10.3390/rs13234864.

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An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

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El-Rabbany, Ahmed, and Mohammed El-Diasty. "An Efficient Neural Network Model for De-noising of MEMS-Based Inertial Data." Journal of Navigation 57, no. 3 (August 24, 2004): 407–15. http://dx.doi.org/10.1017/s0373463304002875.

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Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

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Wang, Feng Yuan, De Hui Huang, and Sheng Li. "Identification of Inertial Parameters of Heavy Truck Powertrains." Applied Mechanics and Materials 43 (December 2010): 225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.43.225.

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Based on the quality of line frequency response function and the principle of rigid body micro-vibration, a technique was proposed to verify the experimental accuracy for the heavy truck powertrain by using rigid body inertia tensor transformation and additive theory. The measurement of inertial parameters of a heavy truck powertrain was carried out by hammer method. The total least square processing theory was proposed to process the experimental data. The experimental results showed satisfactory accuracy and reliability.

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McDaniel, Herbert, and Laurence Hopkins. "IUS Acceptance Failure Data Base." Journal of the IEST 28, no. 1 (January 1, 1985): 25–29. http://dx.doi.org/10.17764/jiet.1.28.1.64850t7m32812752.

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This paper describes the failure data base obtained in component and vehicle level acceptance tests of the Inertial Upper Stage (IUS). Failure trends are presented as a function of test environment, failure classification, and testing sequence for eight flight vehicles.

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Shi, Zhenlian, Yanfeng Sun, Linxin Xiong, Yongli Hu, and Baocai Yin. "A Multisource Heterogeneous Data Fusion Method for Pedestrian Tracking." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/150541.

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Traditional visual pedestrian tracking methods perform poorly when faced with problems such as occlusion, illumination changes, and complex backgrounds. In principle, collecting more sensing information should resolve these issues. However, it is extremely challenging to properly fuse different sensing information to achieve accurate tracking results. In this study, we develop a pedestrian tracking method for fusing multisource heterogeneous sensing information, including video, RGB-D sequences, and inertial sensor data. In our method, a RGB-D sequence is used to position the target locally by fusing the texture and depth features. The local position is then used to eliminate the cumulative error resulting from the inertial sensor positioning. A camera calibration process is used to map the inertial sensor position onto the video image plane, where the visual tracking position and the mapped position are fused using a similarity feature to obtain accurate tracking results. Experiments using real scenarios show that the developed method outperforms the existing tracking method, which uses only a single sensing dataset, and is robust to target occlusion, illumination changes, and interference from similar textures or complex backgrounds.

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Wang, Peiguang, Sheng Ma, Haitao Nie, Zhibin Zang, Jianwei Zhao, Hangfan Zhou, and Cong Wang. "Study of a Multi-Fusion Positioning System Based on Beidou Indoor Inertial Navigation." Journal of Physics: Conference Series 2083, no. 2 (November 1, 2021): 022004. http://dx.doi.org/10.1088/1742-6596/2083/2/022004.

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Abstract This article proposes an indoor combined positioning terminal based on Beidou pseudofiles and micro-inertial navigation sensors. Through the built-in Beidou pseudo lite positioning IP soft core, it can receive and analyse Beidou satellite signals. The article creates BDS ground inertial positioning data receiving hardware; performs inertial positioning data primary information identification authentication on the inertial data received by the hardware; performs multi-inertial data fusion estimation calculation on inertial positioning data after authentication, reduces the dimensional error value, and completes the proposed system Design. The program makes full use of the data resources of the existing airborne equipment, does not need to transmit radio signals, and the user capacity is not limited, which is suitable for highly dynamic users. Through simulation and sports car test, it is proved that the scheme is feasible.

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Wa’ie Hazman, Muhammad Ajwad, Ili Najaa Aimi Mohd Nordin, Faridah Hanim Mohd Noh, Nurulaqilla Khamis, M. R. M. Razif, Ahmad Athif Faudzi, and Asyikin Sasha Mohd Hanif. "IMU sensor-based data glove for finger joint measurement." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 82. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp82-88.

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<p>The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents an inertial measurement unit sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors, MPU-6050 and two flexible bend sensors which are capable to detect angle displacement were attached to the distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joint points on the glove. The data taken from inertial measurement unit sensors and flexible bend sensors were acquired using Arduino and MATLAB software interface. The data obtained were compared with the reference data measured from goniometer to allow for accurate comparative measurement. The percentage of error resulted from MPU-6050 sensor unit were ranged from 0.81 % to 5.41 % were very low which indicates high accuracy when compared with the measurements obtained using goniometer. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor-based data glove using MPU-6050 sensors can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating MPU-6050 sensors on all five fingers.</p>

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Bova, Matteo, Matteo Massaro, and Nicola Petrone. "A Three-Dimensional Parametric Biomechanical Rider Model for Multibody Applications." Applied Sciences 10, no. 13 (June 29, 2020): 4509. http://dx.doi.org/10.3390/app10134509.

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Bicycles and motorcycles are characterized by large rider-to-vehicle mass ratios, thus making estimation of the rider’s inertia especially relevant. The total inertia can be derived from the body segment inertial properties (BSIP) which, in turn, can be obtained from the prediction/regression formulas available in the literature. Therefore, a parametric multibody three-dimensional rider model is devised, where the four most-used BSIP formulas (herein named Dempster, Reynolds-NASA, Zatsiorsky–DeLeva, and McConville–Young–Dumas, after their authors) are implemented. After an experimental comparison, the effects of the main posture parameters (i.e., torso inclination, knee distance, elbow distance, and rider height) are analyzed in three riding conditions (sport, touring, and scooter). It is found that the elbow distance has a minor effect on the location of the center of mass and moments of inertia, while the effect of the knee distance is on the same order magnitude as changing the BSIP data set. Torso inclination and rider height are the most relevant parameters. Tables with the coefficients necessary to populate the three-dimensional rider model with the four data sets considered are given. Typical inertial parameters of the whole rider are also given, as a reference for those not willing to implement the full multibody model.

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Guang, Xingxing, Yanbin Gao, Pan Liu, and Guangchun Li. "IMU Data and GPS Position Information Direct Fusion Based on LSTM." Sensors 21, no. 7 (April 3, 2021): 2500. http://dx.doi.org/10.3390/s21072500.

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In recent years, the application of deep learning to the inertial navigation field has brought new vitality to inertial navigation technology. In this study, we propose a method using long short-term memory (LSTM) to estimate position information based on inertial measurement unit (IMU) data and Global Positioning System (GPS) position information. Simulations and experiments show the practicability of the proposed method in both static and dynamic cases. In static cases, vehicle stop data are simulated or recorded. In dynamic cases, uniform rectilinear motion data are simulated or recorded. The value range of LSTM hyperparameters is explored through both static and dynamic simulations. The simulations and experiments results are compared with the strapdown inertial navigation system (SINS)/GPS integrated navigation system based on kalman filter (KF). In a simulation, the LSTM method’s computed position error Standard Deviation (STD) was 52.38% of what the SINS computed. The biggest simulation radial error estimated by the LSTM method was 0.57 m. In experiments, the LSTM method computed a position error STD of 23.08% using only SINSs. The biggest experimental radial error the LSTM method estimated was 1.31 m. The position estimated by the LSTM fusion method has no cumulative divergence error compared to SINS (computed). All in all, the trained LSTM is a dependable fusion method for combining IMU data and GPS position information to estimate position.

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Xia, Di, Yeqing Zhu, and Heng Zhang. "Faster Deep Inertial Pose Estimation with Six Inertial Sensors." Sensors 22, no. 19 (September 21, 2022): 7144. http://dx.doi.org/10.3390/s22197144.

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We propose a novel pose estimation method that can predict the full-body pose from six inertial sensors worn by the user. This method solves problems encountered in vision, such as occlusion or expensive deployment. We address several complex challenges. First, we use the SRU network structure instead of the bidirectional RNN structure used in previous work to reduce the computational effort of the model without losing its accuracy. Second, our model does not require joint position supervision to achieve the best results of the previous work. Finally, since sensor data tend to be noisy, we use SmoothLoss to reduce the impact of inertial sensors on pose estimation. The faster deep inertial poser model proposed in this paper can perform online inference at 90 FPS on the CPU. We reduce the impact of each error by more than 10% and increased the inference speed by 250% compared to the previous state of the art.

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Xu, Zheng, Zhong Su, and Dongyue Dai. "LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base." Applied Sciences 13, no. 4 (February 20, 2023): 2705. http://dx.doi.org/10.3390/app13042705.

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In order to accurately locate personnel in underground spaces, positioning equipment is required to be mounted on wearable equipment. But the wearable inertial personnel positioning equipment moves with personnel and the phenomenon of measurement reference wobble (referred to as moving base) is bound to occur, which leads to inertial measurement errors and makes the positioning accuracy degraded. A neural network-assisted binocular visual-inertial personnel positioning method is proposed to address this problem. Using visual-inertial Simultaneous Localization and Mapping to generate ground truth information (including position, velocity, acceleration data, and gyroscope data), a trained neural network is used to regress 6-dimensional inertial measurement data from the IMU data fragment under the moving base, and a position loss function is constructed based on the regressed inertial data to reduce the inertial measurement error. Finally, using vision as the observation quantity, the point feature and inertial measurement data are tightly coupled to optimize the mechanism to improve the personnel positioning accuracy. Through the actual scene experiment, it is verified that the proposed method can improve the positioning accuracy of personnel. The positioning error of the proposed algorithm is 0.50%D, and it is reduced by 92.20% under the moving base.

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Pham, Thanh, and Young Suh. "Spline Function Simulation Data Generation for Walking Motion Using Foot-Mounted Inertial Sensors." Electronics 8, no. 1 (December 23, 2018): 18. http://dx.doi.org/10.3390/electronics8010018.

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This paper investigates the generation of simulation data for motion estimation using inertial sensors. The smoothing algorithm with waypoint-based map matching is proposed using foot-mounted inertial sensors to estimate position and attitude. The simulation data are generated using spline functions, where the estimated position and attitude are used as control points. The attitude is represented using B-spline quaternion and the position is represented by eighth-order algebraic splines. The simulation data can be generated using inertial sensors (accelerometer and gyroscope) without using any additional sensors. Through indoor experiments, two scenarios were examined include 2D walking path (rectangular) and 3D walking path (corridor and stairs) for simulation data generation. The proposed simulation data is used to evaluate the estimation performance with different parameters such as different noise levels and sampling periods.

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Li, Chao, Korkut Tokgoz, Masamoto Fukawa, Jim Bartels, Takumi Ohashi, Ken-ichi Takeda, and Hiroyuki Ito. "Data Augmentation for Inertial Sensor Data in CNNs for Cattle Behavior Classification." IEEE Sensors Letters 5, no. 11 (November 2021): 1–4. http://dx.doi.org/10.1109/lsens.2021.3119056.

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Mohd Sultan, Juwita, Nurul Huda Zani, Mohd Azuani, Siti Zuraidah Ibrahim, and Azdiana Md Yusop. "Analysis of Inertial Measurement Accuracy using Complementary Filter for MPU6050 Sensor." Jurnal Kejuruteraan 34, no. 5 (September 30, 2022): 959–64. http://dx.doi.org/10.17576/jkukm-2022-34(5)-24.

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Inertial can be defined as disinclination to motion, action, or change. The inertia of an object is the propensity to remain at rest or if in motion, stays in motion at a steady speed. MPU6050 is one of the low-cost motion tracking sensors that contain a 3-axis gyroscope and 3-axis accelerometer orientation measurement. It is used to analyse the movement or location of a person in an indoor environment. This research is to analyse the accuracy of the inertial measurement of the MPU 6050 sensor. Next, is to improve the achievable accuracy rate up to 95% using the complementary filter and finally to visualize the results on an IoT platform. This MPU6050 sensor is beneficial to an emergency responder such as the firefighter’s department. The accurate inertial measurement and location will help to detect the movement and the motion of the firefighter during operation, especially in an indoor environment. The sensor will detect and collects the inertial measurement of an emergency responder and transmit the data wirelessly by using ESP8266 NodeMCU. Finally, the results can be viewed on an IoT platform. However, the results obtained from the MPU 6050 sensor is not perfectly accurate as there is noise during the measurement. Therefore, a complementary filter is used and analysed in this research. It is expected that the inertial location’s accuracy could be improved by 95% that will provide a precise movement and location of the firefighter during operation.

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Diete, Alexander, and Heiner Stuckenschmidt. "Fusing Object Information and Inertial Data for Activity Recognition." Sensors 19, no. 19 (September 23, 2019): 4119. http://dx.doi.org/10.3390/s19194119.

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In the field of pervasive computing, wearable devices have been widely used for recognizing human activities. One important area in this research is the recognition of activities of daily living where especially inertial sensors and interaction sensors (like RFID tags with scanners) are popular choices as data sources. Using interaction sensors, however, has one drawback: they may not differentiate between proper interaction and simple touching of an object. A positive signal from an interaction sensor is not necessarily caused by a performed activity e.g., when an object is only touched but no interaction occurred afterwards. There are, however, many scenarios like medicine intake that rely heavily on correctly recognized activities. In our work, we aim to address this limitation and present a multimodal egocentric-based activity recognition approach. Our solution relies on object detection that recognizes activity-critical objects in a frame. As it is infeasible to always expect a high quality camera view, we enrich the vision features with inertial sensor data that monitors the users’ arm movement. This way we try to overcome the drawbacks of each respective sensor. We present our results of combining inertial and video features to recognize human activities on different types of scenarios where we achieve an F 1 -measure of up to 79.6%.

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Chen, Chengjun, Kai Huang, Dongnian Li, Yong Pan, Zhengxu Zhao, and Jun Hong. "Assembly torque data regression using sEMG and inertial signals." Journal of Manufacturing Systems 60 (July 2021): 1–10. http://dx.doi.org/10.1016/j.jmsy.2021.04.011.

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Zhang, Hongda, and Ting Zhang. "Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion." Mathematical Problems in Engineering 2021 (February 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/6657362.

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Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

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Gagnon, Eric, Alexandre Vachon, and Yanick Beaudoin. "Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units." Sensors 18, no. 6 (June 12, 2018): 1910. http://dx.doi.org/10.3390/s18061910.

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Ch, Nagadeepa, Balaji N, and Padmaja V. "Analysis of Inertial Sensor Data Using Trajectory Recognition Algorithm." International Journal on Cybernetics & Informatics 5, no. 4 (August 30, 2016): 101–8. http://dx.doi.org/10.5121/ijci.2016.5412.

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Field, Matthew, David Stirling, Zengxi Pan, Montserrat Ros, and Fazel Naghdy. "Recognizing human motions through mixture modeling of inertial data." Pattern Recognition 48, no. 8 (August 2015): 2394–406. http://dx.doi.org/10.1016/j.patcog.2015.03.004.

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Schwesig, René, Siegfried Leuchte, David Fischer, Regina Ullmann, and Alexander Kluttig. "Inertial sensor based reference gait data for healthy subjects." Gait & Posture 33, no. 4 (April 2011): 673–78. http://dx.doi.org/10.1016/j.gaitpost.2011.02.023.

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Baranek, R. "Inertial Measurement Unit – Data Fusion and Visualization using MATLAB." IFAC Proceedings Volumes 45, no. 7 (2012): 12–16. http://dx.doi.org/10.3182/20120523-3-cz-3015.00005.

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Alam, Mushfiqul, and Jan Rohac. "Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth." Sensors 15, no. 2 (February 2, 2015): 3282–98. http://dx.doi.org/10.3390/s150203282.

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ANDERSON, R. C., J. A. DAVENPORT, and C. JEKELI. "Determination of Gravity Data Spacing Required For Inertial Navigation." Navigation 47, no. 1 (March 2000): 1–6. http://dx.doi.org/10.1002/j.2161-4296.2000.tb02417.x.

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Journal articles on the topic 'Inertial data'

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