80 results about "Accelerometer data" patented technology

The invention provides an oil well load indicator diagram prediction method and system, and the method comprises the steps: synchronously collecting an initial electrical parameter data set, a load data set, an accelerometer data set and a barometer data set of an oil well, the initial electrical parameter data set comprising an active power data unit, and then obtaining an active power data segment and a load data segment; obtaining a time lag based on the two, and further obtaining a final electrical parameter data segment; acquiring a reference displacement sequence through the accelerometer data set and the barometer data set; obtaining a dynamic stroke length based on the reference displacement sequence and the load data segment, and further obtaining a final displacement sequence; and constructing a prediction neural network, and obtaining an oil well load indicator diagram based on the prediction neural network and the real-time electrical parameter data segment. According to the technical scheme, the indicator diagram is obtained through continuous and high-frequency real-time electric parameter data segments, motion period information loss caused by low-frequency data collection is avoided, accumulative errors are avoided, and the accuracy of the indicator diagram is ensured.

The invention relates to the technical field of data calibration, and particularly discloses an attitude calibration method and system for a self-adaptive anti-shake handheld three-dimensional scanner, and the method comprises the steps: reading IMU initial data to initialize an attitude calculation algorithm and an initial gyroscope zero offset estimation value, carrying out the real-time compensation of the original angular velocity data of a gyroscope based on the current zero offset estimation value, and carrying out the calibration of the attitude of the self-adaptive anti-shake handheld three-dimensional scanner. Second attitude data is obtained by resolving accelerometer data; a final attitude is output through complementary filtering fusion; the quasi-static state of a scanner is analyzed and judged through IMU data variance; original data of a gyroscope is collected in the state to solve an average value; and the zero offset estimation value is updated through first-order low-pass filtering and is fed back to the compensation step. According to the method, hardware cost does not need to be increased, whole-process online self-calibration is achieved, attitude drift is reduced, the algorithm is efficiently adaptive to a low-cost processor, and long-time scanning precision and real-time performance are guaranteed.

A controller for a vehicle, the vehicle comprising an imaging device for generating image data and the imaging device further comprising an imaging accelerometer for generating imaging accelerometer data for determining the orientation of the imaging device relative to the vehicle, the controller comprising: an input for receiving a signal indicative of the imaging accelerometer data and the generated image data; a processor arranged to identify alignment artefacts in the received image data in dependence on the received imaging accelerometer data; and an output for outputting an error signal in dependence on the identified alignment artefacts.

This invention discloses a method and system for detecting the angle between a tractor and trailer based on a dual-antenna combined inertial navigation system, applicable to vehicle systems. The method includes: acquiring the position difference vector between the main and auxiliary antennas using a dual-antenna GNSS system on the tractor; calculating the first coarse heading angle of the tractor; and then fusing IMU data and filtering it to obtain a high-precision first heading angle. The roll and pitch angles are calculated using accelerometer data from the trailer's IMU, and tilt compensation is performed on the magnetometer data to obtain the second heading angle of the trailer. Furthermore, the heading and lane curvature data from the tractor can be combined to dynamically compensate and correct the trailer's heading online. The difference between the first and second heading angles is calculated in real time to obtain the angle between them. This invention can maintain heading output using IMU and wheel speed data even when GNSS signals fail, and ensure trailer heading accuracy through dynamic compensation, thereby achieving stable and high-precision tractor-trailer angle measurement.

The invention relates to an out-of-levelness estimation method, device and equipment of an inclinometer and a medium, and belongs to the technical field of inclinometers. The method comprises the following steps: in a hardware environment without a gyroscope, acquiring initial attitude angle information through accelerometer data sampling and alignment; performing attitude matrix construction, navigation coordinate system projection, Kalman filter modeling, attitude error calculation and error compensation to equivalently realize a gyroscope-free zero-velocity correction effect, and obtaining a final attitude angle; a self-adaptive Kalman filter based on gravity acceleration module value constant constraint is provided, so that the influence of external interference on the measurement precision can be reduced; and finally, performing smooth filtering on the final attitude angles at a plurality of adjacent sampling moments, and outputting high-precision and high-stability unlevelness. According to the method, under the condition that the hardware cost is not increased, the measurement precision and stability of the inclinometer on the out-of-levelness are improved.

The present invention pertains to an advanced method for automatically detecting and recognizing wellness activities and kinetic movements using accelerometer data from wearable devices. This method involves creating a user profile, linking the user to their wearable device via specific identifiers (make, type, and unique device ID), and processing accelerometer data to develop and continuously update a supervised machine learning model. This model accurately identifies and classifies various physical activities by utilizing detailed device information and user preferences. Designed for scalability, the system integrates new wearable devices and models as they emerge. It is particularly effective in tracking activities across diverse environments, including homes and fitness centers. Moreover, it identifies and associates ICD-10 activity codes with the performed activities and integrates with Electronic Medical Records (EMR) systems. This method offers valuable applications for fitness centers, healthcare providers, government agencies, and payers interested in monitoring or promoting physical wellness through reliable activity tracking.

Implantable medical devices and methods that utilize a sensing circuit to sense cardiac activity (CA) signals and an accelerometer to obtain accelerometer data. Memory stores program instructions and device parameters associated with a collection of ventricular arrhythmia (VA) therapies. A processor analyzes the CA signals over one or more cardiac beats, determines a VA episode based on the analysis of the CA signals, determines the VA episode to be hemodynamically stable or unstable based on at least one of the accelerometer data or the CA signals, selects a first VA therapy from the collection of VA therapies based on whether the VA episode is hemodynamically stable or unstable, and apply the first VA therapy using at least one of the at least two coils.

The invention belongs to the technical field of atmospheric density prediction, and discloses a cross-source data fusion thermal layer atmospheric density hierarchical progressive full-scale prediction system and method, which utilize respective advantages of different gradient data sources to construct a reference density field-corrected density field-instantaneous refined density field three-level hierarchical fusion refinement architecture. Cross-source data calibration and physical constraint modeling are combined to realize month-year scale long-term prediction, day-week scale medium-short-term prediction and short-term prediction of the thermal layer atmospheric density. According to the method, through a three-level layered refinement architecture, the long-term coverage advantage of TLE data, the mesoscale variable rate description advantage of precise orbit data and the high-frequency and high-precision advantages of accelerometer data are fully mined, the defect of a single data source is avoided, full-scale and full-area density precise description is achieved, the thermal layer atmospheric density refinement precision and prediction suitability can be improved, and the method is suitable for large-scale and large-scale measurement. And data cost is reduced.

Disclosed herein are methods for determining a location of a hearing aid, performed by an electronic device having a processor, a memory, and interface circuitry. The method includes obtaining first accelerometer data from a hearing aid. The method includes obtaining second accelerometer data from the electronic device. The method includes determining, based on the first accelerometer data and the second accelerometer data, an acceleration parameter indicative of a difference between the first accelerometer data and the second accelerometer data. In accordance with the acceleration parameter meeting a safety criterion, the method includes generating lost control data and outputting the lost control data to the hearing aid.

Activity level and fall detection using accelerometer data is described. In one or more implementations, measurements of a user generated by a wearable monitoring device during an observation period are obtained, the measurements including accelerometer data. Physical steps taken by the user are detected based on peaks in the accelerometer data above a threshold, and a step count is generated based on the detected physical steps within predetermined time epochs. Activity level classifications of the user for the predetermined time epochs are generated based on the step count. The step count and the activity level classification for the predetermined time epochs may then be output, such as a notification or in a user interface. Fall predictions may be generated and output by processing the accelerometer data using machine learning models trained to correlate patterns in accelerometer data to fall events.

The invention relates to a gyro accelerometer data fusion method, and belongs to the technical field of high-precision acceleration measurement inertia, the gyro accelerometer data fusion method is based on an optical fiber gyro inertial navigation system with a three-axis rotating mechanism, a table body can be controlled to keep stable relative to an inertial space, and three orthogonally placed quartz accelerometers are configured on the table body; the method comprises the following steps of: configuring a pendulum-type integral gyro accelerometer in an X direction, normalizing measured values of the quartz accelerometer and the gyro accelerometer by utilizing measurement redundancy of the quartz accelerometer on acceleration information, and compensating output pulses at non-whole circle points of the gyro accelerometer, under the condition of low overload, the advantage of high-precision acceleration measurement of the gyro accelerometer at the whole precession circle point can be brought into full play; according to the invention, the problem that the measurement error of the gyro accelerometer is large under the condition of low overload in the use process of the existing fiber-optic gyro inertial navigation is solved, and the use precision of the gyro accelerometer on an inertial navigation system is improved.

The invention relates to a system (1) for detecting the presence of life inside a motor vehicle (V) by means of a radar (10) and an accelerometer (40), the system being configured to implement the following steps: - processing, in particular by means of a presence-detection algorithm, data from the radar (10) in order to deliver a result regarding a presence of life in the vehicle, - processing data from the accelerometer (40) in order to deliver a result regarding a movement of the vehicle, - processing, in particular by means of a correlation algorithm, the data from the radar (10) and the data from the accelerometer (40) in order to establish a presence of correlation or an absence of correlation between at least one feature derived from the data from the radar (10) and at least one feature derived from the data from the accelerometer (40), the presence of correlation being representative of a state of actual movement of the radar (10), - in the event of presence of correlation, ignoring the result regarding a presence of life in the vehicle obtained by processing the data from the radar (10).

The invention discloses a remote island reference-free control network autonomous construction and transmission method, which belongs to the technical field of geodetic survey and surveying and mapping engineering, and comprises the following steps: receiving sea surface laser ranging, land gravity, mobile platform images and accelerometer data; performing statistics on peak values of the laser ranging histograms to extract local average sea surface elevation; a relative geometric point cloud is generated based on image texture tracking in combination with an accelerometer calculation scale; calculating a vertical line inclination angle by using a difference value between the gravity data and an earth normal gravity field; and correcting a point cloud vertical component by using a difference value between a water line and a sea surface elevation, correcting a plane component by using an inclination angle, and outputting a control point. According to the invention, a mode of fusing sea surface laser ranging, mobile platform images, inertial data and land gravity data is adopted, and a local three-dimensional control network with unified scale, elevation reference and vertical line direction can be autonomously constructed under the condition of no external reference dependence.

A system may obtain one or more engagement measurements including at least one engagement measurement of a surface in a wellbore from an engagement sensor, wherein the engagement sensor is housed in an electronics housing positioned within a body of a downhole tool. A system may obtain accelerometer data from an accelerometer that is concurrent with the one or more engagement measurements. A system may correlate data variations in the one or more engagement measurements and in the accelerometer data, wherein the data variations are values outside of a threshold value. A system may identify at least one drilling dysfunction based on correlated accelerometer data and engagement measurements. A system may change at least one wellbore parameter based on the correlated accelerometer data and engagement measurements.

This invention provides a six-position temperature-compensated calibration method for accelerometers in a strapdown inertial navigation system (SINS), belonging to the field of inertial navigation technology. The specific steps are as follows: The SINS is calibrated for a short time at different temperatures on a dual-axis temperature-controlled turntable without a mounting reference. The accelerometer zero bias, scale coefficient, and installation error at different temperatures are estimated using the least squares method. A cubic polynomial model is established relating the accelerometer zero bias, scale coefficient, and installation error to the actual temperature. The initial temperature scale coefficient is obtained by fitting the curve. Then, six-position calibration is performed on a turntable with a mounting reference to obtain a transformation matrix. The final temperature compensation coefficient is obtained from the transformation matrix and applied to the SINS to reduce the impact of temperature on the accelerometer output. This invention features short accelerometer data acquisition time, high reliability, and ease of operation. It is a method for improving accelerometer output accuracy through accelerometer compensation calibration.

Aspects of the disclosed technology include machine-learning systems and methods for enhancing speech signals. A machine-learned speech enhancement model can be trained using audio data and synthetic accelerometer data that is generated using a synthetic accelerometer data generation model. The accelerometer data can include synthetic noise data that can be used to train the speech enhancement model to learn a transfer function that models environmental noise to an accelerometer of a wearable device. By simulating data from multiple sensors, the disclosed technology provides a cost-effective solution for training robust large-scale models for speech enhancement.

The application provides an oil well load work graph prediction method and system, the method comprising: synchronously collecting initial electric parameter data set, load data set, accelerometer data set and barometer data set of the oil well, the initial electric parameter data set comprising active power data unit, and then obtaining active power data segment and load data segment; obtaining time lag amount based on the two, and then obtaining final electric parameter data segment; obtaining reference displacement sequence through the accelerometer data set and the barometer data set; obtaining dynamic stroke length based on the reference displacement sequence and the load data segment, and then obtaining final displacement sequence; constructing a prediction neural network, and obtaining the oil well load work graph based on the prediction neural network and real-time electric parameter data segment. The work graph is obtained by using continuous and high-frequency real-time electric parameter data segment, so that the motion cycle information loss caused by low-frequency data collection is avoided, and then the cumulative error is avoided, and the accuracy of the work graph is ensured.

The invention relates to transforming microphone data into accelerometer data. In a method for transforming microphone data into accelerometer data, microphone data is received. A device-specific filter is applied to the microphone data to generate first data, the device-specific filter for transforming the microphone first data according to characteristics of the device. The first data is expanded by adding sensor noise to the first data to generate second data, the sensor noise characterizing noise inherent to the device. The second intermediate data is expanded by adding ambient vibration noise defining an environment in which use of the device is simulated to generate accelerometer data.

A system and methodology is provided in which image and video injection attack attempts, which may occur in connection with document or individual verification processes, can be detected and handled. The system and methodology of the present invention, in some embodiments, employs functionalities such as haptic vibration generation and accelerometer data typically present in existing user devices to implement attack detection and response.

The present technology combines low latency sensor data from gyroscopes and accelerometers with accurate but higher latency data from AR frames by reordering a received series of data collected from the gyroscopes, accelerometers, and AR frames so that the AR frame is placed in the series of data earlier in time than it was received—to a time that corresponds with gyroscope and accelerometer data corresponding to a position and time which the AR frame capture was initiated. The correctly ordered data can then be used to project where the user will be looking at a future time so that the virtual reality application can render a scene that corresponds with the user's expectations. In this way, a smartphone can be used to provide a good virtual reality experience when used with a simple viewer, thus making virtual reality experiences more accessible.

The application discloses an end-to-end sleep monitoring and event detection method based on a wrist accelerometer, and comprises the following steps: collecting original motion data through a wrist accelerometer, and extracting a basic feature vector; generating sleep rhythm prior features by using user historical activity data based on the basic feature vector, and splicing the prior features and the basic feature vector to form an enhanced feature vector; converting the enhanced feature vector into a two-dimensional feature map, and outputting a sleep state probability of each time point by using a time series-variable interaction model; performing multi-scale Gaussian smoothing processing on original binary event labels to generate soft labels, and using the soft labels as a supervision signal to train the time series-variable interaction model; performing forward propagation on new accelerometer data based on the trained model to output a probability sequence, and detecting sleep events and performing sleep staging by using post-processing operations; and the application realizes high-precision sleep staging and sleep event detection by using only accelerometer data.

To provide a wearable device that detects motion data and provides feedback based on comparison information or other analysis.SOLUTION: An accelerometer, a processing device, and a feedback apparatus, wherein the processing device is configured to receive data from the accelerometer and compare motion indicated by the data of the accelerometer with pre-stored motion; Wherein the feedback device is calibrated to issue a notification to a user of the feedback device about at least one aspect of the user's motion, wherein the feedback device compares the captured user's motion in the received data of a less-than-full-speed practice motion with the same motion previously stored at full speed to issue the notification.SELECTED DRAWING: Figure 1

The application provides a fusion data broadband coseismic velocity waveform real-time reconstruction method based on cascade filtering, which uses cascade filtering to fuse high-frequency GNSS original Doppler data, high-frequency GNSS carrier phase data and accelerometer data to realize broadband coseismic velocity waveform real-time reconstruction, and the realization process comprises the following steps: high-frequency GNSS / accelerometer co-located station real-time acquisition of high-frequency GNSS observation data, broadcast ephemeris and original acceleration data, and sending to a ground computing center; the ground computing center real-time calculation of high-frequency GNSS TDCP average velocity and high-frequency GNSS RD instantaneous velocity; the ground computing center zero offset correction of the real-time received original acceleration data to obtain zero offset correction acceleration data; and the ground computing center real-time reconstruction of the broadband coseismic velocity waveform of the high-frequency GNSS / accelerometer co-located station according to the high-frequency GNSS TDCP average velocity, the high-frequency GNSS RD instantaneous velocity and the zero offset correction acceleration data by using cascade filtering.

Techniques for physiological monitoring using low sample rate accelerometer data are described and are implementable to generate insights related to user states during extended wear periods. In an example, low sample rate accelerometer data having a sample rate that is below a sample rate threshold is received from a wearable device mounted on a skin surface of a chest region of a user during a wear period. The low sample rate accelerometer data is processed to extract one or more motion-derived parameters that characterize temporal movement patterns of the user during the wear period. An insight for presentation related to a user state during the wear period is generated based on the one or more motion-derived parameters. The insights can include but are not limited to predictions of sleep states, active states, and inactive states, body angle and position determinations, and detection of device inversion events.

A system and method for detecting or verifying recovery from concussion includes a headset or head-attachable device placed against the head of a patient who has exhibited concussion. In the device is an accelerometer for detecting and measuring natural motions of the head due to blood flow in the brain. After a prescribed number of days post-concussion, when the patient returns to physical activity, the accelerometer data from natural motions are analyzed to determine whether concussion persists. Preferably a gross movement accelerometer is also included in the head-attachable device, to record gross movements of the head, so as to correlate any increase in concussion-indicating data with the return to physical activity.

The application discloses a satellite rotating maneuvering accelerometer calibration method based on data optimization, belongs to the technical field of accelerometer calibration, obtains star sensor data of on-orbit measurement of a gravity satellite, adopts a first processing method to perform first preprocessing on the star sensor data, and calculates satellite angular acceleration data according to the star sensor data; obtains accelerometer data of on-orbit measurement of the gravity satellite, and adopts a second processing method to perform second preprocessing; obtains a first correlation coefficient; obtains a first star sensor data section and a first accelerometer data section; obtains a first weight factor from the first correlation coefficient; adopts an optimized data selection method to calculate an accelerometer scale factor according to the first star sensor data section and the first accelerometer data section; and obtains on-orbit calibration results of the satellite accelerometer scale factor according to the accelerometer scale factor and the first weight factor, so that the technical effect of improving the data quality of satellite maneuvering calibration is achieved.

A CPR training system is provided that provides feedback based on accelerometer data. A CPR device uses an accelerometer to collect data in connection with performed CPR techniques. The CPR device includes a nonlinear spring. The accelerometer data is communicated to a computing system that generates one or more feedback metrics that may be displayed to users.

In accordance with various embodiments of the present disclosure, a method for outputting data from an inertial measurement unit having a first accelerometer having a first operating range and a second accelerometer having a second operating range comprises comparing an absolute value of data from the first accelerometer and / or from the second accelerometer for each of three axes to a first threshold and a second threshold; if the absolute value of the data from the first accelerometer and / or from the second accelerometer is less than the first threshold for all of the three axes, writing the data from the first accelerometer to a data buffer; and if the absolute value of the data from the first accelerometer and / or from the second accelerometer is greater than the second threshold for any of the three axes, writing the data from the second accelerometer to the data buffer.

An implantable medical device and computer implemented methods comprise a sensing circuit configured to sense cardiac activity (CA) signals. An accelerometer is configured to be implanted in a patient and obtain accelerometer data along at least one axis. A memory is configured to store program instructions and device parameters associated with each ventricular arrhythmia (VA) therapy in a collection of VA therapies with different levels of intensity. One or more processors execute the program instructions and are configured to analyze the CA signals over one or more cardiac beats, determine a VA episode based on the analysis of the CA signals, determine a posture of the patient based on the accelerometer data in response to the determination of the VA episode, and select a first VA therapy from the collection of VA therapies based on the posture.

The invention relates to the field of strapdown inertial navigation, in particular to a strapdown inertial navigation three-loop integrated navigation method and a computer device. The precision of the INS system is improved, and the calculation burden of the system is reduced. According to the technical scheme, the method comprises the following steps of: acquiring gyroscope and accelerometer data from an IMU (Inertial Measurement Unit), calculating a rotation matrix through a scheme of rotating around an X axis, converting the original IMU data to a carrier coordinate system, performing angular rate integration and specific force integration in a high-speed loop with a sampling interval, simultaneously acquiring a cone error and a paddling error from the IMU data, and performing integration, thereby obtaining an integral result of the cone error and the paddling error; in a medium-speed loop with a sampling interval, a cone and paddle error integral are used for compensating PVA updating, an updating result enters a low-speed loop, and in a low-speed loop with a sampling interval, a Kalman filter is used for processing and fusing data so as to provide more accurate position, speed and attitude estimation. The method is suitable for a high-precision inertial navigation system.