227 results about "Accelerometer data" patented technology

The invention relates to the field of computer medical engineering, and discloses a blood pressure continuous measurement abnormity early warning method and system based on AI data analysis, and the method comprises the steps: collecting a pulse wave signal, triaxial accelerometer data, an ambient light intensity signal and a skin conductance signal through a sensor on a preset wearable device; generating multi-scale physiological features; systolic pressure and diastolic pressure are calculated, and blood vessel stiffness is calculated; calculating the main arteriosclerosis risk level; when the main arteriosclerosis risk level is higher than a preset risk level, a user is prompted through the wearable device. According to the invention, the pulse wave, the triaxial accelerometer, the ambient light intensity and the skin conductance signal are synchronously acquired and analyzed to obtain the systolic pressure, the diastolic pressure dynamic trend and the blood vessel stiffness, and the main arteriosclerosis risk level can be accurately quantified. In addition, a'physiological parameter-angiosclerosis-risk decision 'closed-loop management mechanism is established, and automatic and active health management of the user is realized.

The invention relates to the technical field of VR remote control, and discloses a VR remote control method and system based on gyroscope data fusion, and the method comprises the steps: obtaining current environment data, gyroscope three-axis data and accelerometer data; inputting the current environment data into a pre-trained error estimation model, and outputting an error estimation value and a prediction confidence coefficient; performing confidence coefficient judgment according to the error estimation value and the prediction confidence coefficient to obtain a zero offset estimation result; performing attitude analysis according to the gyroscope three-axis data and the accelerometer data to obtain a preliminary attitude estimation result; performing dynamic compensation according to the zero offset estimation result and the gyroscope three-axis data to obtain corrected gyroscope data; and performing fusion correction according to the initial attitude estimation result and the corrected gyroscope data, and outputting a final attitude estimation result. The method has the advantages that the accuracy of attitude estimation in VR remote control can be improved.

A system for closed-loop control of sub-perception spinal cord stimulation therapy is provided. The system includes an implantable pulse generator (IPG) with an evoked compound action potential (ECAP) recording module and accelerometer. A regression model maps accelerometer data to optimal stimulation parameters for consistent nerve activation. This enables closed-loop therapy in the sub-perception domain where ECAP features are not visually detectable. A clinician programmer applies the model and sub-perception dose to therapy programs deployed to the IPG. The model can be customized to each patient by collecting ECAP and accelerometer data. The mapping reconciles ECAP -based control between perception and sub-perception domains by supplementing unavailable ECAP data with accelerometer data. This improves stimulation therapies for patients receiving sub-perception and multi-sensor paresthesia treatments.

A system for closed-loop control of sub-perception spinal cord stimulation therapy is provided. The system includes an implantable pulse generator (IPG) with an evoked compound action potential (ECAP) recording module and accelerometer. A regression model maps accelerometer data to optimal stimulation parameters for consistent nerve activation. This enables closed-loop therapy in the sub-perception domain where ECAP features are not visually detectable. A clinician programmer applies the model and sub-perception dose to therapy programs deployed to the IPG. The model can be customized to each patient by collecting ECAP and accelerometer data. The mapping reconciles ECAP-based control between perception and sub-perception domains by supplementing unavailable ECAP data with accelerometer data. This improves stimulation therapies for patients receiving sub-perception and multi-sensor paresthesia treatments.

The invention relates to a data acquisition and analysis system and method for laser interference measurement, and aims to solve the problem of limited measurement precision caused by asynchronous signal acquisition, imperfect environmental interference compensation and algorithm errors of the existing system. The system comprises a photoelectric conversion unit, a signal conditioning unit, a high-speed synchronous acquisition unit, a real-time processing unit and an error compensation module. The photoelectric conversion unit adopts a four-quadrant PIN photodiode array to receive interference light signals and convert the interference light signals into analog electric signals; the signal conditioning unit performs gain adjustment and bandwidth limitation on the signal; the high-speed synchronous acquisition unit realizes femtosecond-level synchronous acquisition of signals; the real-time processing unit performs phase solution and eliminates harmonic errors based on an improved arc tangent algorithm; and the error compensation module fuses temperature sensor data and accelerometer data to realize thermal drift and vibration compensation.

A system (1) for efficiently determining a driving behaviour of a driver driving a vehicle. The system includes a 3-axis accelerometer (2) generating accelerometer data (3), a direction sensor (4) generating direction data (5) and orientation sensor (6) generating orientation and angular velocity data (7), all placed inside the vehicle. The system (1) also includes a processing unit (8) which processes the accelerometer data (3), the direction data (5) and the orientation data (7), and calibrates the accelerometer data (3) to generate a calibrated accelerometer data (9). The calibrated accelerometer data (9) is used in determining the driving behaviour of the driver of the vehicle and generate Driving Deficiency Alerts, including but not limited to Over-speeding. Hard acceleration. Sudden Braking, Fast Cornering, Quick Lane Changing, Distracted Driving, Dangerous Driving over bumps and Crash Detection.

One or more location analysis computing devices, methods, and computer-readable media are disclosed herein for determining the position of a mobile device (smartphone, tablet computer) within an interior of a vehicle. The position of the mobile device may be calculated by detecting changes in accelerometer data. The accelerometer data may first need to be translated to determine corresponding axes, since the device may not be right side up (e.g., in a pocket). The vehicle may travel over road discontinuities such as bumps, and calculating the position of the mobile device may be based on the different magnitude and angle resulting from a first tire and a second tire hitting the bump. Data from vehicle sensors or other mobile device sensors may also be used in the calculating. Once the position is determined, commands may be sent to the mobile device to deactivate certain functionality, or to a remote server for further processing.

The invention belongs to the technical field of inertial navigation, and particularly relates to a detection method for judging a fault of an inertial measurement unit, which comprises the following steps: acquiring fiber-optic gyroscope data and accelerometer data of the inertial measurement unit; analyzing and judging the data of each axis fiber-optic gyroscope, counting a numerical value of which the output change of adjacent sampling intervals is 0, and if a preset first condition is met, judging that the axis fiber-optic gyroscope has a fault; if the X-axis fiber-optic gyroscope, the Y-axis fiber-optic gyroscope and the Z-axis fiber-optic gyroscope all have faults, judging that the gyroscope main circuit board has Analyzing and judging the data of each axis accelerometer, counting the duration when the output value of each axis accelerometer is 0, and if the preset duration is reached, judging that the axis accelerometer has a fault; and if the X-axis accelerometer, the Y-axis accelerometer and the Z-axis accelerometer all fail, the I / F conversion module is judged to fail. According to the method, accurate identification and rapid positioning of various potential and complex fault modes can be realized through deep analysis and comprehensive diagnosis of a single IMU component under the condition of not depending on an additional inertial navigation inertial device.

The invention discloses a sleep staging detection method and system based on a smart watch, and the method comprises the steps: S1, obtaining the heart rate variability, triaxial accelerometer data and body movement intensity index signals of a wearer through a sensor of the smart watch, and carrying out the multi-source signal fusion and time synchronization; s2, carrying out noise reduction processing on the synchronized multi-source signal, and constructing a feature vector; s3, calculating a time sequence self-correlation feature of the feature vector; s4, based on a bidirectional long and short time memory network structure, performing time sequence modeling on the input feature vector and the time sequence self-correlation feature to obtain fused bidirectional feature representation; and S5, performing multi-classification processing on the fused bidirectional feature representation based on a Softmax classifier to realize accurate classification of sleep stages. According to the method, the heart rate variability signal and the three-axis acceleration signal can be fused, and the sleep state of the human body can be accurately recognized and classified in combination with the bidirectional long-short-term memory network and time sequence self-correlation feature analysis.

A measurement device (MD) for measuring depth within a borehole comprising a sensing component including, at least, a plurality of accelerometers configured to generate accelerometer data as the MD moves through the borehole. The MD further comprises a controller component configured to correct the accelerometer data generated by the sensing component by: repeatedly, for each of a plurality of movement periods in which the MD moves within the borehole: altering the accelerometer data generated during the movement period to reduce errors associated with the movement of the MD, based on the accelerometer data generated in at least one of a first stationary period of the MD and a second stationary period of the MD, the first and second stationary periods occurring respectively before and after the movement period, wherein each of the plurality of movement periods corresponds to extraction of a portion of the drill string from the borehole.

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.

Systems and methods for providing assessments of a disease using sensor data for a subject captured during day-to-day living are provided. The sensor data includes accelerometer data and is acquired by a sensor disposed on the subject's wrist while the subject performs both active and sedentary activities. The sensor data is analyzed for one or more bouts of activity; the sensor data is filtered, optionally rotated, and Fourier transformed. Spectral analysis parameters are then calculated from the transformed data to detect disease-relevant movement patterns. Movement distribution parameters for the accelerometer data associated with the disease-relevant movement patterns are calculated and compared to one or more disease scores to evaluate a progression of the disease or an efficacy of a treatment

The invention discloses a real-time multi-mode physiological signal analysis method for myocardial ischemia, and belongs to the technical field of data analys.The technical scheme includes that electrocardiogram, photoelectric volume pulse waves and thoracic impedance signals are synchronously acquired, and phase difference caused by motion displacement is compensated in real time by the aid of a triaxial accelerometer; constructing a multi-dimensional quality evaluation matrix containing an electrocardiogram signal-to-noise ratio, a photoelectric volume pulse wave perfusion index and a chest impedance variance, and jointly judging whether signal reconstruction is triggered or not based on a dynamic threshold value and accelerometer data; a multi-channel signal block which is judged to be low in quality through the quality evaluation matrix and comprises the electrocardiogram and photoelectric volume pulse waves and chest impedance synchronized with the electrocardiogram is input into a generative adversarial network with electrophysiological constraints to be reconstructed, and a reconstructed high-quality electrocardiogram signal is output; and myocardial ischemia key indexes are extracted from the reconstructed signals, and multi-level risk early warning is carried out. The real-time multi-modal physiological signal analysis method for myocardial ischemia has the beneficial effect that the real-time multi-modal physiological signal analysis method for myocardial ischemia is provided.

The present disclosure is directed to a device and method for human fall detection solution. Fall detection is performed by a low power inertial measurement unit (IMU) that is communicatively coupled between a pressure sensor and an application processor. The IMU includes one or more motions sensors, such as an accelerometer and gyroscope. The application processor is the main processor of the containing device. The IMU receives pressure sensor data from the pressure sensor, and executes the fall detection using both the pressure sensor data and accelerometer data.

The invention provides a multi-sensor fusion positioning method and related equipment. The method comprises the following steps: acquiring original data of a plurality of sensors; performing preprocessing and synchronous processing on the original data to obtain aligned sensor data; the sensor data comprises point cloud data, image data, accelerometer data, gyroscope data and satellite positioning data; similarity matching is carried out based on the point cloud data and loopback sampling points of a target area, and an initial pose is determined; performing branch and bound search based on the initial pose to determine a global pose; fusing the sensor factors corresponding to the sensor data to obtain a joint factor; and obtaining a positioning result based on the joint factor and the global pose.

Systems, methods, and devices are provided for determining a status of a container. Some example systems include a container configured to rotate such that material(s) that may be disposed within the container are able to exit the container due to gravitational force. The systems also include a device connected to the container, which includes an accelerometer configured to sense movement corresponding to the container discharging the material(s). Such systems may also include a processor and a memory, and the processor may be configured to receive accelerometer data from the accelerometer, determine a tilt angle of the container based on the accelerometer data, determine whether the tilt angle satisfies a predetermined threshold, and cause a user to be notified when the tilt angle satisfies the predetermined threshold. The predetermined threshold may be a value representative of an assumption that a certain amount of the material(s) has been removed from the container.

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.

Method for processing users' cellphone data, the method comprising using a hardware processor for analyzing at least accelerometer (IMU) data provided by cellphones of at least a threshold number of users in a given location including recognizing at least one urban feature at said given location each time at least the threshold number of users, known to be present at the given location, exhibit a given motion pattern known by the processor to be characteristic of said urban feature; and / or generating a map including a stored representation of said at least one urban feature which is associated in memory with said given location at which the accelerometer data indicative of the urban feature was collected.

Apparatus and methods for touch screen motion compensation are disclosed herein. In certain embodiments, a touch screen motion compensation system for a vehicle includes an accelerometer that generates accelerometer data indicating a motion of the vehicle, a touch screen display that generates touch location data in response to tactile input from a user, and a computing system that generates compensated touch location data by compensating the touch location data based on the accelerometer data.

An infusion pump includes a housing, a pumping mechanism, an accelerometer configured to detect at least one of pump motion and pump angular orientation, and a temperature control unit. The temperature control unit is configured to receive accelerometer data and determine a pump configuration based on the accelerometer data. The pump configuration may include a contact configuration or a non-contact configuration. The temperature control unit is also configured to establish a threshold housing temperature based on the determined pump configuration. Additionally, the temperature control unit is configured to modify at least one pump feature or function to maintain the housing below the threshold housing temperature.

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.

The invention relates to the technical field of geological monitoring, and particularly provides a geological disaster monitoring system and method of a dynamic reference station. In the monitoring method, when the reference station is abnormal and has no data, the processing is not simply abandoned; a possible stable station is screened as a new reference station by checking historical data of other monitoring stations and accelerometer data, so that the emergency situation that the reference station has no data is effectively handled, and the monitoring data is prevented from having holes; after the base station is switched, the monitoring result under the new base station can be converted into the coordinate system of the original base station, data continuity and comparability are guaranteed, the new base station can be adjusted according to the acceleration stability index in dynamic monitoring, and stability and accuracy of data processing are guaranteed. The monitoring system provided by the invention does not need to additionally add a plurality of standby reference stations, so that the construction cost and the operation and maintenance cost of the reference stations are reduced.

An exemplary virtual IMU extraction system and method are disclosed for human activity recognition (HAR) or classifier system that can estimate inertial measurement units (IMU) of a person in video data extracted from public repositories of video data having weakly labeled video content. The exemplary virtual IMU extraction system and method of the human activity recognition (HAR) or classifier system employ an automated processing pipeline (also referred to herein as “IMUTube”) that integrates computer vision and signal processing operations to convert video data of human activity into virtual streams of IMU data that represents accelerometer, gyroscope, or other inertial measurement unit estimation that can measure acceleration, inertia, motion, orientation, force, velocity, etc. at a different location on the body. In other embodiments, the automated processing pipeline can be used to generate high-quality virtual accelerometer data from a camera sensor.

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 invention relates to a reconfigurable smart surface (RIS). Provided is an RIS capable of fitting a non-planar surface, the RIS comprising: a plurality of RIS unit cells; a plurality of accelerometers, each configured to detect accelerometer data of at least one of the RIS unit cells; and a controller configured to determine an operating condition of the RIS based on the detected accelerometer data of the RIS unit cell, and adjust one or more operating parameters based on the operating condition. A method of operating such an RIS is also provided in which each RIS unit cell includes a dedicated accelerometer configured to detect accelerometer data of the RIS unit cell.

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.

The invention belongs to the technical field of positioning navigation, and discloses an initial course alignment method and device based on geometric constraint type filtering, and the method comprises the steps: calculating a magnetic course angle and estimating the GNSS driving distance and orientation according to received GNSS original observation data, earth magnetic field information, and accelerometer data and gyroscope data obtained based on an IMU array; based on an unscented Kalman filtering algorithm under a Gaussian progressive framework, correcting accelerometer data in combination with geometric constraints, and identifying an IMU with a fault in the IMU array for isolation to obtain filtered accelerometer data and gyroscope data; calculating to obtain a GNSS-IMU course angle by combining the filtered accelerometer data and gyroscope data and the GNSS driving distance and direction; and according to the loss duration of the GNSS original observation data, synthesizing the magnetic course angle and the GNSS-IMU course angle to obtain a final initial course angle. The initial alignment time of the integrated navigation system is effectively shortened, and the robustness of the system is enhanced.

Systems and methods for fusing multiple types of sensor data to determine a heart rate of a user. An accelerometer obtains accelerometer data associated with the user over a time period, and a gyroscope obtains gyroscope data associated with the user over the time period. Also, a camera obtains a plurality of images of the user's eye over the time period. The plurality images are analyzed to generate image data of the user's eyelid over the time period. The accelerometer data, the gyroscope data, and the image data are fused into fused sensor data, and a heart rate of the user is determined from the fused sensor data.