141 results about "Accelography" patented technology

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention is a system for continuous monitoring of respiratory and gastric functions using wearable devices. Each device includes a three-axis accelerometer and optionally a gyroscopic sensor. They feature wireless communication for data transmission and means for attachment to the skin. A computation device analyzes the data, using a machine learning model to detect coughing, swallowing and respiration. The method involves placing sensors on the skin of the cricoid region and epigastric region, filtering and transmitting data, training the model, and plotting coughing frequency. This system enhances detection accuracy and facilitates early medical interventions, benefiting chronic condition management and postoperative care.

The invention discloses a non-invasive wearable vascular arteriosclerosis assessment system and method based on multi-mode pulse wave signal fusion, and belongs to the field of vascular function assessment. The system comprises a distributed acquisition front end, a butterfly-shaped flexible substrate, an integrated double-accelerometer, a four-wavelength LED (530nm / 660nm superficial blood vessel measurement), a 810nm / 940nm deep artery measurement device, a double-photodiode, a double-impedance electrode and a pressure sensor. The central integrated control end processes a multi-mode signal and realizes pressure-LED current closed-loop control; the upper computer terminal extracts the multi-modal pulse wave time difference of the limbs and the neck, the local and regional pulse wave conduction velocity and the blood oxygen hemoglobin change parameters induced by the standardized posture, the dimension is reduced to 30 through principal component analysis, then the parameters are input into the random forest regression model, and the vascular elasticity index is output. Signal stability is improved through flexible fitting, single-source limitation is solved through multi-modal fusion, signal quality is guaranteed through closed-loop control, sensitivity is enhanced through dynamic physiological excitation, and accuracy and universality of arteriosclerosis assessment are improved.

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 invention discloses a gastrointestinal decompression nursing device and a gastrointestinal tract pressure supervision system, and belongs to the technical field of medical nursing. A gastrointestinal decompression nursing device comprises a suction tank, a regulation and control box, a stomach tube and a flexible arm band, and the regulation and control box comprises a data preprocessing module and a control module; a plurality of flexible resistance type sensors are distributed in the flexible arm band at equal intervals, an in-vivo capsule is arranged at the front end of the stomach tube, and a pressure sensor and a three-axis accelerometer are arranged in the in-vivo capsule. According to the gastrointestinal nursing pressure reducer, the problem that an existing gastrointestinal nursing pressure reducer cannot achieve pressure nursing and supervision work according to different patients is solved, the in-vivo capsule is integrated with the pressure sensor, the triaxial accelerometer and the impedance sensor, and the pressure in an intestinal tract cavity, the peristalsis frequency and the content impedance can be synchronously collected; in combination with body surface pressure gradient data of the flexible arm band, improper treatment caused by misjudgment is avoided, and serious complications such as intestinal perforation and bleeding caused by delayed treatment are avoided.

The invention relates to the technical field of disease monitoring, in particular to a multi-device collaborative disease risk prediction system. The system comprises a non-contact type signal receiving and transmitting device, a gait collecting device, an intelligent bracelet and a central processing unit, the non-contact type signal receiving and transmitting device captures macroscopic movement data of a user through a millimeter wave signal transmitting and receiving module, and the macroscopic movement data comprises periodic characteristics of gaits, movement tracks of four limbs and posture changes; the gait acquisition device acquires plantar pressure distribution data of a user through a pressure sensor array; the smart bracelet collects motion data of a user through an accelerometer and a gyroscope; and the central processing unit carries out space-time alignment on data of the non-contact signal transceiving device, the gait acquisition device and the intelligent bracelet through a timestamp synchronization algorithm, so that the disease risk is predicted according to the analysis data. The system supports all-weather and multi-scene monitoring, is suitable for disease risk assessment, and can early warn cardiovascular events and other health problems.

The invention relates to a safety monitoring method for rehabilitation training of an elderly weak patient. The safety monitoring method is used for automatically optimizing the rehabilitation training process of the elderly weak patient. According to the method, physical strength level, disease history and medicine use condition information of a patient are collected, physiological data of a rehabilitation person are monitored in real time in combination with various sensors, heart rate, blood oxygen, body temperature, myoelectric activity, fall detection, an accelerometer and a gyroscope, and a corresponding training scheme or adjustment suggestion is generated; the system outputs the physical health state of the rehabilitation person through the attention neural network; the body health state and the real-time data of the sensor serve as rule engine algorithm input, and then decision suggestions in rehabilitation training are made in real time; the method can reduce the health risk in training, and is especially suitable for personalized rehabilitation training of weak elderly patients.

The present application relates to the technical field of health monitoring, in particular to a kind of infant asphyxia risk autonomous monitoring method and system based on laryngeal respiratory sound, comprising: utilize high sensitivity MEMS microphone to collect the airflow and respiratory sound signal of infant larynx, and utilize micro accelerometer to monitor the thoracic movement data of infant synchronously;The airflow and respiratory sound signal are denoising, and the denoising respiratory sound signal is obtained;The denoising respiratory sound signal is separated according to respiratory event Signal, and is enhanced and combined into respiratory sound data set;Respiratory sound feature is extracted in respiratory sound data set, and asphyxia anomaly classification model is constructed based on respiratory sound feature by machine learning model, and dynamic baseline model that adapts individual respiratory difference is established according to respiratory sound feature.The present application utilizes the hardware device of high sensitivity MEMS microphone and micro accelerometer and artificial intelligence model to combine, realizes the use of laryngeal respiratory sound signal in home environment and autonomously completes infant asphyxia risk identification and early warning.

To make it possible to identify causes that may lead to damage to a medical device by monitoring corresponding usage conditions, and to record appropriate data for predictive diagnosis.SOLUTION: A monitoring system (20) comprises: an accelerometer (22) adapted to detect acceleration of a medical device (10) during handling of the medical device (10); a sensor unit (24) adapted to detect a further condition of the medical device (10) during handling of the medical device (10); and a control unit (26) with a dedicated storage unit (26a), wherein the control unit (26) is operatively connected to the accelerometer (22) and the sensor unit (24) and adapted to derive use data of the medical device (10) from the output data of the accelerometer (22) and the sensor unit (24) and to differentiate based on the respective output data of the accelerometer (22) and the sensor unit (24) between a regular use state and an unintended use state of the medical device (10).SELECTED DRAWING: Figure 1

The invention discloses a uterine contraction detection method based on multi-modal signal fusion reconstruction, which comprises the following steps of: 1, synchronously acquiring an abdominal surface uterine electromyographic signal in real time through a high-density electrode array, acquiring a uterine contraction pressure signal in cooperation with a fundus pressure sensor, synchronizing a triaxial accelerometer signal to acquire a maternal motion signal, and preprocessing; 2, performing time sequence alignment and resampling on the data, and constructing a quality mask to form a multi-mode uterine contraction signal data set; 3, constructing a reconstruction network based on multi-modal feature fusion; 4, constructing a total loss function of the multi-modal feature fusion reconstruction network; and 5, training a reconstruction network of multi-modal feature fusion, wherein the reconstruction network is used for fusion reconstruction of the multi-modal uterine contraction data. According to the invention, through multi-modal signal fusion and deep feature correlation analysis, accurate monitoring and prediction of uterine contraction and premature delivery risks in the pregnant and birth period can be realized.

Neurostimulation therapy can be efficiently controlled based on information from an acceleration signal, such as can be obtained from an accelerometer. In an example, the accelerometer can be implanted in a cervical region or submandibular region of a patient. Circuitry can be configured to identify a first series of respiration phase transition events in the acceleration signal and, in response, provide the neurostimulation therapy synchronously with an inspiration phase of a patient's respirator cycle. In an example, m absence of identifying the first series of respiration phase transition events in the acceleration signal, the neurostimulation therapy can be provided asynchronously with the patient's respiratory cycle.

A mount (3) comprising a clamp (11) and a suction pad (13) for attaching a reconstitution device (1) containing the constituent parts of a reconstitution drug to a touch screen (15) of a smart phone (5). A control program (27) provided in a memory (21) of the smart phone (5) causes a processor (17) to compare motion of the smart phone (5) as detected by an accelerometer (19) to a required motion for mixing the constituent parts of a reconstitution drug contained a reconstitution device (1) and provide a user with feedback to a user indicating when the smart phone (5) to which a reconstitution device (1) containing the constituent parts of a reconstitution drug is attached by the mount (3) has been sufficiently shaken to reconstitute the drug contained within the reconstitution device (1).

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.

A patient support apparatus includes a support surface, a control panel, a network transceiver, and a controller. The controller is adapted to send one or more messages to a server that indicates at least one of the following: an incorrect passcode was entered into the control panel; a correct passcode was entered into the control panel; a post-passcode-entry window of time expired; a microphone detected a sound level exceeding a threshold; the microphone detected a keyword; a block control was activated on the patient support apparatus; an agitation level of the patient; a patient activation of a nurse call control; a status of a patient restraint; a status of a restraint attachment cover; or movement of the patient support apparatus as detected by an accelerometer. A software application executed by a server may be configured to instruct a display to display an indicator relating to any of these messages.

An apparatus includes a photoplethysmography (PPG) sensor configured to measure a PPG signal associated with a user, an accelerometer configured to measure an acceleration signal associated with the user, and a processor operatively coupled to the PPG sensor and the accelerometer. The processor is configured to determine whether the user is engaging in movement based on the acceleration signal, in response to determining that the user is engaging in movement, determine a type of movement of the user based on the acceleration signal, generate a probability distribution associated with a pulse rate of the user based on the type of movement, and determine the pulse rate of the user based on the PPG signal, the acceleration signal, and the probability distribution.

A circulatory support system includes an implantable blood pump including a housing, a rotor operable to pump blood from an inlet to an outlet, a stator, and at least two of the following: a current sensor, a rotor position sensor, an accelerometer, and a pressure sensor. The controller is connected to the sensors and includes a signal-processing module configured to receive, from each of the sensors, a data stream. The signal-processing module is also configured to filter the data streams received from the plurality of sensors, determine at least one of a pump operating parameter, a cardiac characteristic, and a pump control parameter based on at least two of the filtered data streams, and output the at least one of the pump operating parameter, the cardiac characteristic, and the pump control parameter to at least one of the operator interface module and the pump control module.

The invention particularly relates to an internal arteriovenous fistula power loss early warning system based on fusion of miniature ultrasound and biological impedance, and relates to the technical field of medical instruments. A data processing module; and an intelligent early warning module. According to the invention, through the integrated design of the miniature ultrasonic probe module and the biological impedance electrode module, the synchronous measurement of the blood flow velocity and the blood vessel volume change is realized; wherein the PMUT array is combined with a three-axis accelerometer to correct the angle of a probe in real time, angle errors caused by operation are avoided, meanwhile, the biological impedance electrode measures the blood vessel volume through alternating current, an impedance diameter model is dynamically updated through AI, the cross sectional area of the blood vessel is calculated, and traditional static calibration is replaced; the problem of insufficient precision of a single mode is solved, more reliable hemodynamic data is provided for clinic, and the abnormal state of the blood vessel is recognized in an early stage.

Implantable medical devices include a first sensor for detecting a magnetic field that indicates an exposure mode of operation is appropriate. The implantable medical devices include a second sensor for detecting whether an MRI characteristic is present that indicates whether MRI or non-MRI post exposure diagnostics and other actions should be implemented and may also indicate whether the exposure mode should be MRI or non-MRI specific. An MRI post exposure diagnostic may perform pacing capture threshold tests and the post exposure pacing amplitude output may be kept at a higher than normal level. The second sensor may be an overvoltage clamp circuit of a telemetry coil that indicates whether an overvoltage condition on the telemetry coil is occurring to indicate whether an MRI characteristic is present. The second sensor may be a second threshold magnetic sensor, an accelerometer, or a microphone to indicate whether an MRI characteristic is present.

An impaction device that may include a force transducer and optional accelerometer to measure an impact force between an impactor device and broach / implant or broach / implant handle. The impaction device may include a microcontroller electrically coupled to memory and the force transducer and optional accelerometer, wherein the microcontroller is configured to execute operations stored in the memory, the operations comprising: receiving resistive force data and acceleration data to calculate resistance for each impaction and generate a resistance curve for output to a display.

This invention provides a motion monitoring method, system, and wearable device. It collects acceleration and angular velocity data during a patient's movement using an inertial measurement unit (IMU). The collected data is filtered and subjected to variational mode processing to extract gait information. Utilizing the waveform stability characteristics of the accelerometer and gyroscope at rest, the zero-velocity angular velocity point is identified. Based on the zero-velocity angular velocity point in the gyroscope data, the zero-velocity acceleration segment is also identified. Subsequently, the gyroscope's attitude is corrected using the acceleration data. The corrected gyroscope data generates a rotation matrix to transform the IMU coordinate system to the navigation coordinate system, and finally, trajectory reconstruction is performed. During trajectory reconstruction, data such as gait variability, step count, step frequency, stride length, movement distance, and movement trajectory are displayed on the wearable device or transmitted to a remote device for reference by doctors and patients.

This utility model discloses an exoskeleton robot control system that facilitates control, improves control effectiveness, and simplifies control methods. The exoskeleton robot control system includes an exoskeleton robot frame, an online acquisition system, a data processor, and an exoskeleton robot controller. The online acquisition system includes a smart bracelet, multiple patch-type IMU signal acquisition devices, and multiple patch-type electromyography (EMG) sensors. The smart bracelet is capable of detecting heart rate and blood pressure and can connect via WiFi or Bluetooth. The patch-type IMU signal acquisition devices integrate accelerometers and gyroscopes. The online acquisition system and the exoskeleton robot controller are electrically connected to the data processor. This exoskeleton robot control system improves user comfort and is suitable for controlling medical rehabilitation assistive exoskeleton robots, thus improving rehabilitation outcomes.

A laryngeal pacemaker is configured for external placement on skin of a patient to produce respiration stimulation signals. An implantable stimulation electrode delivers the respiration stimulation signals to adjacent target neural tissue for vocal fold abduction during respiration of the recipient patient. A triaxial accelerometer produces a body motion signal reflecting energy expenditure of the recipient patient. A respiration sensor includes a flexible skin-transferrable printed tattoo electrode having a tetrapolar configuration for impedance pneumography measurement to produce a sensed respiration signal for the laryngeal pacemaker. The respiration sensor is configured for transfer and release by guided placement from a sensor applicator to the skin at the angulus sterni of the recipient patient. And the laryngeal pacemaker is configured to interpret the body motion signal and the sensed respiration signal to make a real time determination of respiratory phase and frequency for adaptively adjusting the respiration stimulation signals accordingly.

A wireless wearable sensor device, system, method, and non-transitory computer readable medium for screening for structural heart disease based on electrocardiogram, phonocardiogram, and / or accelerometer signals on a patient's skin surface.

The invention provides an intelligent wearable health monitoring system, and relates to the technical field of sports biomechanics, and the method comprises the steps: collecting original sensing data streams of a triaxial accelerometer and a gyroscope in real time, and converting the original sensing data streams to a human skeleton coordinate system through a rotation matrix; calculating the Euler angle of the limb joint based on the converted data, and extracting gait phase parameters in combination with the curvature features of the motion trail; calculating a real-time stride frequency according to the gait phase parameter; when a plurality of continuous stride frequency sampling values exceed a preset medium-high intensity motion threshold value, generating a medium-high intensity motion state identifier; synchronously executing according to the medium-high intensity motion state identification, constructing a blood glucose dynamic evaluation window based on a continuous sampling sequence of a blood glucose sensor, calculating a time sequence gradient of blood glucose reading in the window, and generating a dynamic compensation coefficient according to the time sequence gradient. According to the invention, dynamic and accurate health monitoring and risk early warning in a motion scene are realized.

A system and method for managing medication titration using real-time walking and balance data collected from mobile electronic devices are disclosed. Smartphone sensors, including accelerometers and gyroscopes, capture mobility metrics such as gait speed, stride characteristics, postural stability, and fall risk. The mobility data is integrated with additional physiological metrics and a patient's electronic medical records. Analytical algorithms, including machine-learning models, evaluate changes in mobility to identify correlations between medication usage and functional performance. Based on detected changes, the system generates alerts and recommendations to assist healthcare providers in adjusting medication dosages. The invention supports proactive and personalized medication management, particularly for medications that affect balance or fall risk, and improves patient safety by enabling data-driven clinical decision-making using real-world mobility insights.

A computer-implemented method is provided to estimate a periodic feature, such as a period length or a period location, of a physiological rhythm, such as a cardiac rhythm or an autonomic respiratory rhythm. The computer-implemented method comprises receiving a signal representing the physiological rhythm, estimating a first set of periodic feature estimates based on the signal, generating a prior probability of the periodic feature based on at least a subset of the first set of periodic feature estimates, and estimating a second set of periodic feature estimates based on the first set and the prior probability. By using such a computer-implemented method, the accuracy of the signal is improved, in particular when the signal is obtained from an accelerometer arranged on the chest of a subject.

A medical device connector module, including a housing, a cable extending from the housing, and an optical fiber. The housing includes a receptacle configured to receive a first plug to establish an optical connection between a medical device and the medical device connector module. The housing further includes a plurality of sensors configured to provide sensor data for determining a reference plane for shape sensing. The plurality of sensors include a gyroscope and an accelerometer. The cable includes a second plug. The optical fiber extends from the receptacle through the cable to the second plug. The optical fiber is configured to convey input optical signals from the optical interrogator to the receptacle and reflected optical signals from the receptacle to the optical interrogator.

A system for monitoring a physiologic condition of a patient includes an implantable medical device (IMD) comprising: an accelerometer configured to output an accelerometer signal; sensing circuitry configured to sense a cardiac activity (CA) signal; and a memory configured to store program instructions. One or more processors that, when executing the program instructions, are configured to: determine a heart rate (HR) at a point in time based on the CA signal; determine an activity level (AL) at the point in time based on accelerometer data that is based on the accelerometer signal, the HR and AL forming an HR-AL event; compare the HR-AL event to a heart condition (HC) metric that defines combinations of HRs and ALs representing a physiologically normal heart condition and a physiologically abnormal heart condition; and transmit the HR-AL event or a result of the comparison to a second device for use in diagnosing and / or treating a heart condition.

An example medical device system includes a medical device including an accelerometer, one or more memories, and processing circuitry. The processing circuitry is configured to detect, based on the one or more accelerometer signals, an impact event occurring at a point in time. The processing circuitry is configured to process the one or more accelerometer signals to determine a plurality of first characteristics corresponding to a first period of time before the impact event. The processing circuitry is configured to process the one or more accelerometer signals to determine a plurality of second characteristics corresponding to a second period of time following the impact event. The processing circuitry is configured to determine a severity of the impact event based on the first characteristics and the second characteristics and to determine whether to output an alert based on the severity of the impact event.