82 results about "Accelography" patented technology

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 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 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.

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.

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.

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 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 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.

An implantable medical device that includes an inflatable member, a fluid reservoir, a first pressure sensor connected to the fluid reservoir, a second pressure sensor connected to the inflatable member, an accelerometer configured to detect an acceleration of the implantable medical device along three orthogonal directions, and an electronic pump device. The electronic pump device includes a controller that is configured to execute operations that include receiving a first signal with pressure readings from the first pressure sensor and computing an ambient pressure value based on the pressure readings. The operations further include detecting an inflation pressure of the inflatable member using the second pressure sensor, receiving a second signal with acceleration data from the accelerometer, computing a gauge pressure based on the ambient pressure, the acceleration data, and the inflation pressure, and controlling inflation or deflation of the inflatable member based on the gauge pressure.

An exercising device with fistula maturation management and assessment abilities and a method of fistula maturation management and assessment is described. The device is a tool for remote monitoring of compliance of exercise routines prescribed to Chronic Kidney Disease (CKD) patients for proximal fistula maturation. The device measures and syncs exercise data captured via a motion-sensing sensors (eg: a MEMS / NEMS micro-electro-mechanical systems or nano-electro-mechanical systems or other techniques - based accelerometer, gyroscope, magnetometer, inertial measurement unit etc.) and via computational device (eg: mobile phone, tablet, or an onboard device) to a cloud platform (eg: cloud platform, such as Amazon web service [AWS], Google cloud etc.) from which the Nephrologist can retrieve patient data and check for compliance.

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.

A health monitoring system for the autonomous support of elderly people, the system comprising the following: a variety of Internet of Medical Things (IoMT) sensors, including at least one wearable physiological sensor, one or more environmental sensor units, a mobility tracking device and a medication intake module, wherein the IoMT sensors are configured to continuously collect physiological, environmental, behavioral and medication use data; an edge processing device that includes an embedded processor and a lightweight machine learning inference unit configured to perform real-time anomaly detection and generate local alerts in the absence of cloud connectivity; a cloud-based analytics engine comprising one or more deep learning models configured to perform longitudinal health modeling, population-level clustering, and predictive risk forecasting using historical and real-time data; and a multi-channel communication unit configured to securely transmit notifications, reports, and emergency triggers to nursing staff, clinical staff, family members, and emergency services, wherein the edge processing device includes a dedicated hardware accelerator selected from a field-programmable gate array (FPGA), the accelerator being optimized to perform low-latency computations, including fall detection, arrhythmia detection, and respiratory abnormality monitoring, within a processing window of less than 200 milliseconds;and wherein the wearable physiological sensor is designed as a wrist-worn or patch-based device comprising a set of sensor elements, including an electrocardiography (ECG) electrode, a photoplethysmography (PPG) sensor for SpO2 measurement, a three-axis accelerometer, a gyroscope, and a skin temperature probe, the device further being configured to integrate raw signal data streams through a multimodal fusion technique performed locally on the edge device to minimize noise, compensate for motion artifacts, and improve the specificity of anomaly detection across multiple concurrent parameters.

Described herein are apparatuses and methods for classifying a patient as being asleep or awake. Such an apparatus can include an accelerometer and a processor. The accelerometer, alone or in combination with the processor, is used to determine an activity level of the patient and a posture of the patient. The processor is configured to classify the patient as being asleep in response to both (i) the posture of the patient being recumbent or reclined for at least a sleep latency duration, and (ii) the activity level of the patient not exceeding an activity threshold for at least the sleep latency duration; and classify the patient as being awake in response to at least one of (iii) the posture of the patient being upright for at least an awake latency duration, or (iv) the activity level of the patient exceeding the activity threshold for at least the awake latency duration.

The invention particularly relates to a one-stop service method and system for intelligent hospital logistics management, and relates to the technical field of intelligent hospital logistics management. And asset characteristic data including motion state characteristics, space attitude characteristics and base in-place state characteristics are obtained. According to the invention, the three-axis accelerometer, the magnetic induction sensor and the positioning unit are integrated in the end-side intelligent tag, and a mixed acquisition strategy combining event-driven triggering and periodic heartbeat reporting is adopted, so that the multi-dimensional synchronous sensing of the medical asset position, the motion state, the spatial attitude and the base in-place state is realized, and the sensing dimension is expanded; and meanwhile, the sensor component enters the low-power-consumption sleep mode in the time period when no event is triggered and the heartbeat reporting moment is not reached, so that the continuous power consumption of the terminal is reduced.

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 abnormal electrocardiosignal marking method comprises the steps that electrocardiosignals are continuously collected through wearable equipment, and whether a trigger signal is currently obtained or not is detected and judged in real time; the method comprises the following steps: when a trigger signal is obtained, recording a timestamp when the current trigger signal occurs, correspondingly marking an electrocardiosignal position under the current timestamp, intercepting electrocardiosignals in a certain period of time before and after taking the electrocardiosignal position under the current timestamp as a midpoint, and marking the electrocardiosignals as abnormal electrocardiosignals, an accelerometer in the wearable device detects and recognizes in real time, marked abnormal electrocardiosignals are uploaded to a server side for a doctor to download and check, and important reference significance is provided for the doctor to diagnose the state of the patient in the daily life at home and in the postoperative rehabilitation stage.

A first device comprises an accelerometer and is configured to deliver cardiac pacing to a patient, and sense physiological activity of the patient sensed via the accelerometer. A second device is configured to sense the physiological activity of the patient via a plurality of electrodes. Processing circuitry is configured to compare the sensing of the physiological activity by the first device to the sensing of the physiological activity by the second device and based on the comparison, control whether the first device delivers the cardiac pacing based on the sensing of the physiological activity via the accelerometer.

The present invention discloses a system and method for identifying phases of breathing activity and managing exercises. The system includes a wearable device configured to capture physiological data of user comprising acoustic sound signals associated with inhalation, exhalation, and breath holding, and inertial measurement unit (IMU) data including accelerometer, gyroscope, and magnetometer data. A database stores user data and trained models, including probabilistic breath phase, breath phase transition, posture, and stability models. A computing device, in network communication with the wearable device and the database, executes program modules to receive the physiological data, preprocess the sound and motion data from physiological data, and extract features therefrom. The sound data are processed into Mel-frequency cepstral coefficient features, and the motion data are processed into slope-based features. Using these features, the system predicts breath phases in real time, segments individual breath cycles, and determines transitions between inhalation, holding, and exhalation

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 technology for determining an indication of heart failure of a subject (18) is proposed. It comprises: obtaining (100a) a first signal interval (36) from a source signal recorded with an accelerometer (14) placed on the chest of a subject (18), wherein the first signal interval (36) corresponds to a first subinterval of a heart cycle: inputting (200a) the first signal interval (36) into a first autoencoder, wherein the first autoencoder is trained on the corresponding first signal intervals obtained from healthy subjects and outputs a reconstructed first signal interval (44), determining (300a) a first correlation between the first signal interval (36) and the reconstructed first signal interval (44), and determining (400) the indication of heart failure based on the first correlation.

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.

The application relates to the technical field of healthcare informatics, in particular to a seismocardiogram analysis system and method based on accelerometer data, wherein the system comprises a heart vibration detection unit, a motion artifact detection unit and a data processing unit. The heart vibration detection unit is arranged to collect a sternum vibration signal generated by heart activity. The motion artifact detection unit is arranged to detect a sternum posture signal generated by thoracic activity during breathing. In the application, the data processing unit acquires signals of the heart vibration detection unit and the motion artifact detection unit, encodes the signals into vector group data, performs digital filtering on the vector group data, preliminarily performs band-pass filtering to retain only low-frequency signals, and performs modal decomposition on the sternum vibration signal based on a VMD algorithm to decompose the SCG signal in the sternum posture signal based on parameter characteristics of the sternum posture signal as a constraint condition.

A medical device includes one or more accelerometers; a sensor configured to monitor a physiological parameter of a patient; a memory; and processing circuitry configured to receive, from the one or more accelerometers, an accelerometer signal indicative of an amount of movement of the patient; determine, based on the accelerometer signal, that the patient is in an active period; determine, based on the accelerometer signal, that the active period has ended; and cause the sensor to transition from a low-power state to a high-power state in response to determining that the active period has ended.