319results about "User/patient communication for diagnostics" patented technology

The invention provides a stroke patient gait walking analysis method and device. The stroke patient gait walking analysis method comprises the steps that pressure data and inertial data generated in the gait walking process of a patient are collected based on a plantar pressure sensing module and an inertial measurement module; performing data preprocessing on the pressure data and the inertial data; constructing a double-flow neural network to respectively extract pressure features and inertial features of the pressure data and the inertial data, performing feature fusion, and calculating step length, stride frequency, gait asymmetry index and gait stability index key indexes based on the fused features so as to realize gait evaluation; an evaluation result is transmitted to the flexible OLED touch screen, and gait parameters, rehabilitation progress, abnormal early warning and training guidance information are displayed in real time through the flexible OLED touch screen. According to the method, the end-to-end process from original sensor data to clinical application is realized, manual monitoring is avoided, the accuracy and clinical practicability of evaluation are improved, and the method is particularly suitable for family rehabilitation monitoring of stroke patients and precise rehabilitation evaluation of medical institutions.

The invention discloses a multi-modal data fusion and intelligent analysis high-precision sleep monitoring system and method. The system comprises a data acquisition layer, a transmission layer, an analysis layer and an application layer, the data acquisition layer transmits data acquired by a sensor to the transmission layer step by step through BLE or Wi-Fi, the transmission layer divides the data acquired by the sensor into two paths through a Wi-Fi module and transmits the data to a main control device and a cloud server in the analysis layer, and the main control device and the cloud server process the data. The data collection layer comprises a data collection module, the data collection module comprises a physiological data collection unit, an environment data collection unit and a subjective data collection unit, the data collection layer is used for collecting data of multiple sources such as physiological data, environment data and subjective data, and the data collection layer is used for collecting data of multiple sources such as the physiological data, the environment data and the subjective data. The deep learning and big data analysis technology is applied, high-precision recognition of the sleep state and precise evaluation of health risks are achieved, and a personalized sleep improvement scheme is provided.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and / or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.

The invention relates to the technical field of medical data analysis and rehabilitation, in particular to a cerebral apoplexy patient rehabilitation exercise analysis system based on medical big data, which comprises a data acquisition module used for acquiring electromyographic signals, exercise data and behavior data of a patient to form a data set; the data processing and fusion module is used for preprocessing the electromyographic signals in the data set, normalizing motion and behavior data and fusing the motion and behavior data into a multi-modal feature vector; the hierarchical analysis module is used for performing motion intention recognition according to the preprocessed electromyographic signals and outputting an intention classification result, recognizing the motion intention and predicting a rehabilitation effect through a double-flow Transform encoder, and evaluating a behavior risk level in combination with a risk factor formula; the optimization module is used for dynamically adjusting the mirror image treatment intensity according to the risk level and optimizing the prediction model by adopting incremental learning; and the interaction module is used for generating a thermodynamic diagram superposition rehabilitation curve in the X-Y coordinate system, and marking doctor intervention suggestions to complete visual interaction.

The invention discloses portable intelligent assessment and rehabilitation training integrated equipment for cognitive disorder patients, and belongs to the technical field of cognitive disorder assessment. Comprising a device body composed of a base and a display screen, and the device body is electrically connected with an intelligent evaluation training system. The problem that an existing assessment method has limitation is solved, and the portability of existing medical equipment is improved by performing integrated design on the equipment in a notebook computer form; multi-dimensional evaluation data of the patient are collected through a MoCA Monte-Evaluation scale, scores and change trends of all cognitive functions are extracted by using a correlation analysis method, and the scores and change trends are predicted through a neural network model to obtain a prediction result of the patient; corresponding rehabilitation training suggestions are retrieved from the decision tree classification model, optimization is carried out in combination with actual conditions, and then rehabilitation training is carried out on the patient; and by introducing an intelligent technology, personalized design and comprehensive intervention, the evaluation and rehabilitation training effects are further improved.

The invention relates to the technical field of blood glucose monitoring, in particular to an intelligent blood glucose monitoring and early warning method and system based on multi-source data fusion. Comprising the following steps: S1, acquiring blood glucose monitoring information of a monitored object, and determining first combined blood glucose monitoring information and second combined blood glucose monitoring information according to the blood glucose monitoring information; the first combined blood glucose monitoring information is an absolute difference between the blood glucose instantaneous information and the blood glucose continuous information; the second combined blood glucose monitoring information is a mean value of the blood glucose instantaneous information and the blood glucose continuous information; acquiring environment information of the monitored object, and determining a first environment variable quantity and a second environment variable quantity according to the environment information. According to the method, the dynamic deviation parameter and the early warning threshold value are fused, the measurement deviation and the physiological delay are quantified in combination with the extreme value features, double-source interference suppression and individualized early warning optimization of blood glucose monitoring are achieved in the plateau environment, and the risk identification accuracy and adaptability are improved.

Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and / or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.

The invention discloses a concentration improvement training method based on brain waves, which comprises the following steps: S1, a user wears brain wave monitoring equipment on the head, and the brain wave monitoring equipment collects brain wave data of the user in real time; s2, a brain wave monitoring module in the brain wave monitoring equipment preprocesses the brain wave data collected in the S1; the brain wave data of the user is monitored in real time, the concentration level of the user is analyzed through the AI algorithm, the training content and difficulty are automatically adjusted, immersive concentration training experience is provided in combination with the VR / AR technology, personalized training can be carried out according to the actual situation of the user, and the training method is scientific, systematic and high in practicability. The concentration level of the user can be effectively improved, and the training effect is improved.

A vagus nerve stimulation system for stimulating a vagus nerve in a biological subject, the system including a clip configured to be attached to a tragus of the subject, the clip including opposing arms configured so that a distal end of the arms are biased towards each other and electrodes positioned proximate a distal end of the arms on opposing faces so that the electrodes are urged into engagement with opposing faces of the tragus and a signal generator electrically connected to the electrodes, the signal generator being configured to generate at least one therapy signal that is applied to the vagus nerve within the tragus via the electrodes, to thereby modulate the vagus nerve.

The invention discloses a blood pressure management system based on dynamic environment perception, and the system comprises an environment perception module which is used for monitoring environment parameters including temperature, humidity, air pressure and noise in real time, and is in linkage with an external environment adjustment system through multi-parameter coupling compensation to optimize and adjust a measurement environment to a specified state; the data processing and control module is used for carrying out deep processing and analysis on the collected data and making a control decision according to an analysis result so as to realize accurate management and monitoring on the blood pressure; and the blood pressure measurement and interaction module is used for executing blood pressure measurement operation and feeding back a measurement result to the user. By integrating the environment sensing technology, the measurement parameters are adjusted in real time to adapt to different environment conditions, so that the accuracy of blood pressure measurement is improved, the device can sense and adapt to various environment changes, and it is ensured that the blood pressure can be accurately monitored in various environments.

Disclosed herein are systems and methods for sensing electrophysiological signals using dynamically changing lead configurations. A garment system is provided, the system having a plurality of electrodes, each disposed to contact a respective body portion of a plurality of body portions of a wearer of the garment system. Electrophysiological signals of the wearer are sensed in a first lead configuration of the garment system using a first subset of the electrodes. At least one change in contact location is detected between the electrodes and the body portions. Upon such detecting, further electrophysiological signals of the wearer are sensed in a second lead configuration of the garment system using a second subset of the electrodes. The second lead configuration may provide an improved signal quality relative to the first lead configuration.

The invention provides a body fat scale and a control method thereof, the body fat scale comprises an identity recognition unit and a control unit, the identity recognition unit comprises a first fingerprint recognition module and a second fingerprint recognition module, and the first fingerprint recognition module comprises an angle-adjustable first fingerprint recognition panel; the second fingerprint identification module comprises a second fingerprint identification panel which forms a first inclination angle with the vertical plane, and the angle of each module accords with the natural angle of contact between the finger of the user and the body fat scale, thereby improving the user experience. The control unit is configured to determine a target fingerprint identification module adapted to a user; determining a first identifier corresponding to the first fingerprint image in response to a fingerprint input operation of a user through a target fingerprint identification module; and based on the first identifier, establishing a matching relationship between the first initial data and the basic data corresponding to the first identifier. In the use process of the user, the use habits and the like of the user are learned to adapt to operation characteristics of different users, and the user identity is quickly and accurately determined.

The embodiments of the present disclosure herein address unresolved problems of quality of signals in real time for wearables to provide optimal signals which can be used for brain signal based applications. Further, conventional techniques fail to provide real-time calibration of wearable devices, to understand the quality of the signals from the wearable device. Embodiments herein provide a method and system for a real-time calibration of one or more Electroencephalography (EEG) signals received from a wearable Ear-EEG device. The system is leveraging quality of signals in real time for wearables to provide optimal signals which can be used for early detection of neurodegenerative disease and brain-computer interface (BCI) applications. Further, the system is able to detect electrodes in the wearable device where the EEG signals have not been collected because the contact was not established.

A computing device for enhancing communications is disclosed. The computing device includes a memory configured to store data, a video camera configured to receive video input data, and a microphone configured to receive audio input data. The computing device also includes a processor that is configured to analyze the received video input data, the received audio input data, and the data stored in the memory. The computing device further includes a visual display device that is configured to output a visual display based on the processor analysis, and a speaker output device that is configured to output an audio signal based on the processor analysis.

According to an embodiment, a computing device can include one or more processors and one or more computer-readable media that store instructions that, when executed by the processor(s), cause the computing device to perform operations. The operations can include obtaining a plurality of sleep stages associated with a sleep session of a user. The sleep session can be at least partially defined by an estimated bedtime of the user. The operations can further include identifying, in the plurality of sleep stages, one or more defined sleep stages indicative of a defined sleep state of the user. The operations can further include calculating a time before sound sleep (TBSS) metric based at least in part on the estimated bedtime of the user and a start time of the defined sleep stage(s). The operations can further include performing one or more operations based at least in part on the TBSS metric.

One embodiment may provide a method of providing a notification of signal loss in a glucose monitoring system, the method including, when a communication failure occurs between a terminal and a sensor transmitter, delaying a signal loss notification for a first period of time by the terminal, when the communication failure persists for the first period of time, further determining whether signal loss persists for a second period of time by the terminal, when the signal loss persists for the second period of time, generating the signal loss notification by the terminal, and outputting, by the terminal, the signal loss notification to a user.

The invention discloses a multi-modal sensing data automatic labeling method and system for gait assessment, and the system comprises a sensing collection module which comprises two paths of pressure sensors and a six-axis IMU sensor; the two paths of pressure sensors are used for acquiring plantar pressure data; the six-axis IMU sensor is used for acquiring lower limb movement data; the wireless transmission module is used for uploading the collected plantar pressure data and lower limb movement data to a cloud Internet of Things platform; the cloud data processing module is used for receiving and storing the plantar pressure data and the lower limb movement data; the data labeling module is used for automatically recognizing a gait cycle and carrying out multi-dimensional score labeling; and the user interaction module is used for interacting with the data annotation module and the cloud data processing module and displaying gait evaluation related data and results. According to the invention, a multi-mode sensing PCB is self-developed, a closed-loop system of collection, cloud loading, labeling and display is constructed, and accurate, continuous and remote evaluation of gaits of children with cerebral palsy is realized.

Introduced are methods and systems for monitoring a person's sleeping patterns, and detecting episodes of sleeping disorders such as snoring and sleep apnea. In one embodiment, a sensor strip attached to the mattress monitors the user's breathing, and detects signature frequencies corresponding to snoring and sleep apnea. Once a sleeping disorder is detected, a notification can be sent to a device associated with the user, or the user's bed can be automatically adjusted to alleviate the sleeping disorder.

The invention provides an activity level evaluation method and related equipment, and belongs to the technical field of terminals. The method comprises the following steps: screening a first heart rate of a user under low activity intensity and a second heart rate of the user under medium and high activity intensity according to walking heart rates in heart rate data of the user in the past one week; acquiring a weekly activity level assessment result of the user according to the first heart rate and the second heart rate by using an activity level assessment hierarchical model, the activity level assessment hierarchical model including a first activity level assessment model corresponding to low activity intensity and a second activity level assessment model corresponding to medium and high activity intensity, the activity acquisition module is used for acquiring the corresponding activity amount according to the heart rate, and the unit of the activity amount is Meide MET multiplied by time. According to the method, an activity level analysis result of a user is obtained by taking weekly activity data as an analysis unit through a personalized hierarchical model, so that the problem that an activity level evaluation result cannot be in butt joint with an international standard and is inaccurate is solved.

A system for determining placement of a catheter in a patient includes a hemodynamic sensor and a display. The hemodynamic sensor produces a signal representative of a right ventricular pressure waveform of the patient. The catheter is connected to the hemodynamic sensor. The system further includes one or more processors and computer-readable memory encoded with instructions that, when executed by the one or more processors, cause the system to receive the signal representative of the right ventricular pressure waveform and extract features from the right ventricular pressure waveform. The instructions further cause the system to compare the features to one or more value ranges of the features to determine that the catheter is correctly placed when values of the features are within the one or more value ranges or that the catheter is incorrectly placed, is not properly connected to the system, or signal quality issues are occurring.

The present invention refers to a respiratory therapy device comprising: a mouthpiece defining a longitudinal axis and having an end port; a housing connected to the mouthpiece and having a top surface, wherein at least a portion of the top surface is longitudinally spaced from the end port and defines a viewing surface adapted to be visible to a user having the end port disposed in a mouth of the user; and a feedback array disposed on the viewing surface, wherein the feedback array extends diagonally across the viewing surface and is curved or curvi-linear.

An air adapter tube for diagnosis and / or treatment of a respiratory disorder includes a tubular body configured to transport pressurized treatment air between a proximal end of the tubular body and a distal end of the tubular body, the proximal end being configured to connect to a patient interface, and the distal end being configured for connection to an air delivery tube; and a sensor being configured to generate a signal based on air passing at the proximal end for diagnosis and / or treatment of a respiratory disorder.

A system for facilitating resuscitation includes: a first electrode assembly having a therapy side and a first motion sensor; a second electrode assembly having a therapy side and a second motion sensor; processing circuitry operatively connected to and programmed to receive and process signals from the first and second motion sensors to estimate at least one of a chest compression depth and rate during administration of chest compressions and to compare the chest compression depth or rate to a desired range; and an output device for providing instructions to a user to administer chest compressions based on the comparison of the estimated chest compression depth or rate to the desired range. One or both of the electrode assemblies may be constructed so that the conductive therapeutic portion is able to maintain substantial conformance to the anatomy of the patient when coupled thereto.

A patient support apparatus for inferring a patient's future behavior tracks data related to patient bed exits from the patient support apparatus. A controller may be in communication with the patient support apparatus. The controller may include a processor and a non-transitory memory device. The memory device may include instructions that, when executed by the processor, acquire data related to patient bed exits.

An improved analyte monitoring system having a sensor and a transceiver with improved communication and / or user interface capabilities. The transceiver may communicate with and power the sensor. The transceiver may receive one or more analyte measurements from the sensor and may calculate one or more analyte concentrations based on the received analyte measurements. The transceiver may generate analyte concentration trends, alerts, and / or alarms based on the calculated analyte concentrations. The system may also include a display device, which may be, for example, a smartphone and may be used to display analyte measurements received from the transceiver. The display device may execute a mobile medical application. The system may include a data management system, which may be web-based.

The disclosure relates to a method of determining a desired rest period for a phosphagen regime physical activity of a user, comprising determining an end of the physical activity and the start of a rest period of the user and measuring an oxygen consumption during the rest period. The method comprises determining whether said oxygen consumption meets one or more predetermined parameter; and providing an indication if the one or more parameter is met.

According to an aspect, there is provided a computer-implemented method (200) of determining a concentration of glucose in blood of a subject, the method comprising: acquiring (202) first measurement data indicative of a response resulting from an interaction of an electromagnetic (EM) signal with blood of the subject in a body part of the subject; acquiring (204) second measurement data indicative of at least one parameter associated with the subject; and inferring (206) a concentration of glucose in the blood of the subject from the first measurement data and the second measurement data using a predictive model, the predictive model having been trained to infer a concentration of glucose in the blood of the subject from the first measurement data and the second measurement data.

The invention relates to the technical field of medical equipment, and discloses a medical image acquisition device which comprises a device base and a man-machine interaction device, a bed board is fixedly connected to the top of the device base, a positioning groove is formed in the top of the bed board, a grip and a positioning block are arranged on the side face of the positioning groove, and the grip and the positioning block are fixedly connected with the bed board; a fixing device is fixedly connected to the side face of the bed board, a scanning device is fixedly connected to the top of the fixing device, and a data acquisition module, a data preprocessing module, a data processing module, a data analysis module and a data transmission module are arranged in the scanning device; the device is provided with the data acquisition module and the data preprocessing module, respiration and motion frequencies of a to-be-detected person can be acquired, reduction of the number of motion artifacts and the probability of occurrence of the motion artifacts is facilitated, and therefore the acquisition efficiency and accuracy are improved; and the working intensity of medical staff can be reduced, so that the image acquisition efficiency is improved.

The application discloses a fall detection method and system based on a wrist inertia sensor, and the method comprises the following steps: acquiring motion data collected by an inertia sensor installed on a human wrist and performing pretreatment to obtain target motion data; constructing a fall detection model and a fall direction detection model based on one-dimensional convolution and a long short-term memory neural network; performing fall detection by using the fall detection model; if falls are detected for a continuous setting number of times, it is confirmed that a fall occurs, then fall direction detection is performed by using the fall direction detection model, and a fall direction detection result is output. The application combines the advantages of a convolution neural network and a long short-term memory neural network to construct the fall detection model and the fall direction detection model, not only realizes automatic recognition of a fall direction, but also improves the accuracy of fall detection; and a judgment method based on multiple continuous sliding window data can effectively avoid that an approximate fall behavior is wrongly judged as a fall.