533results 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 an ear-nose-throat department symptom monitoring method and system based on multi-modal data fusion, and relates to the technical field of medical monitoring. Comprising the steps of monitoring physiological data in real time, calling historical medical record data, collecting environment data, and preprocessing the monitored, called and collected data; carrying out feature fusion on the preprocessed data, extracting potential associated features, and carrying out multilevel and sequential feature learning; on the basis of the extracted features, an improved long-short-term memory network is used for conducting symptom prediction, in combination with historical trends of the blood sugar and the blood pressure of the patient, correlation between the symptoms and ear-nose-throat symptoms is evaluated in real time, and personalized symptom early warning is output; and sending a health prompt to the patient through the intelligent equipment or the mobile terminal application in real time. According to the invention, conventional symptom early warning can be carried out, and personalized health intervention measures can be provided according to individual health conditions.

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.

A combined one way inspiratory valve and expiratory release valve assembly is provided for controlling air flow in a respiratory treatment system. The valve assembly comprises a housing comprising a valve inlet, a valve outlet and at least one vent opening, and a diaphragm sealingly connected to the housing at an outer circumference of the diaphragm, wherein the diaphragm divides the housing into; a) an upstream portion, which is in fluid communication with the valve inlet, and b) a downstream portion which is in fluid communication with the valve outlet. The diaphragm has a circular, oval, elliptical or stadium shape. An inner portion of the diaphragm defines a one way inspiratory valve. An outer portion of the diaphragm defines an expiratory release valve. A method of characterising vent flow in a respiratory treatment system which includes such a valve is also described.

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.

Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.

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.

A haircare appliance has a main body housing an airflow generator for generating an airflow, an intermediate attachment for detachably connecting to the main body, and a hair treatment attachment for detachably connecting to the intermediate attachment. The intermediate attachment has an inlet for receiving the airflow from the main body and an outlet for emitting the airflow. The hair treatment attachment is configured to receive the airflow from the outlet of the intermediate attachment and emit the airflow towards hair to be treated in use. The intermediate attachment has a sensor module for sensing a property of at least one of hair to be treated and the hair treatment attachment.

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.

In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

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 relates to the field of medical equipment and health monitoring, and discloses a sleep monitoring system for lead shedding real-time monitoring and early warning, comprising: an intelligent material lead electrode for fitting the skin and providing high conductivity; the micro air pressure adjusting module is used for adjusting the attaching force through air pressure to guarantee tight contact between the electrode and the skin; the multi-sensor fusion module is used for monitoring the contact state between the electrode and the skin in real time; the microcontroller is used for processing a sensor signal and detecting a lead state; and the signal processing and fusing module is used for processing and fusing various sensor signals in real time and detecting a lead state. Accurate monitoring and dynamic adjustment are achieved through multi-sensor fusion and a deep learning algorithm, the detection precision and the system adaptability are improved, real-time data transmission and user reminding are ensured through the wireless transmission and vibration feedback module, the monitoring efficiency is improved, the equipment endurance is prolonged through the efficient energy management module, and the maintenance cost is reduced.

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.

A method of providing behavioral information for glucose level control according to an embodiment of the present disclosure includes: acquiring subject glucose level data corresponding to a hypoglycemic range through an analyte monitoring device; acquiring target glucose level data belonging to a normal glucose level range; acquiring a trained neural network model that calculates the behavioral information for changing a glucose level value corresponding to the hypoglycemic range to the normal glucose level range; acquiring the behavioral information including at least one of a type and amount of food to be consumed by a user from the subject glucose level data and the target glucose level data using the trained neural network model; and outputting the behavioral information through the user terminal device.

Systems, methods, and devices involve approaches for limiting the number of detected cardiac events for which a physician is alerted. Approaches involve detecting cardiac events based on electrocardiogram (ECG) data and associating the cardiac events with a criticality. ECG data associated with a subset of the cardiac events is transmitted to a remote computing system based, at least in part, on the criticality. An electronic notification is transmitted to a patient care group regarding at least one of the cardiac events in the subset. Approaches further include determining whether an acknowledgement of the electronic notification has been received by the server from the patient care group within a set period of time.

The invention provides a pressure monitoring patch and a processing method and system thereof, and relates to the technical field of medical monitoring, and the pressure monitoring patch comprises a pressure buffer body which is provided with a pasting contact surface, and the pasting contact surface is attached to a user monitoring part; the pressure monitor is arranged in the pressure buffer body and is used for sensing and monitoring the pressure of the monitored part of the user; the chemical parameter monitor is arranged in the pressure buffer body and is used for sensing and monitoring chemical parameters of the monitored part of the user; and the data receiving processor is connected with the pressure monitor and the chemical parameter monitor, receives the monitoring sensing data of the pressure monitor and the chemical parameter monitor, and analyzes and processes the monitoring sensing data to obtain a monitoring feedback result. The technical problems that in the prior art, a real-time monitoring, early warning and prevention means for the stress damage is lacked, and the stress damage cannot be effectively found and prevented in advance are solved, and the comprehensiveness, the accuracy and the early warning response speed of stress damage monitoring are improved.

This information processing method causes a computer to execute processing for acquiring motion information detected by an motion detection device for detecting a motion of a subject, storing the acquired motion information in a storage unit, deriving reference motion information of the body left side and the body right side of the subject on the basis of the stored motion information for a predetermined period, and determining whether an abnormal state exists in which there is a possibility of cerebral infarction in the subject on the basis of the derived reference motion information and the motion information of the body left side and the body right side of the subject for a detection time subsequent to the predetermined period.

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.

The invention relates to a method for monitoring cerebral apoplexy, a device for monitoring cerebral apoplexy and a storage medium, and the method comprises the steps: collecting physiological parameters such as electroencephalogram (EEG), oxyhemoglobin saturation (SpO2), electrocardiogram (ECG) and blood pressure through wearable equipment or a family health monitoring system, and carrying out data preprocessing, feature extraction and anomaly detection; the EEG signals are processed by utilizing frequency domain characteristics and short-time energy analysis, and the cerebral apoplexy risk is identified by adopting a support vector machine (SVM) classification model. The system can analyze and detect abnormal conditions in real time, and sends out early warning information through a mobile phone App, a short message or a telephone to remind a user to see a doctor in time. The system comprises wearable equipment, a family health system, a hospital platform and a mobile health application, and is suitable for personal health monitoring and centralized monitoring of medical institutions. The real-time performance and accuracy of cerebral apoplexy monitoring are remarkably improved, the user experience is enhanced, the medical response speed is increased, and the method has wide application prospects and practical value.

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 relates to the technical field of medical equipment, in particular to an analyte sensor needle assisting device and an analyte sensor implanting device. The analyte sensor implanting device adopts the locking structure to lock the monitor frame and the shell assembly, after the locking structure is unlocked through the unlocking part, the monitor frame moves in the direction close to the human body under the elastic force of the implanting elastic piece, and under the action of the stopping structure, the monitor frame can drive the guide needle frame to move and enable the guide needle to pierce the human body, so that the human body is prevented from being damaged. The analyte sensor is implanted into the human body. According to the invention, the elastic force is applied by implanting the elastic piece to complete the penetration of the guide needle, so that the requirement on the manual operation force is reduced. The guide needle frame can move in the direction away from the human body under the action of the needle returning elastic force of the needle returning elastic piece, then the guide needle is pulled out of the human body, the guide needle can be inserted in place under the action of the implantation elastic piece, the release structure can stably complete the release effect on the stop structure, and the operation difficulty of operators is reduced.

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.