Vita-course Technologies Co., Ltd.

Vita-course Technologies (hainan) Co., Ltd.

Vita-course Digital Technologies (tsingtao) Co., Ltd.

Oakwell Distribution, Inc.

Jiwei Zhao

Zhiyong Wang

Chuanmin Wei

Jiao Yu

Heng Peng

Ziming Deng

Zijian Huang

Ying Lu

Ting Ma

Jia Wang

Jianbin Liu

Ruiqing Ma

Zhiqiang Lv

Kezheng Ma

Mengcheng Hu

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure includes: receiving a request to determine a blood pressure of a first subject from a terminal, obtaining data relating to the first subject, the data relating to the first subject including data relating to heart activity of the first subject and personal information relating to the first subject, extracting target features relating to the first subject from the data relating to the first subject, determining a preliminary blood pressure of the first subject using a prediction model based on the target features relating to the first subject, determining a predicted blood pressure of the first subject using an optimization model based on the preliminary blood pressure and sending the predicted blood pressure of the first subject to the terminal in response to the request.

Systems and methods for determining blood pressure of subject

The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing heart activity of the subject may be received. A second signal representing time-varying information on at least one pulse wave of the subject may be received. A first feature in the first signal may be identified. A second feature in the second signal may be identified. A pulse transit time based on a difference between the first feature and the second feature may be computed. The blood pressure of the subject may be calculated according to a first model based on the computed pulse transit time and a first set of calibration values, the first set of calibration values relating to the subject.

System and method for blood pressure monitoring

The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject based on physiological features and personalized models. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on a pulse wave of the subject may be received. A personalized model for the subject may be designated. Effective physiological features of the subject based on the plurality of second signals may be determined. A blood pressure of the subject based on the effective physiological features and the designated model for the subject may be calculated.

System and method for physiological feature derivation

The present disclosure relates to a device, method and system for estimating or monitoring the health condition of a subject. At least one processor, when executing instructions, may perform one or more of the following operations. At least one physiological signal or information including a physiological signal of a subject may be received. At least one physiological parameter of interest may be generated based on the physiological signal. The physiological parameter of interest may be analyzed according to an analysis model. A physiological result may be generated. A recommendation may be provided based on the physiological analysis result.

System and method for health monitoring

A method may include acquiring first physiological data relating to a first subject, extracting at least one first physiological feature from the first physiological data relating to the first subject, determining a first model relating to at least one first reference physiological feature, generating, based on the first model and the at least one first physiological feature, a second model, the second model relating to at least one second reference physiological feature corresponding to the second model, and determining, based on the second model and the at least one first physiological feature, at least one identification physiological feature relating to the first subject. In some embodiments, the at least one identification physiological feature may correspond to the at least one second reference physiological feature.

System and method for identifying user

A device, method and system for calculating, estimating, or monitoring the blood pressure of a subject. A first signal representing heart activity of a subject may be received. A plurality of second signals representing time-varying information on at least one pulse wave of the subject may be received from a plurality of body locations of the subject. A first feature of the first signal may be identified. For each of the plurality of second signals, a second feature may be identified. A pulse transit time based on a difference of the first feature and at least one of the second features may be computed. A blood pressure of the subject may be calculated according to a model based on the computed pulse transit time. The model may include a compensation term relating to the plurality of second signals or the second features thereof.

System and method for physiological parameter monitoring

Systems and methods for determining an arrhythmia type are provided. A method may include: obtaining an electrocardiogram signal, obtaining a feature vector of the electrocardiogram signal, obtaining a trained prediction model, and determining an arrhythmia type of the electrocardiogram signal based on the trained prediction model and the feature vector. The feature vector includes a threshold crossing sample (TCSC) percent feature, an L-Z complexity feature, an empirical mode decomposition (EMD) complex number feature, a sample entropy feature, and a set of wavelet transform energy features.

Systems and methods for determining an arrhythmia type

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure may include: receiving a request to determine blood pressure of a first subject from a terminal, obtaining data related to heart activity of the first subject, determining a personalized model for predicting blood pressure with respect to the first subject, determining the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject, and sending the blood pressure of the first subject to the terminal in response to the request.

Systems and methods for determining blood pressure of a subject

The present disclosure relates to systems and methods for analyzing physiological sign information, including information acquisition, data storage, calculation or analysis, processing, result output, etc. The systems may perform a calculation or an analysis on the acquired information by a plurality of algorithms, perform a determination or a processing on the calculation result, and output the determination result of the processed physiological information.

Systems and methods for physiological sign analysis

The present disclosure provides a system and method for analyzing a physiological parameter of a vital sign signal. The method may include acquiring a vital sign signal, storing data, computing and analyzing, processing, and outputting a result. The system may compute and analyze the physiological parameter of the vital sign signal, especially a blood oxygen saturation, via a plurality of algorithms, judge or process the computation result, and output the judgment result.

Method and system for detecting the oxygen saturation within the blood

A device (), method and system () for calculating, estimating, or monitoring the blood pressure of a subject. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing heart activity of the subject may be received. A second signal representing time-varying information on at least one pulse wave of the subject may be received. A first feature in the first signal may be identified. A second feature in the second signal may be identified. A pulse transit time based on a difference between the first feature and the second feature may be computed. The blood pressure of the subject may be calculated according to a first model based on the computed pulse transit time and a first set of calibration values, the first set of calibration values relating to the subject.

The present disclosure relates to a vital sign extraction method and system. The system comprises a receiving module for receiving at least one type of physiological information; a feature extraction module configured to extract a first feature and a second feature of the physiological information by using a first technique and a second technique; a processing module for performing a matching calculation on the first feature and the second feature, marking matching results, and identifying a noise result of the physiological information based on the matching results; and a calculation module for calculating a physiological sign of a living body.

Physiological sign information acquisition method and system

A stress management device is formed in an integrated design that enables pulse measurement, display of results and a power supply gathered into a small finger clip device. The stress management device includes a non-invasive infrared sensor that identifies and measures pulse rate variability and utilizes that information to calculate the power spectrum distribution and create a curve of the results multiple times per second. The device incorporates a display screen on which a graphic image of a pair of lungs filling and emptying to encourage the user into a predetermined breathing pattern known to reduce stress levels during a training period. After the completion of the training period, the device presents a score reflecting the percentage of times the user achieved high, medium and low relaxation states, and a pie chart reflecting the distribution of the states of relaxation calculated during the training period.

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Shenzhen, China

Jiwei Zhao