2139 results about "Heart beat rate" patented technology

The present invention relates to the technical field of heart rate monitoring, discloses a heart rate monitoring method, wherein the heart rate monitoring method comprises: obtaining initial electrocardio signals of a user by a preset heart rate sensor, and acquiring initial acceleration energies of the user by a preset acceleration sensor; pre-treating the initial electrocardio signals, obtaining pretreated target electrocardio signals, and pre-treating the initial acceleration energies to obtain pretreated target acceleration energies, the present invention can be used to help doctors and users to know current heart rate conditions, make personalized and well-targeted health management measures and improve living quality.

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

The invention relates to the technical field of health monitoring, in particular to a deep sleep analysis and early warning system integrated with heart rate monitoring, which comprises a physiological parameter acquisition module, an environmental parameter acquisition module, a deep sleep analysis module, an anomaly detection and early warning module, a dynamic feedback and optimization module, a personalized suggestion module and a data storage and analysis module. The physiological parameter acquisition module acquires physiological parameters of a user, the environmental parameter acquisition module acquires environmental parameters, the deep sleep analysis module identifies deep sleep features based on a multivariable data analysis algorithm and evaluates sleep quality, and the anomaly detection and early warning module detects physiological anomaly and triggers early warning. The dynamic feedback and optimization module optimizes model parameters according to user feedback, the personalized suggestion module generates sleep improvement suggestions, and the data storage and analysis module stores data. According to the invention, the deep sleep state of the user can be comprehensively analyzed, a personalized health management and sleep optimization scheme is provided, and the sleep quality of the user is improved.

A medical device with an implantable blood pump and a control and sensing unit configured to determine the flow rate generated by the blood pump when driven by an electric motor, wherein the flow rate is determined using peak-to-peak current data generated by the electric motor and, in some cases, associated heart rate data. In some embodiments, the validity of pulsatility of the resulting blood flow is determined and, if out of predetermined limits, an alarm may be actuated.

The invention discloses a sleep monitoring analysis method and system based on HRV, and relates to the technical field of sleep monitoring, and the method comprises the following steps: at the beginning of monitoring, continuously recording a continuous heartbeat time interval of a sleeper at a preset initial heartbeat interval acquisition frequency for evaluating the autonomic nervous system state of the sleeper at night; the respiration data of the sleeper is acquired in real time while the heart rate data is acquired. By combining real-time respiration data and an intelligent increment control algorithm, when irregular respiration is detected, the heartbeat interval acquisition frequency is dynamically adjusted, so that the sampling precision of heart rate change is improved, fine fluctuation of a sleeper when apnea or irregular respiration occurs can be captured, and the sleep quality of the sleeper can be improved. In addition, the detection accuracy of the apnea event can be improved under high sensitivity, potential health risks can be recognized in time, more reliable data support is provided for improvement of sleep quality and early intervention, and therefore health damage caused by missing detection is effectively avoided.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention discloses a fatigue driving monitoring and early warning system based on adaptive learning, and the system comprises a data processing module which is used for collecting and preprocessing driving data; the facial feature module is used for constructing a facial key point dynamic trajectory graph; the physiological feature module is used for extracting a heart rate multi-order modal component and a skin electric energy disturbance factor; the behavior characteristic module is used for extracting a periodic disturbance degree, a lane offset curvature fluctuation range and a control rhythm index; the feature fusion module is used for integrating multi-source information and carrying out time domain modeling; the recognition updating module is used for constructing an individualized recognition model and dynamically updating model parameters; the fatigue evaluation module is used for evaluating a fatigue state and generating a corresponding grade output signal; and the early warning intervention module is used for triggering voice prompt, seat vibration or visual prompt according to the output signal. According to the invention, real-time identification and intelligent intervention of the driving fatigue state are realized, and driving safety and response efficiency are improved.

The invention relates to the technical field of health assessment, in particular to a newborn health assessment method and system based on visual analysis, and the method comprises the following steps: obtaining a newborn image frame sequence, extracting a hue curve, recognizing a variation region to generate a layer, and extracting a mutation region bitmap group in combination with a heart rate RR interval difference value; and analyzing an included angle mapping grid between the center of gravity of the pigment and the heart rate slope, superposing a respiratory rate curvature, performing co-occurrence clustering to form a trend graph block, and evaluating a risk distribution interface formed by joint mutation. According to the method, a dynamic coupling relation between an image and a physiological signal is established through slope direction angle change and included angle deviation, cross-dimension mapping of heart rate trend change and visual feature change is achieved, the heart rate, skin color tracks and curvature change of respiratory frequency are fused, a trend gathering map is constructed, and the dynamic coupling relation between the image and the physiological signal is obtained. The risk judgment and display are completed according to the joint grading of the block density, the time span and the parameter quantity in the atlas, and the health level fluctuation trend is visually presented in the risk evolution sorting.

The invention provides an autism assessment system and method based on multi-modal time sequence data fusion, and relates to the technical field of children autism spectrum disorder assessment calculation processing. The invention innovatively provides a dual time sequence alignment method based on dynamic time warping (DTW) and LSTM prediction, the problem of time sequence dislocation of cross-modal data (heart rate / eye movement / limb movement) caused by acquisition frequency difference is solved, millisecond-level synchronization precision is realized, the technical problem of cross-modal data time sequence dislocation in an existing autism assessment system is solved, and the accuracy of time sequence alignment of the cross-modal data in the autism assessment system is improved. The small-range time deviation caused by acquisition equipment delay or physiological response difference is eliminated, and the robustness and reliability of the autism evaluation system are improved.

The invention is suitable for the technical field of fall detection and help calling, and provides a fall detection and help calling method for the elderly at home, and the method comprises the steps: collecting the multi-source health monitoring data of the elderly through a monitoring camera and a sensor, and carrying out the preprocessing based on the collected data; respectively extracting walking posture features, getting-up and sitting-down posture features, daily activity posture features, motion features, acceleration features, gyroscope features and heart rate features of the elderly; inputting the extracted features into a trained abnormal action detection model for action detection; when the old people have abnormal behavior actions, performing multi-dimensional analysis on the abnormal behavior actions of the old people by utilizing a clustering algorithm; and feeding back the analysis result of the abnormal behavior action of the old person in real time according to a preset help calling rule. According to the invention, the aims of comprehensively monitoring the health state of the elderly, timely discovering abnormity, deeply analyzing reasons and quickly responding to rescue can be fulfilled.

The invention relates to the technical field of physical education teaching, in particular to an interactive physical education teaching method based on a large model, and aims to realize real-time monitoring and personalized training guidance of the motion state of a student through an advanced sensor technology and a machine learning model. The method comprises the following steps: firstly, collecting motion data of a student in real time through a camera and a depth sensor, including motion posture, speed, response time, heart rate and other information; then, the collected data are input into a pre-trained large model, the model comprises a convolutional neural network, a support vector machine and a random forest model which are respectively used for evaluating the action standard, the exercise intensity and the fatigue degree of students, the large model automatically generates personalized training items, and the exercise postures of the students are captured in real time; according to the motion deviation correcting system and method, by comprehensively applying various technical means, the scientificity and individuation level of physical education are improved, and the motion deviation correcting system and method have wide application prospects.

The present invention provides a data processing method for a heart rate monitoring system, and belongs to the technical field of data processing. The present invention effectively solves the problems that heart rate monitoring systems have low accuracy in acquiring raw heart rate data of patients, have difficulty in removing heart rate interference factors, and require precise heart rate value data and analysis results. The present invention is used in a heart rate monitoring system, and the heart rate monitoring system comprises: a power supply apparatus, an information acquisition apparatus, an information conditioning module, a data fusion unit, a data analysis module, an anomaly detection alarm module, a display unit, and a storage unit. The present invention comprises the following steps: S1, information acquisition; S2, information conditioning; S3, data fusion; and S4, data analysis. The present invention can ensure that the heart rate monitoring system can accurately and reliably monitor and interpret heart rate data during data processing, thereby providing more reliable support for clinical practice.

The invention provides a depression state assessment method and device based on heart rate variability characteristics and a parallel neural network. Real-time electrocardiosignals are collected in real time through wearable equipment and transmitted to a mobile terminal, the mobile terminal transmits the electrocardiosignals to a cloud server, and work of signal preprocessing, heart rate variability (HRV) feature extraction, feature selection and data enhancement and depth model construction and optimization is carried out on the cloud server. And evaluating the depression state of the subject. The cloud outputs an evaluation result to a display screen of the mobile terminal to be displayed, the depression state of the subject is finally displayed, and the four evaluated depression states are healthy, mild depression, moderate depression and severe depression; in addition, the terminal also supports depression state historical record query and key HRV feature tracing.

The invention discloses a multi-modal emotion analysis method and system, and the method comprises the steps: carrying out the heart rate variability analysis of a collected vital sign signal, so as to extract a time domain feature, a frequency domain feature and a nonlinear feature corresponding to a heart rate variability signal obtained after the analysis, fusing the time domain feature, the frequency domain feature and the nonlinear feature to generate a heart rate variability feature corresponding to the vital sign signal; calculating a power spectrum and a power spectrum entropy corresponding to the collected electroencephalogram signals, and extracting electroencephalogram characteristics corresponding to the electroencephalogram signals according to the power spectrum and the power spectrum entropy; extracting skin electric characteristics corresponding to the collected skin electric signals; respectively inputting the heart rate variability feature, the electroencephalogram feature and the galvanic skin feature into a pre-trained emotion recognition model, and outputting a plurality of emotion analysis results; and acquiring an emotion regulation scheme matched with each emotion analysis result to output a corresponding emotion regulation strategy, thereby improving the accuracy of the emotion analysis and regulation strategy.

The invention relates to a human motion data processing method, and belongs to the technical field of data processing. Comprising the following steps: controlling a microwave radar to emit a detection signal, and detecting a heart rate signal and a respiration signal of a human body; performing clustering analysis on the data according to the collected heart rate and respiratory rate, and extracting heart rate, respiratory rate change, duration and exercise intensity characteristics; according to the extracted heart rate change, duration, exercise intensity and other characteristics, the exercise stage is recognized, and according to the neural network model library, an energy consumption result is output in combination with data analysis. According to the human motion data processing method provided by the invention, the microwave radar is combined with the intelligent wearable device, so that the detection precision of heart rate and respiration signals is remarkably improved, multi-modal data fusion is realized, personalized heart rate prediction is performed, motion stage division is optimized in combination with acceleration data, and the accuracy of motion data processing is improved. The accuracy of motion mode recognition is improved, and personalized motion suggestions can be provided for the user.

The invention discloses a non-contact heart rate detection method and system and related equipment, and the detection method comprises the steps: obtaining an original face video, and carrying out the preprocessing of the original face video, and obtaining a video frame sequence; calculating a pixel-by-pixel difference value between adjacent frames based on the video frame sequence to extract a difference video frame sequence; performing convolution operation and processing on the video frame sequence and the differential video frame sequence to reduce the spatial size and extract preliminary features, and applying 3D convolution to obtain spatial-temporal features; the spatial-temporal features are projected into query vectors, key vectors and value vectors by using time difference convolution, and correlation among the vectors is calculated in a frequency domain. Two paths are designed to process an original video frame sequence and a differential video frame sequence respectively, and time-frequency information related to the heart rate in video frames is effectively captured by extracting time-frequency features. And the features of the two paths are spliced in the channel dimension, and finally spatial averaging, up-sampling and 1D convolution are carried out to finally obtain an accurate rPPG signal prediction result.

The exercise risk assessment method of the present invention includes the steps of acquiring historical electrocardiogram signals of an athlete awaiting evaluation at the exercise intensity at the time awaiting evaluation and at different previous exercise intensities; inputting the heart beat interval series for different time periods obtained based on the historical electrocardiogram signals into a fuzzy entropy model to calculate fuzzy entropy and obtain an exercise fuzzy entropy series; and inputting the time awaiting evaluation, the exercise fuzzy entropy series, and the exercise intensity into a series change point identification model to perform change point identification, thereby performing risk assessment based on the change point identification result obtained by inputting the time awaiting evaluation, the exercise fuzzy entropy series, and the exercise intensity into a series change point identification model, and a risk state transition model to obtain a risk assessment result at the time awaiting evaluation. [Effects] Not only can it reliably quantify the disorder and complexity of heart rate variability, but it can also analyze the nonlinear growth law of exercise load in real time, effectively quantify the relationship between exercise risk and exercise intensity, and further improve the reliability and accuracy of exercise risk assessment.

The invention relates to a chronic heart failure dynamic course evolution prediction model construction method. Comprising the following steps: uniformly mapping continuous variables including LVEF and heart rate and event variables into a time trajectory frame through an event alignment and time domain nesting strategy; using a local change rate algorithm to identify inflection points including states before acute deterioration and intervention reactions in the course of disease of each patient; constructing a state fragment set for supporting hierarchical modeling in an evolution stage; a bidirectional fusion method of trajectory clustering and medical knowledge embedding is used to construct a state space with clinical interpretability including a compensation period, edge decompensation and an acute deterioration period; taking the trajectory vector as a main input, taking a state space as a prediction target, and introducing a dual-channel structure; predicting a future path based on the current state; the disease course track change of early medication / non-hospitalization / treatment scheme change is simulated; a doctor is supported to deduce a result; the change of the output state is analyzed through perturbation of the current trajectory, and key variables are found out.

The invention relates to the technical field of medical monitoring, in particular to an intelligent old people health monitoring system, which comprises a heart rate module for collecting heart rate signals, identifying local peak values in the signals to detect heartbeat, measuring time intervals between continuous peak values to determine heart beat intervals, and calculating differences between adjacent intervals to evaluate variability. According to the method, through synchronous analysis of multi-dimensional data of the comprehensive heart rate, the skin conductance and the facial expression, the comprehensive understanding and evaluation ability of the health state of the old people is enhanced. The complex relationship between the emotion change and the physiological response is accurately captured and analyzed by integrating multi-source data without singly depending on any physiological index. Through the method, the health warning signal can be identified in the subtle change, and early warning can be carried out in time. Besides, environmental factors such as illumination, music and fragrance can be dynamically adjusted according to the mood and health state monitored in real time, so that more personalized and optimized environmental response is provided.

The invention discloses a self-adaptive position constraint exoskeleton rehabilitation robot on-demand auxiliary control system and method, and relates to the technical field of rehabilitation robot control. Comprising the following steps: collecting historical motion time sequence data of a patient, and recording a corresponding motion intention of the patient; establishing a deep learning network model, taking the historical motion time sequence data as an input sample, taking the corresponding motion intention as a label, and training the model; collecting real-time motion time sequence data of the patient, inputting the real-time motion time sequence data into the trained model to obtain real-time motion intention data of the patient, and calculating and generating target state parameters based on the real-time motion intention data; establishing a state parameter adaptive adjustment model, collecting a current state parameter, and analyzing and generating control output of the motion joint based on the generated target state parameter; and collecting real-time heart rate data of the patient, correcting the data in the control output, and sending a control instruction to the exoskeleton rehabilitation robot based on the corrected data.

The invention discloses a respiratory training quantitative evaluation method, which comprises the following steps of: acquiring physiological parameter characteristics related to heart rate, respiration and brain function of a human body through a human body physiological data acquisition platform, and performing dimensionality reduction, dimensionless processing and weighted summation on the characteristics to obtain a respiratory training quantitative evaluation result. The method comprises the following specific steps: firstly, collecting human heart rate, respiration and brain function related physiological features through a physiological parameter collection platform, then calculating feature classification weight of each dimension through random forest and cross validation and removing redundant feature dimensions, and then removing dimensions of features in a feature subset through a dimensionless method; and finally, performing weighted summation on the feature data through the feature weight to obtain a respiratory training quantitative evaluation result. Quantitative evaluation is carried out on respiratory training, so that people can be helped to formulate more scientific respiratory training strategies and methods.

The invention discloses a pressure sensitivity classification evaluation method based on heart rate variability and wearable equipment, and relates to the technical field of biomedical signal processing. According to the method, the collected signals can be processed to form the RR interval sequence and the resultant acceleration, the motion state is judged, the motion state mark is set, the RR interval sequence and the motion state mark are aligned according to the timestamp, the structured data are generated, the pressure sensitivity level of an individual is judged based on the structured data, and the pressure sensitivity level of the individual is calculated. The subjective influence of pressure sensitivity evaluation can be avoided, and the classification precision and adaptability are improved.

The invention provides a heart rate calculation method based on physical and psychological health analysis and a monitoring sensing device, and belongs to the technical field of medical equipment.The heart rate calculation method specifically comprises the steps that credible monitoring times in different stable monitoring times are determined by means of temperature change data, and the temperature change data in the different credible monitoring times are used for calculating the heart rate according to the credible monitoring times; the credible monitoring times are divided into different similar monitoring parameter groups, and when it is determined that the similar monitoring parameter groups with the health analysis results not meeting the requirements exist according to the analysis results of the heart rate data of the different credible monitoring times in the different similar monitoring parameter groups, the credible monitoring times are determined to be the credible monitoring times. According to the method, the distribution data and the temperature change data of the credible monitoring times in the different similar monitoring parameter groups are obtained, and the health analysis result of the monitoring analysis target is determined in combination with the health analysis results of the different similar monitoring parameter groups, so that the accuracy of the health analysis result is improved.

The invention discloses a remote photoplethysmography (rPPG) signal processing method and system based on a long and short term space-time convolution network, and belongs to the crossing field of biomedical signal processing and computer vision. In order to solve the problem of signal distortion caused by illumination fluctuation, motion artifacts and skin color differences, the method constructs a multi-scale space-time modeling framework: extracting local space-time features of transient changes of facial capillaries by adopting a 3D convolutional network, capturing long-range periodic features of heart rate rhythm in combination with a 1D expansion convolutional network, and establishing a multi-scale space-time modeling framework; the spatial-temporal characteristics are dynamically fused through the self-attention weight and the gating residual structure, and the anti-interference capability is improved. In the preprocessing stage, a face area is positioned through MTCNN, motion artifacts are compensated by using an optical flow equation, and signal purity is enhanced by combining a skin color mask and a YUV color space. The system adopts a deep separable convolution and parallel acceleration strategy to realize light weight, and optimizes the network through time domain MSE loss and frequency domain KL divergence. Experiments show that the phase error of the method is reduced by 40% in a dynamic scene, the signal amplitude of a deep skin color group is improved by 60%, the method is suitable for non-contact health monitoring equipment, and the robustness and the measurement precision of the rPPG technology in a complex environment are remarkably improved.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

A hybrid time domain multiplexing (TDM) and frequency domain multiplexing (FDM) configuration protocol for a doppler-based ultrasound fetal monitoring system (FMS) is provided. In an example, a FMS determines context information regarding an operating context of the FMS, the context information comprising a number of fetal sensor devices (FSDs) activated for monitoring a corresponding number of fetuses of a single mother, wherein the FSDs respectively comprise ultrasound transducers configured to measure fetal parameters of a single fetus, and wherein each of the FSDs are configurable to operate using either the FDM mode or the TDM mode. The FMS further configures respective operating modes (i.e., FDM mode or TDM mode) of the FSDs based on the context information and in accordance with a configuration protocol that varies the respective operating modes (i.e., FDM mode or TDM mode) of the FSDs under different operating contexts of the FMS.

The invention discloses a wireless heart rate monitoring and short message alarm method based on an Arduino single-chip microcomputer, and particularly relates to the technical field of heart rate monitoring, which comprises the following steps: acquiring environment and user activity information through a multi-source sensor, acquiring a heart rate data priority according to an environment risk score and user activity intensity, and sending the heart rate data priority to the Arduino single-chip microcomputer; future environment and activity conditions are predicted based on the long short-term memory network, sampling frequency and transmission strategies are adjusted in advance, prospective intervention on potential risks is achieved, practicability, accuracy and reliability of the heart rate monitoring system are improved, and the heart rate monitoring system is suitable for wide health monitoring scenes; when the heart rate abnormity is compared with the threshold value, multi-level alarm is triggered, the high-frequency sampling energy consumption of the sensor can be reduced, the data transmission path is evaluated and optimized in combination with the network reliability, the abnormity detection precision and stability are remarkably improved, and the problems that the abnormity is difficult to recognize in time and the power consumption is too high in a single heart rate sensor in a complex environment are solved.

The invention relates to the technical field of fetal heart monitoring, in particular to a fetal heart monitoring analysis method, monitoring equipment and a storage medium. The fetal heart monitoring analysis method comprises the following steps: acquiring a heart rate signal and clinical table data of a fetus; performing continuous wavelet transform on the heart rate signal to obtain a time-frequency domain image, and inputting the time-frequency domain image into a ResNet50 network and a Mangban module to obtain image time sequence characteristics; inputting the clinical table data into a multi-scale text encoder, obtaining dependency relationships of the clinical table data in time sequence data of different scales, and fusing the dependency relationships to obtain time sequence dependency features; fusing the time sequence features and the time sequence dependence features of the image by adopting a cross-modal attention mechanism to obtain fused features; the fusion features are input into a classification module, an anoxia risk assessment result is obtained, and a joint loss function of the classification module comprises intra-modal alignment loss, inter-modal alignment loss and masking language modeling loss. According to the invention, the accuracy and reliability of diagnosis are improved.

The invention relates to a video heart rate detection method based on deep learning, and belongs to the technical field of image processing. The method comprises the following steps: preprocessing a to-be-detected video to generate a preprocessed face video sequence; inputting the preprocessed face video sequence into a double-flow collaborative spatial-temporal feature enhancement module group to generate spatial-temporal features; inputting the spatial-temporal feature representation into a multi-scale spatial-temporal convolution module, and extracting spatial-temporal features of different scales in parallel; training the detection model by adopting a time-frequency domain joint constraint composite loss function; and inputting the spatiotemporal features of different scales into a trained detection model to obtain a video heart rate detection result output by the detection model. The invention aims to solve the technical problem that the anti-interference capability and the detection precision are difficult to guarantee in the prior art.

The embodiment of the invention provides a maximum oxygen uptake evaluation method and system. The maximum oxygen uptake evaluation method comprises the steps that physiological information of a user is obtained; based on a heart rate sensor assembly and an inertial sensor assembly, motion information and heart rate information of the user in the test motion execution period are collected; calculating the metabolic equivalent weight of the user based on the physiological information, the motion information and the heart rate information; and calculating and determining the maximum oxygen uptake of the user based on the metabolic equivalent and the heart rate information. The maximum oxygen uptake evaluation method provided by the invention is used for quickly and accurately evaluating and determining the maximum oxygen uptake of the user.