1738 results about "Heart beat rate" patented technology

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.

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 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 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 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 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 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 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 invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention provides a remote monitoring method and system applied to children's watches, and relates to the technical field of children's watches, time axis synchronous alignment is carried out on collected multi-source heterogeneous data through an interpolation method, data time sequence chaos caused by sampling rate differences is prevented, and then the data time sequence chaos caused by sampling rate differences is prevented. Through dynamic coupling calculation of a light intensity mutation gradient and a heart rate stress coefficient, the cooperative influence of environment change on the physiological state of the child is quantified in real time, and the physiological state of the child is determined by further combining power spectrum centroid frequency and physiological phase difference analysis of the motion rhythm and logarithmic correlation of acoustic envelope frequency and GPS speed. In addition, an acoustic propagation delay compensation mechanism is introduced, a two-factor dynamic equation model is constructed through a time delay cross term, and a dynamic risk threshold generation mechanism based on a noise energy ratio is combined, so that the dynamic risk threshold of the motion intensity, the rhythm characteristics and the acoustic scene is effectively distinguished. Effective remote monitoring on child safety in different scenes is realized.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention discloses an AI-driven dynamic body temperature monitoring system and aims to solve the problem that existing body temperature detection equipment is difficult to adapt to individual differences and environment dynamic changes. The system comprises a temperature sensing unit, an environment acquisition unit and a heart rate sensing unit which are respectively used for acquiring skin temperature, environment parameters and heart rate variation characteristics; the system constructs a body temperature and environment incidence matrix, extracts a nonlinear coupling region, and fuses heart rate features to generate a three-mode state tensor; abnormal sensitivity is evaluated through an attention mechanism and a graph neural network model, and an individualized fever judgment threshold is dynamically generated in combination with a historical stable state, so that an accurate health response decision is realized; and the system further outputs behavior suggestions based on the decision result, such as water replenishing or medical treatment prompting, so that the intelligence and practicability of body temperature monitoring are improved.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention relates to the technical field of heart rate detection, in particular to a wearable underwear heart rate detection method and system.The method comprises the following steps that motion information of a user is obtained, and specific body posture changes are recognized based on the motion information of the user; generating a physiological artifact signal related to the specific body posture change based on the specific body posture change; removing a physiological artifact signal from the original physiological signal to obtain a purified physiological signal; heart rate abnormity judgment is conducted on the purified physiological signals. According to the scheme, the physiological artifact signals can be effectively removed, and the accuracy of heart rate detection is improved.

The invention discloses a fetal state dynamic monitoring method and system based on multi-modal deep learning, and the method comprises the steps: segmenting a fetal heart rate signal and a uterine contraction signal into a plurality of segmented time signals through a sliding window, and carrying out the denoising preprocessing of each segmented signal; performing wavelet transformation on the signal to obtain a time-frequency spectrogram, and inputting the time-frequency spectrogram into a 3D convolutional neural network to extract features; a cross attention module is introduced to fuse the two modal features, a transform encoder is put into use to complete time sequence feature extraction, and finally fetal state monitoring is achieved through a full connection layer. According to the method, the multimodal fusion and deep learning technology is utilized, and the fetal state can be accurately and dynamically monitored. Manual monitoring errors can be effectively reduced, the monitoring efficiency and accuracy are improved, powerful support is provided for medical staff to know the fetus condition in real time, the abnormal state of the fetus can be found in time, and the safety of the fetus is guaranteed.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

The invention discloses a monitoring method and system and a related device, which are applied to first electronic equipment, the first electronic equipment is provided with a first circuit, and the first circuit comprises a first electrode, a second electrode, a third electrode, a fourth electrode, an excitation current generation unit and a voltage measurement unit; receiving and responding to a first instruction, and determining that the first electrode and the second electrode are in good contact with skin through a first circuit; first information is determined through the first circuit, the first electrode makes contact with a first position of the user skin, the second electrode makes contact with a second position of the user skin, and the first position and the second position are located at the two ends of the thoracic cavity tissue respectively; and outputting first information, wherein the first information comprises one or more of the following items: cardiac output, stroke output, heart rate, ejection fraction and cardiac function judgment result. Therefore, the heart function condition of the user can be monitored in real time, and the user can conveniently monitor anytime and anywhere.

The present invention relates to computer vision technology and RPG heart rate detection technology, and specifically to a heart rate detection method based on CardiA2Net. The present invention utilizes CardiA2Net to combine a self-attention convolutional hybrid network (ACmix) with an attention-based long short-term memory (ALSTM) network to perform enhanced feature extraction from raw data and improve time series learning with an integrated attention mechanism. The present invention also proposes a comprehensive set of preprocessing methods for facial video datasets, effectively reducing noise interference in facial videos and improving the accuracy of heart rate detection. The present invention calculates heart rate through facial video analysis and simultaneously processes noise in the video, effectively reducing the difficulty of contactless heart rate detection and improving the accuracy of contactless heart rate detection.

The invention provides a health monitoring module and electronic equipment. The health monitoring module comprises a shell, an optical heart rate module, a circuit board and a light transmitting part, the shell is fixed to one side of the circuit board, and the shell and the circuit board jointly define a containing space. The shell comprises a conductive part made of conductive materials, one part of the conductive part is exposed out of the top face of the shell, and the other part of the conductive part is electrically connected with the circuit board. The optical heart rate module is located in the containing space, the optical heart rate module is fixed to the circuit board and electrically connected with the circuit board, the optical heart rate module and the light hole are oppositely arranged, and the optical heart rate module emits and receives light through the light hole and the light transmitting piece. The optical heart rate module can be used for acquiring PPG signals of a user. The conductive part can be used for collecting electric signals of a user. And the ECG signal of the user can be obtained after the electric signal is processed. Devices for measuring ECG signals and PPG signals are integrated in one module, so that the module can realize multiple functions in a small size.