CN113080918A - BCG-based non-contact heart rate monitoring method and system - Google Patents

BCG-based non-contact heart rate monitoring method and system Download PDF

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Publication number
CN113080918A
CN113080918A CN202110266567.3A CN202110266567A CN113080918A CN 113080918 A CN113080918 A CN 113080918A CN 202110266567 A CN202110266567 A CN 202110266567A CN 113080918 A CN113080918 A CN 113080918A
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module
heart rate
bcg
input end
output end
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CN202110266567.3A
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Chinese (zh)
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曹世华
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Hangzhou Aoxin Technology Co ltd
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Hangzhou Aoxin Technology Co ltd
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Priority to CN202110266567.3A priority Critical patent/CN113080918A/en
Priority to AU2021102014A priority patent/AU2021102014A4/en
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1102Ballistocardiography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6892Mats
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7253Details of waveform analysis characterised by using transforms
    • A61B5/726Details of waveform analysis characterised by using transforms using Wavelet transforms
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/74Details of notification to user or communication with user or patient ; user input means
    • A61B5/746Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

Abstract

The invention discloses a non-contact heart rate monitoring method and system based on BCG. In the invention, the BCG acquisition module is fixedly installed outside the control module, the output end of the control module is connected with the input end of the BCG acquisition module, the output end of the BCG acquisition module is connected with the input end of the control module, the input end of the BCG acquisition module is fixedly provided with the vibration sensor, the output end of the processor module is connected with the input end of the data recording module, the output end of the data analysis module is connected with the input end of the data early warning module, the output end of the data early warning module is connected with the input end of the receiving terminal, and the output end of the receiving terminal is connected with the input end of the display terminal; the lifting wavelet module and the heart rate extraction module are arranged inside the BCG acquisition module, so that the data acquired by the vibration sensor and the BCG acquisition module can be precisely analyzed and processed, and the stability of the data is improved.

Description

BCG-based non-contact heart rate monitoring method and system
Technical Field
The invention belongs to the technical field of heart rate detection, and particularly relates to a BCG-based non-contact heart rate monitoring method and system.
Background
The wearable vital sign monitoring technology is restricted by factors such as sensors, electrode plates, leads and battery endurance, so that the experience and comfort of a user are seriously affected, and great difficulty is brought to practical application. The non-contact vital sign monitoring technology does not affect the user, completes the acquisition of the vital signs under the non-inductive condition, and is a vital sign monitoring technology with a very promising prospect. For example, heart rate monitoring mainly adopts manual observation chest fluctuation to judge the heart rate at present, wastes time and energy and produces errors easily. Or the wearable thermal resistance lead heart rate monitoring or abdominal pressure belt heart rate monitoring is adopted, so that the accuracy is high.
However, when a common heart rate method is used for detecting, the heart rate is extracted and calculated simply, so that the accuracy is not high enough, and meanwhile, in a common system, when the system power supply voltage is unstable, the system cannot be used for detecting, so that the service life of the system is not long enough.
Disclosure of Invention
The invention aims to: in order to solve the above-mentioned proposed problems, a BCG-based non-contact heart rate monitoring method and system are provided.
The technical scheme adopted by the invention is as follows: a non-contact heart rate monitoring method and system based on BCG comprises a processor module, a control module, a data recording module, a data analysis module, a data early warning module, a receiving terminal, a BCG acquisition module, a power supply module, a vibration sensor, a voltage detection module, a buzzer alarm, a display terminal, a data output module, a heart rate extraction module, a circuit amplification module and a lifting wavelet module, wherein the output end of the processor module is connected with the input end of the control module, the BCG acquisition module is fixedly installed outside the control module, the output end of the control module is connected with the input end of the BCG acquisition module, the output end of the BCG acquisition module is connected with the input end of the control module, the vibration sensor is fixedly installed at the input end of the BCG acquisition module, and the output end of the processor module is connected with the input end of the, the output end of the data recording module is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the data early warning module, the output end of the data early warning module is connected with the input end of the receiving terminal, and the output end of the receiving terminal is connected with the input end of the display terminal.
In a preferred embodiment, the power supply module is fixedly mounted on the outside of the processor module, the voltage detection module is arranged inside the power supply module, and the buzzing alarm is electrically connected to the outside of the voltage detection module.
In a preferred embodiment, the circuit amplification module, the lifting wavelet module, the heart rate extraction module and the data output module are fixedly installed inside the voltage detection module.
In a preferred embodiment, an output end of the circuit amplification module is connected to an input end of the voltage detection module, an output end of the circuit amplification module is connected to an input end of the lifting wavelet module, an output end of the lifting wavelet module is connected to an input end of the heart rate extraction module, and an output end of the processor module is connected to an input end of the data output module.
In a preferred embodiment, the internal lifting mode of the lifting wavelet module realizes db2 discrete wavelet transform, the main steps of the lifting wavelet transform include splitting prediction and updating, wherein the splitting is to divide an original signal sequence into an even sequence and an odd sequence, the prediction mainly predicts a sequence of odd columns and even sequences through an operation of the even sequence and odd sequence, and the updating is to update the odd sequence of the even sequence through an operation so as to keep the even sequence consistent with the original signal, so as to better predict; each step of the lifting wavelet transformation has an inverse process, and the reconstruction process is the inverse process of the decomposition process and comprises three steps of inverse updating, inverse prediction and combination.
In a preferred embodiment, the heart rate extraction module extracts the heart rate by using a time domain method, the heart rate detection in the time domain mainly uses the periodic characteristics of the PPG signal to obtain a pulse rate value, and the pulse rate value is equivalent to a heart rate value; the dominant wave of the PPG signal in the time domain is more obvious, and the pulse rate value is usually determined by the position of the dominant wave, i.e. the number of dominant waves per minute, i.e. the pulse rate per minute.
In a preferred embodiment, in the time domain method extraction center, the PP of the time domain may be one of a mobile phone, a tablet computer, or a computer of the user, and a human-computer interaction module is fixedly installed outside the receiving terminal, and the human-computer interaction module includes an operation mouse, a writing keyboard, and a writing pad.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the invention, the lifting wavelet module and the heart rate extraction module are arranged in the BCG acquisition module, so that the data acquired by the vibration sensor and the BCG acquisition module can be precisely analyzed and processed, the stability of the data is improved, meanwhile, the heart rate extraction module adopts the BCG mode to acquire the heart rate, a specific number of sensors are placed at a specific position of the mattress, the number and the position of the sensors have specificity, the acquisition precision is ensured to be highest, and the anti-interference capability is strongest; and a specific breath extraction algorithm ensures that users with different weights and sexes have reliable detection precision.
2. According to the invention, the buzzer alarm is matched with the voltage detection module to detect the power supply voltage in the power supply module, when the voltage in the power supply module is not stable enough, the voltage detection module detects that the voltage is not stable enough, and then the buzzer alarm can be sent out through the buzzer alarm to remind a worker to check the voltage in time, so that the safe operation of the system is ensured, and the stability of the system is improved.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
fig. 2 is a block diagram of the BCG acquisition module system in the present invention.
The labels in the figure are: the system comprises a processor module, a 2-control module, a 3-data recording module, a 4-data analysis module, a 5-data early warning module, a 6-receiving terminal, a 7-BCG acquisition module, an 8-power supply module, a 9-vibration sensor, a 10-voltage detection module, an 11-buzzer alarm, a 12-display terminal, a 13-data output module, a 14-heart rate extraction module, a 15-circuit amplification module and a 16-lifting wavelet module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1-2, a non-contact heart rate monitoring method and system based on BCG comprises a processor module 1, a control module 2, a data recording module 3, a data analysis module 4, a data early warning module 5, a receiving terminal 6, a BCG acquisition module 7, a power supply module 8, a vibration sensor 9, a voltage detection module 10, a buzzer alarm 11, a display terminal 12, a data output module 13, a heart rate extraction module 14, a circuit amplification module 15, and a lifting wavelet module 16, wherein the power supply module 8 is fixedly installed outside the processor module 1, the voltage detection module 10 is installed inside the power supply module 8, the circuit amplification module 15, the lifting wavelet module 16, the heart rate extraction module 14, and the data output module 13 are fixedly installed inside the voltage detection module 10, an output end of the circuit amplification module 15 is connected with an input end of the voltage detection module 10, the output end of the circuit amplification module 15 is connected with the input end of the lifting wavelet module 16, db2 discrete wavelet transform is realized by the internal lifting mode of the lifting wavelet module 16, the main steps of the lifting wavelet transform comprise splitting prediction and updating, wherein the splitting is to divide an original signal sequence into an even sequence and an odd sequence, the prediction is mainly to predict an odd sequence even sequence through the operation of the even sequence odd sequence, and the updating is to update the even sequence odd sequence through the operation so that the even sequence odd sequence is consistent with the original signal, thereby facilitating better prediction. Each step of lifting wavelet transformation has an inverse process, and the reconstruction process is the inverse process of the decomposition process and comprises three steps of inverse updating, inverse prediction and combination; the output end of the lifting wavelet module 16 is connected with the input end of the heart rate extraction module 14, the heart rate extraction module 14 extracts the heart rate by a time domain method, the heart rate in the time domain is detected, a pulse rate value is obtained by mainly utilizing the periodic characteristics of a PPG signal, and the pulse rate value is equivalent to the heart rate value. The PPG signal main wave of the time domain is obvious, the pulse rate value is usually determined by using the position of the main wave, the number of the main wave per minute is the pulse rate number per minute, in the heart is extracted by the time domain method, the PP number of the time domain is often influenced by motion artifacts, part of the main wave characteristics can be lost, and the condition that the tide wave is higher than the main wave can also occur, the interference section of the signal is matched by a symbolization method based on an improved dynamic difference threshold method, and the main wave position of the pulse signal is detected by an adaptive threshold value; the output end of the processor module 1 is connected with the input end of the data output module 13; the exterior of the voltage detection module 10 is electrically connected with a buzzer alarm 11; the buzzer alarm is matched with the voltage detection module to detect the power supply voltage in the power supply module, when the voltage in the power supply module is not stable enough, the voltage detection module detects that the voltage is not stable enough, and then the buzzer alarm can send out a buzzer alarm sound to remind a worker to check in time, so that the safe operation of the system is ensured, and the stability of the system is improved; the output end of the processor module 1 is connected with the input end of the control module 2, the BCG acquisition module 7 is fixedly installed outside the control module 2, the output end of the control module 2 is connected with the input end of the BCG acquisition module 7, the output end of the BCG acquisition module 7 is connected with the input end of the control module 2, the input end of the BCG acquisition module 7 is fixedly installed with the vibration sensor 9, the output end of the processor module 1 is connected with the input end of the data recording module 3, the output end of the data recording module 3 is connected with the input end of the data analysis module 4, the output end of the data analysis module 4 is connected with the input end of the data early warning module 5, the output end of the data early warning module 5 is connected with the input end of the receiving terminal 6, and the receiving terminal 6 can be a, the receiving terminal 6 is fixedly provided with a human-computer interaction module outside, and the human-computer interaction module is provided with an operating mouse, a writing keyboard and a handwriting board; the output end of the receiving terminal 6 is connected with the input end of the display terminal 12; the lifting wavelet module and the heart rate extraction module are arranged in the BCG acquisition module, and can be used for precisely analyzing and processing data acquired by the vibration sensor and the BCG acquisition module, so that the stability of the data is improved; and a specific breath extraction algorithm ensures that users with different weights and sexes have reliable detection precision.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. The utility model provides a non-contact heart rate monitoring method and system based on BCG, including processor module (1), control module (2), data record module (3), data analysis module (4), data early warning module (5), receiving terminal (6), BCG collection module (7), power module (8), vibration sensor (9), voltage detection module (10), buzzer siren (11), display terminal (12), data output module (13), the heart rate draws module (14), circuit amplification module (15), promote wavelet module (16), its characterized in that: the output end of the processor module (1) is connected with the input end of the control module (2), the BCG acquisition module (7) is fixedly installed outside the control module (2), the output end of the control module (2) is connected with the input end of the BCG acquisition module (7), the output end of the BCG acquisition module (7) is connected with the input end of the control module (2), the input end of the BCG acquisition module (7) is fixedly installed with the vibration sensor (9), the output end of the processor module (1) is connected with the input end of the data recording module (3), the output end of the data recording module (3) is connected with the input end of the data analysis module (4), the output end of the data analysis module (4) is connected with the input end of the data early warning module (5), the output end of the data early warning module (5) is connected with the input end of the receiving terminal (6), the output end of the receiving terminal (6) is connected with the input end of the display terminal (12).
2. The BCG-based non-contact heart rate monitoring method and system of claim 1, wherein: the external fixed mounting of processor module (1) has power module (8), the inside of power module (8) is provided with voltage detection module (10), the outside electric connection of voltage detection module (10) has buzzer siren (11).
3. The BCG-based non-contact heart rate monitoring method and system of claim 1, wherein: the circuit amplification module (15), the lifting wavelet module (16), the heart rate extraction module (14) and the data output module (13) are fixedly installed in the voltage detection module (10).
4. The BCG-based non-contact heart rate monitoring method and system of claim 1, wherein: the output of circuit amplification module (15) is connected with the input of voltage detection module (10), the output of circuit amplification module (15) is connected with the input of promotion wavelet module (16), the output of promotion wavelet module (16) is connected with the input of heart rate extraction module (14), the output of processor module (1) is connected with the input of data output module (13).
5. The BCG-based non-contact heart rate monitoring method and system of claim 1, wherein: the discrete wavelet transform db2 is realized by the internal lifting mode of the lifting wavelet module (16), the main steps of the lifting wavelet transform comprise splitting prediction and updating, wherein the splitting is to divide an original signal sequence into an even sequence and an odd sequence, the prediction is mainly to predict an odd-therapy sequence even sequence through the operation of the even sequence odd sequence, and the updating is to update the even sequence odd sequence through the operation so that the even sequence odd sequence is consistent with the original signal, thereby facilitating better prediction; each step of the lifting wavelet transformation has an inverse process, and the reconstruction process is the inverse process of the decomposition process and comprises three steps of inverse updating, inverse prediction and combination.
6. The BCG-based non-contact heart rate monitoring method and system of claim 1, wherein: the heart rate extraction module (14) extracts heart rate by adopting a time domain method, detects the heart rate in the time domain, and mainly obtains a pulse rate value by using the periodic characteristics of a PPG signal, wherein the pulse rate value is equivalent to a heart rate value; the dominant wave of the PPG signal in the time domain is more obvious, and the pulse rate value is usually determined by the position of the dominant wave, i.e. the number of dominant waves per minute, i.e. the pulse rate per minute.
7. The BCG-based non-contact heart rate monitoring method and system of claim 6, wherein: in the heart, the time domain method extracts the PP (number is often influenced by motion artifacts, part of main wave characteristics can be lost, and the condition that the tide wave is higher than the main wave can also occur, and the main wave position of the pulse signal is detected by an improved dynamic differential threshold method through matching the interference section of the signal by a symbolization method and through a self-adaptive threshold.
8. The BCG-based non-contact heart rate monitoring method and system of claim 1, wherein: the receiving terminal (6) can be one of a mobile phone, a tablet personal computer or a computer of a user, a human-computer interaction module is fixedly installed outside the receiving terminal (6), and the human-computer interaction module is provided with an operation mouse, a writing keyboard and a handwriting board.
CN202110266567.3A 2021-03-10 2021-03-10 BCG-based non-contact heart rate monitoring method and system Pending CN113080918A (en)

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AU2021102014A AU2021102014A4 (en) 2021-03-10 2021-04-19 Bcg-based non-contact heart rate monitoring method and system

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116256016A (en) * 2022-11-30 2023-06-13 广东省建筑设计研究院有限公司 Whole process health monitoring system of building structure system

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202133763U (en) * 2011-06-30 2012-02-01 陈洪光 Power supply fault detection information system
CN203119443U (en) * 2013-01-22 2013-08-07 林国祯 Power supply monitoring device
CN104730942A (en) * 2013-12-19 2015-06-24 西安恒飞电子科技有限公司 Low-voltage power source circuit with buzzing prompt function
CN107041730A (en) * 2017-03-07 2017-08-15 杭州医电园科技有限公司 A kind of contactless sleep monitoring device and its monitoring method
CN107233086A (en) * 2016-03-28 2017-10-10 三星电子株式会社 The method and apparatus that heart rate and respiratory rate are estimated are carried out using low power sensor
CN107766845A (en) * 2017-11-20 2018-03-06 苏州蓝珀医疗科技股份有限公司 A kind of breathing and BCG method for extracting signal based on light shock sensor
CN207397295U (en) * 2017-11-20 2018-05-22 苏州蓝珀医疗科技股份有限公司 A kind of breathing and BCG signal extraction systems based on light shock sensor
CN109009128A (en) * 2018-08-09 2018-12-18 深圳市大耳马科技有限公司 Breathing pattern detection method, device, processing equipment and system
CN109893113A (en) * 2019-02-20 2019-06-18 成都乐享智家科技有限责任公司 Information processing method based on BCG and HRV technology
CN111419208A (en) * 2020-04-13 2020-07-17 河北工业大学 Acceleration sensor-based unbound real-time heart rate monitoring method and system
CN211381368U (en) * 2019-12-27 2020-09-01 电子科技大学 Intelligent monitoring system based on cardiac shock signal

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202133763U (en) * 2011-06-30 2012-02-01 陈洪光 Power supply fault detection information system
CN203119443U (en) * 2013-01-22 2013-08-07 林国祯 Power supply monitoring device
CN104730942A (en) * 2013-12-19 2015-06-24 西安恒飞电子科技有限公司 Low-voltage power source circuit with buzzing prompt function
CN107233086A (en) * 2016-03-28 2017-10-10 三星电子株式会社 The method and apparatus that heart rate and respiratory rate are estimated are carried out using low power sensor
CN107041730A (en) * 2017-03-07 2017-08-15 杭州医电园科技有限公司 A kind of contactless sleep monitoring device and its monitoring method
CN107766845A (en) * 2017-11-20 2018-03-06 苏州蓝珀医疗科技股份有限公司 A kind of breathing and BCG method for extracting signal based on light shock sensor
CN207397295U (en) * 2017-11-20 2018-05-22 苏州蓝珀医疗科技股份有限公司 A kind of breathing and BCG signal extraction systems based on light shock sensor
CN109009128A (en) * 2018-08-09 2018-12-18 深圳市大耳马科技有限公司 Breathing pattern detection method, device, processing equipment and system
CN109893113A (en) * 2019-02-20 2019-06-18 成都乐享智家科技有限责任公司 Information processing method based on BCG and HRV technology
CN211381368U (en) * 2019-12-27 2020-09-01 电子科技大学 Intelligent monitoring system based on cardiac shock signal
CN111419208A (en) * 2020-04-13 2020-07-17 河北工业大学 Acceleration sensor-based unbound real-time heart rate monitoring method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116256016A (en) * 2022-11-30 2023-06-13 广东省建筑设计研究院有限公司 Whole process health monitoring system of building structure system

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