AU2021102014A4 - Bcg-based non-contact heart rate monitoring method and system - Google Patents

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

Info

Publication number
AU2021102014A4
AU2021102014A4 AU2021102014A AU2021102014A AU2021102014A4 AU 2021102014 A4 AU2021102014 A4 AU 2021102014A4 AU 2021102014 A AU2021102014 A AU 2021102014A AU 2021102014 A AU2021102014 A AU 2021102014A AU 2021102014 A4 AU2021102014 A4 AU 2021102014A4
Authority
AU
Australia
Prior art keywords
module
heart rate
bcg
input end
output end
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
AU2021102014A
Inventor
Shihua CAO
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Aoxin Technology Co Ltd
Original Assignee
Hangzhou Aoxin Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Aoxin Technology Co Ltd filed Critical Hangzhou Aoxin Technology Co Ltd
Application granted granted Critical
Publication of AU2021102014A4 publication Critical patent/AU2021102014A4/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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 present invention discloses a BCG-based non-contact heart rate monitoring method and system. In the present invention, a BCG acquisition module is fixedly installed outside a control module; an output end of the control module is connected with an 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; a vibration sensor is fixedly installed at the input end of the BCG acquisition module; the output end of a processor module is connected with the input end of a data recording module; the output end of a data analysis module is connected with the input end of a data early-warning module; the output end of the data early-warning module is connected with the input end of a receiving terminal; and the output end of the receiving terminal is connected with the input end of a display terminal. A lifting wavelet module and a heart rate extraction module are arranged inside the BCG acquisition module to precisely analyze and process data acquired by the vibration sensor and the BCG acquisition module, thereby improving the stability of the data. Drawings of Description 9 ration Sensor CG acquisitiomnl mod 2 11 1Q 8 Control module o e tecton P esuplyProcessor module BuzzeralarmData recording module Data analysis module Data earl-wvarning 6 Receiv-ing terminal 12 Display teinafil Fig. 1 1

Description

Drawings of Description
9 ration Sensor
CG acquisitiomnl mod
2
8 Control module 1Q
o e tecton P esuplyProcessor module
BuzzeralarmData recording module
Data analysis module
Data earl-wvarning
6 Receiv-ing terminal
11 12 Display teinafil
Fig. 1
Description
BCG-BASED NON-CONTACT HEART RATE MONITORING METHOD AND SYSTEM
Technical Field
The present 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 sensors, electrodes, wires, battery life and other factors, thereby seriously affecting experience and comfort of users and causing great difficulties in practical application. The non-contact vital sign monitoring technology has no influence on users and collects vital signs without feeling, thereby becoming a promising vital sign monitoring technology. For example, the heart rate monitoring is time-consuming, laborious and error-prone because the heart rate is mainly judged by manually observing the rise and fall of chest at present. Alternatively, the wearable thermal-resistance lead heart rate monitoring or abdominal pressure band heart rate monitoring, with high accuracy, can be adopted. However, relatively simple heart rate extraction and calculation methods are adopted in a common heart rate method during detection, causing insufficient accuracy. Meanwhile, in a common system, the system is unable to detect when the supply voltage of the system is unstable, causing insufficient service life of the system.
Summary
Description
A purpose of the present invention is to provide a BCG-based non-contact heart rate monitoring method and system to solve the above problems. Technical solutions are adopted in the present invention as follows: a BCG-based non-contact heart rate monitoring method and system 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 an output end of the processor module is connected with an input end of the control module; the BCG acquisition module is fixedly installed outside the control module; an 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; the output end of the processor module is connected with the input end of the data recording module; 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 installed outside the processor module; the voltage detection module is arranged inside the power supply module; and the buzzer alarm is electrically connected outside 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.
Description
In a preferred embodiment, the output end of the circuit amplification module is connected with the input end of the voltage detection module and the input end of the lifting wavelet module; the output end of the lifting wavelet module is connected with the input end of the heart rate extraction module; and the output end of the processor module is connected with the input end of the data output module. In a preferred embodiment, db2 discrete wavelet transform is realized by an internal lifting way of the lifting wavelet module. The lifting wavelet transform mainly comprises steps of split, prediction and update, wherein the split is to catch and split an original signal sequence into an even sequence and an odd sequence; the prediction is to mainly predict an odd-ordered even sequence by an even-ordered odd sequence through operation; and the update is to update the even-ordered odd sequence through operation to keep the sequence consistent with the original signal for better prediction. Each step of the lifting wavelet transform comprises an inverse process; and a reconstruction process is the inverse process of a decomposition process, comprising three steps of undo update, undo prediction and merging. In a preferred embodiment, the heart rate is extracted by a time domain method inside the heart rate extraction module; the heart rate in the time domain is detected in such a manner that a pulse rate, which is equivalent to the heart rate, is mainly obtained by utilizing periodic characteristics of PPG signals. Main waves of the PPG signals in the time domain are relatively apparent; the pulse rate is usually determined by positions of the main waves; and the number of main waves per minute is the number of pulse rates per minute. In a preferred embodiment, the PP in the time domain may be one of a mobile phone, a tablet computer and a computer of a user during extraction of the heart rate with the time domain method; a man-machine interaction module is fixedly installed outside the receiving terminal; and the man-machine interaction module is provided with an operating mouse, a writing keyboard and a handwriting board.
Description
In conclusion, due to the adoption of the above technical solutions, the present invention has the beneficial effects as follows. 1. In the present invention, the lifting wavelet module and the heart rate extraction module are arranged inside the BCG acquisition module, to precisely analyze and process the data acquired by the vibration sensor and the BCG acquisition module, thereby improving the stability of the data. Meanwhile, the heart rate is collected by a BCG way inside the heart rate extraction module; a certain number of sensors are placed at specific positions of a mattress, so that the number and the positions of the sensors are specific, to ensure the highest acquisition precision and the strongest anti-interference capability. A specific breath extraction algorithm is adopted to ensure the reliable detection precision for users of different weights and genders. 2. In the present invention, the buzzer alarm can cooperate with the voltage detection module to detect the supply voltage inside the power supply module. When the voltage inside the power supply module is not stable enough, the voltage detection module detects that the voltage is not stable enough; and the buzzer alarm can sound at this moment to remind workers to check in time, thereby ensuring the safe operation of the system and improving the stability of the system.
Description of Drawings
Fig. 1 is a system block diagram of the present invention; and Fig. 2 is a system block diagram of a BCG acquisition module of the present invention. Reference numerals in the figures: 1-processor module, 2-control module, 3-data recording module, 4-data analysis module, 5-data early-warning module, 6-receiving terminal, 7-BCG acquisition module, 8-power supply module, 9-vibration sensor, 10-voltage detection module, 11-buzzer alarm, 12-display terminal, 13-data output module, 14-heart rate extraction module, 15-circuit amplification module and 16-lifting wavelet module.
Description
Detailed Description
To make purposes, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are used for only illustrating the present invention, rather than limiting the present invention. Referring to Figs. 1-2, a BCG-based non-contact heart rate monitoring method and system comprise 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. 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 and the input end of the lifting wavelet module 16. The db2 discrete wavelet transform is realized by an internal lifting way of the lifting wavelet module 16. The lifting wavelet transform mainly comprises steps of split, prediction and update. The split is to catch and split an original signal sequence into an even sequence and an odd sequence; the prediction is to mainly predict an odd-ordered even sequence by an even-ordered odd sequence through operation; and the update is to update the even-ordered odd sequence through operation to keep the sequence consistent with the original signal for better prediction. Each step of the lifting wavelet transform comprises an
Description
inverse process; and a reconstruction process is the inverse process of a decomposition process, comprising three steps of undo update, undo prediction and merging. 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 is extracted by a time domain method inside the heart rate extraction module 14. The heart rate in the time domain is detected in such a manner that a pulse rate, which is equivalent to the heart rate, is mainly obtained by utilizing periodic characteristics of PPG signals. Main waves of the PPG signals in the time domain are relatively apparent; the pulse rate is usually determined by positions of the main waves; and the number of main waves per minute is the number of pulse rates per minute. During extraction of the heart rate in the time domain, PP number of the time domain is often affected by motion artifacts, causing the loss of some main wave characteristics, and also causing a situation that tidal waves are higher than main waves. Interference bands of the signals are matched by a symbolic method based on an improved dynamic difference threshold method; and the positions of the main waves of the pulse signals are detected by adaptive thresholds. The output end of the processor module 1 is connected with the input end of the data output module 13. The buzzer alarm 11 is electrically connected outside the voltage detection module 10. The buzzer alarm can cooperate with the voltage detection module to detect the supply voltage inside the power supply module. When the voltage inside the power supply module is not stable enough, the voltage detection module detects that the voltage is not stable enough; and the buzzer alarm can sound at this moment to remind workers to check in time, thereby ensuring the safe operation of the system and improving the stability of the system. 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
Description
connected with the input end of the control module 2. The vibration sensor 9 is fixedly installed at the input end of the BCG acquisition module 7. 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 receiving terminal 6 may be one of a mobile phone, a tablet computer and a computer of a user. A man-machine interaction module is fixedly installed outside the receiving terminal 6; and the man-machine 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 inside the BCG acquisition module, to precisely analyze and process the data collected by the vibration sensor and the BCG acquisition module, thereby improving the stability of the data. Meanwhile, the heart rate is collected by a BCG way inside the heart rate extraction module. A certain number of sensors are placed at specific positions of a mattress, so that the number and the positions of the sensors are specific, to ensure the highest acquisition precision and the strongest anti-interference capability. A specific breath extraction algorithm is adopted to ensure the reliable detection precision for users of different weights and genders. It should be noted that the relational terms herein such as first and second are used for only distinguishing one entity or operation from another entity or operation, rather than necessarily requiring or implying any such actual relationship or order of these entities or operations. In addition, the terms "comprise", "include" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, a method, an article or equipment
Description
comprising a series of elements comprises not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or equipment. Without further limitation, the elements defined by the sentence "comprising a/an ... " do not exclude the presence of other identical elements in the process, the method, the article or the equipment comprising the above elements. The above embodiments are only used for illustrating the technical solutions of the present invention, rather than limiting the present invention. Although the present invention is described in detail with reference to the foregoing embodiments, those ordinary skilled in the art shall understand that the technical solutions described in the foregoing embodiments can still be modified, or some technical features in the embodiments can be substituted equivalently. However, these modifications or substitutions will not make the essence of the corresponding technical solutions deviated from the spirit and scope of the technical solution of each embodiment in the present invention.

Claims (8)

Claims
1. A BCG-based non-contact heart rate monitoring method and system, comprising 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 an output end of the processor module (1) is connected with an input end of the control module (2); the BCG acquisition module (7) is fixedly installed outside the control module (2); an 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 vibration sensor (9) is fixedly installed at the input end of the BCG acquisition module (7); 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 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 according to claim 1, wherein the power supply module (8) is fixedly installed outside the processor module (1); the voltage detection module (10) is arranged inside the power supply module (8); and the buzzer alarm (11) is electrically connected outside the voltage detection module (10).
3. The BCG-based non-contact heart rate monitoring method and system according to 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 inside the voltage detection module (10).
Claims
4. The BCG-based non-contact heart rate monitoring method and system according to claim 1, wherein the output end of the circuit amplification module (15) is connected with the input end of the voltage detection module (10) and the input end of the lifting wavelet module (16); the output end of the lifting wavelet module (16) is connected with the input end of the heart rate extraction module (14); and the output end of the processor module (1) is connected with the input end of the data output module (13).
5. The BCG-based non-contact heart rate monitoring method and system according to claim 1, wherein db2 discrete wavelet transform is realized by an internal lifting way of the lifting wavelet module (16); the lifting wavelet transform mainly comprises steps of split, prediction and update; the split is to catch and split an original signal sequence into an even sequence and an odd sequence; the prediction is to mainly predict an odd-ordered even sequence by an even-ordered odd sequence through operation; the update is to update the even-ordered odd sequence through operation to keep the sequence consistent with the original signal for better prediction; each step of the lifting wavelet transform comprises an inverse process; and a reconstruction process is the inverse process of a decomposition process, comprising three steps of undo update, undo predict and merging.
6. The BCG-based non-contact heart rate monitoring method and system according to claim 1, wherein the heart rate is extracted by a time domain method inside the heart rate extraction module (14); the heart rate in the time domain is detected in such a manner that a pulse rate, which is equivalent to the heart rate, is mainly obtained by utilizing periodic characteristics of PPG signals; main waves of the PPG signals in the time domain are relatively apparent; the pulse rate is usually determined by positions of the main waves; and the number of main waves per minute is the number of pulse rates per minute.
7. The BCG-based non-contact heart rate monitoring method and system according to claim 6, wherein during extraction of the heart rate in the time domain, PP number of the time domain is often affected by motion artifacts, causing the
Claims
loss of some main wave characteristics, and also causing a situation that tidal waves are higher than main waves; interference bands of the signals are matched by a symbolic method based on an improved dynamic difference threshold method; and the positions of the main waves of the pulse signals are detected by adaptive thresholds.
8. The BCG-based non-contact heart rate monitoring method and system according to claim 1, wherein the receiving terminal (6) may be one of a mobile phone, a tablet computer and a computer of a user; a man-machine interaction module is fixedly installed outside the receiving terminal (6); and the man-machine interaction module is provided with an operating mouse, a writing keyboard and a handwriting board.
AU2021102014A 2021-03-10 2021-04-19 Bcg-based non-contact heart rate monitoring method and system Active AU2021102014A4 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110266567.3 2021-03-10
CN202110266567.3A CN113080918A (en) 2021-03-10 2021-03-10 BCG-based non-contact heart rate monitoring method and system

Publications (1)

Publication Number Publication Date
AU2021102014A4 true AU2021102014A4 (en) 2021-06-03

Family

ID=76132948

Family Applications (1)

Application Number Title Priority Date Filing Date
AU2021102014A Active AU2021102014A4 (en) 2021-03-10 2021-04-19 Bcg-based non-contact heart rate monitoring method and system

Country Status (2)

Country Link
CN (1) CN113080918A (en)
AU (1) AU2021102014A4 (en)

Families Citing this family (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

Family Cites Families (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
US10722182B2 (en) * 2016-03-28 2020-07-28 Samsung Electronics Co., Ltd. Method and apparatus for heart rate and respiration rate estimation 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
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
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
CN109009128B (en) * 2018-08-09 2020-11-27 深圳市大耳马科技有限公司 Breathing mode 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

Also Published As

Publication number Publication date
CN113080918A (en) 2021-07-09

Similar Documents

Publication Publication Date Title
CN110133610B (en) Ultra-wideband radar action identification method based on time-varying distance-Doppler diagram
US10265029B2 (en) Methods and systems for calculating and using statistical models to predict medical events
Khalifa et al. Pervasive self-powered human activity recognition without the accelerometer
CN106886675B (en) Method and system for monitoring continuous biomedical signals
AU2021102014A4 (en) Bcg-based non-contact heart rate monitoring method and system
CN101194261A (en) Morphograms in different time scales for robust trend analysis in intensive/critical care unit patients
KR102141185B1 (en) A system of detecting epileptic seizure waveform based on coefficient in multi-frequency bands from electroencephalogram signals, using feature extraction method with probabilistic model and machine learning
US20220022798A1 (en) Waveform Analysis And Detection Using Machine Learning Transformer Models
CN109443719B (en) Drill bit vibration signal online virtual test method and system thereof
EP3692898A1 (en) Sleep/motion determination based on wi-fi signals
US20180177415A1 (en) Cardiovascular disease detection
Moshiri et al. Using GAN to enhance the accuracy of indoor human activity recognition
Wang et al. Synchrophasor data compression under disturbance conditions via cross-entropy-based singular value decomposition
CN113114400A (en) Signal frequency spectrum hole sensing method based on time sequence attention mechanism and LSTM model
CN111557661B (en) Electrocardiosignal processing method and device
Sharma et al. QRS complex detection in ECG signals using the synchrosqueezed wavelet transform
CN111667843A (en) Voice wake-up method and system for terminal equipment, electronic equipment and storage medium
Zhang et al. CSI-based location-independent human activity recognition using feature fusion
CN115474901A (en) Non-contact living state monitoring method and system based on wireless radio frequency signals
CN113762355B (en) User abnormal electricity behavior detection method based on non-invasive load decomposition
CN112380903B (en) Human body activity recognition method based on WiFi-CSI signal enhancement
CN111714100A (en) Dual-sensing vital sign monitoring system and method
Pillai et al. Abnormality detection and energy conservation in wireless body area networks using hidden markov models: a review
US20220000415A1 (en) Epileptic seizure predicting device, method for analyzing electrocardiographic index data, seizure predicting computer program, model constructing device, model constructing method, and model constructing computer program
CN112790774A (en) Original electroencephalogram deep learning classification method and application

Legal Events

Date Code Title Description
FGI Letters patent sealed or granted (innovation patent)