CN104814727B - Non-contact biofeedback training method - Google Patents
Non-contact biofeedback training method Download PDFInfo
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- CN104814727B CN104814727B CN201510257152.4A CN201510257152A CN104814727B CN 104814727 B CN104814727 B CN 104814727B CN 201510257152 A CN201510257152 A CN 201510257152A CN 104814727 B CN104814727 B CN 104814727B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, 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
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
- A61B5/6898—Portable consumer electronic devices, e.g. music players, telephones, tablet computers
Abstract
The invention discloses a non-contact biofeedback training method, and relates to a biofeedback training method based on a non-contact wireless physiological sensor and for monitoring respiratory waves and heartbeat waves in real time. The method comprises the following steps: when a patient or a trainer uses the system, the biofeedback training software on a computer or a smart phone is firstly opened, and then the non-contact wireless physiological sensor is placed in front of or behind the patient or the trainer. Then, the training start button is clicked to start training based on biofeedback. The invention utilizes the wireless ultra-wideband pulse transmitting and receiving machine and combines the digital signal processing module to monitor physiological signals, including the change of respiratory signals and the change of heartbeat signals, and inputs the changes into a biofeedback training software program to form a set of finished feedback systems.
Description
Technical Field
The invention relates to a non-contact biofeedback training method, in particular to a biofeedback training method based on a non-contact wireless physiological sensor and monitoring respiratory waves and heartbeat waves in real time.
Background
Biofeedback, known as a psychophysiological mirror, is a physiological signal that a patient or trainer can use to monitor and control the human body. Through biofeedback, the patient or trainer can learn how to change some of their physiological patterns, or to generate some control over some physiological patterns. Through the biofeedback training of the system, the control power of the patient or the trainer is continuously improved, and the patient or the trainer can improve partial physiological functions of the human body. Biofeedback training has proven to be an effective treatment for many diseases such as asthma, hypertension, tension headaches, anxiety, attention deficit, etc.
The biofeedback training system usually uses sound, light and electricity, such as music, and pictures to stimulate the patient, so as to guide the patient to make certain physiological responses, such as changes of breathing, changes of heartbeat, and activities of brain electricity, especially changes of sympathetic nerves and parasympathetic nerves, then uses an electronic sensor to non-invasively detect physiological signals, further changes stimulation signals according to the changes of the physiological signals, or removes the stimulation signals, and the patient or a trainer autonomously controls certain responses of the patient or the trainer, thereby achieving the training purpose of self-regulating and controlling the responses of the physiological signals of the patient.
The traditional method needs to use the traditional respiration detection instrument and the electrocardiograph which are both contact type, and has a plurality of influences and interferences on the physiology and nervous system of the human body, so the use is inconvenient.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a non-contact biofeedback training method, which utilizes a wireless ultra-wideband pulse transmitting and receiving machine and combines a digital signal processing module to monitor physiological signals, including the change of respiration signals and the change of heartbeat signals, and the changes are input into a biofeedback training software program to form a set of finished feedback systems.
In order to achieve the purpose, the invention is realized by the following technical scheme: the utility model provides a biofeedback training system of non-contact, is including installing the biofeedback procedure software on the computer or smart mobile phone to and can gather the wireless physiological sensor of non-contact that respiratory signal changes and heart rate signal change in real time, wireless physiological sensor adopt bluetooth or wireless local area network wifi to be connected with the computer or the smart mobile phone that install biofeedback system.
The non-contact biofeedback training method comprises the following steps: when a patient or a trainer uses the system, the biofeedback training software on a computer or a smart phone is firstly opened, and then the non-contact wireless physiological sensor is placed in front of or behind the patient or the trainer. Then, the training start button is clicked to start training based on biofeedback.
The biofeedback training program software comprises a prompting module, a feedback module, a course management module and a multimedia player, wherein the prompting module mainly realizes a breathing metronome.
The breathing metronome mainly displays a dynamically changing breathing prompter on a user interface of biofeedback computer software or a biofeedback smart phone APP, a rolling ball rolls along a parabola, inspiration is represented in a rolling ascending period, expiration is represented in a descending period, and therefore a patient or a trainer is prompted to exhale and inhale according to the prompting of the breathing metronome.
The multimedia player can play preset wonderful music or beautiful pictures, and the preset wonderful music or beautiful pictures are alternately played to prompt a patient or a trainer to adjust the breathing and the mood of the patient or the trainer according to the prompt.
The rolling or playing frequency and rhythm of the breathing metronome and the multimedia player can be adjusted in real time according to the physiological signal change sent by the feedback module from the non-contact wireless physiological sensor. Finally, the frequency is adjusted to the corresponding physiological resonance frequency according to the condition of the patient and is kept in the state for a period of time, thereby achieving the purposes of feedback and training.
The feedback module is a central control module for controlling the whole biofeedback training process and comprises a playing frequency of a breathing metronome in the generation prompting module, a rhythm and a frequency for playing multimedia files, a rolling frequency of the breathing metronome is adjusted according to the breathing signal change and the heartbeat signal change received from the wireless physiological sensor, and the playing frequency and the content of the multimedia files are adjusted. The feedback module comprises a user interface, and the score obtained by training of the user along with the change of time is displayed to the user in real time, if the score reaches the standard, the user is prompted to continue to keep with color, and if the score does not reach the standard, the user is prompted to not reach the standard with color. The user will more strictly follow the prompts of the prompt interface in adjusting breathing and mood. The wireless physiological sensor can detect the respiration waveform and the heartbeat waveform of a patient or a training person and transmit the respiration waveform and the heartbeat waveform to the feedback module by Bluetooth or wifi.
The calculation and tracking of the scores obtained by training are a self-feedback process, and the specific flow method comprises the following steps:
1. And (4) calculating HRV energy in real time, wherein the interval of adjacent peak points of the heartbeat waveform is consistent with the interval of an electrocardiogram RR wave. And calculating time domain indexes and frequency domain indexes of the HRV according to a calculation formula of the HRV. The time domain indexes mainly comprise statistical variables such as mean, total standard deviation, mean standard deviation and the like, and the frequency domain indexes mainly comprise total energy TF, very low frequency energy VLF, low frequency energy LF and high frequency energy HF.
HRV total power (TF): TF = VLF + LF + HF
2. And calculating the respiration rate according to the respiration waveform, and if the respiration rate of the patient is greater than the frequency of the respiration metronome, adjusting the frequency of the respiration metronome to be low, otherwise, adjusting the frequency to be high.
3. If the LF/TF ratio of HRV energy is increased after the suggested respiratory metronome frequency is adjusted to be low, the adjustment is continued until the LF/TF ratio is stable, and the respiratory frequency at the moment is the resonance frequency.
4. Breath at the resonance frequency (RF + -0.5 BPM) to improve (LF/TF) as much as possible, with higher LF/TF ratios and higher scores. The LF/TF ratio exceeding 35% is generally up to standard. This threshold may be increased to increase the difficulty of training.
The course management module may design a default threshold and a default breathing rate for the patient or trainer for the current training based on a profile recorded by the patient or trainer for the scores of previous training sessions.
The non-contact wireless physiological sensor consists of a wireless ultra-wideband pulse transmitting and receiving machine and a digital signal processing module, wherein the wireless ultra-wideband pulse transmitting and receiving machine is connected with the digital signal processing module. When an ultra-wideband narrow-pulse radio-frequency signal meets a static human body, different echo waves are generated by different human body parts for transmitting wireless signals along with time change, and different echo waves are generated on different time delays because the relative distances between different areas and transmitting and receiving antennas are different. The echo distance versus time delay can be represented by the following equation:
ΔL = Dt + Dr = cτ
Where τ is the time delay from the emission of a pulse to the receipt of a pulse, and c is the speed of light. The echoes of different time delays correspond to reflections from different parts of the body.
The wireless receiving circuit receives echo, the echo signal enters a fast time scanning type delay sampler controlled by 512 different delay devices through a low-noise amplifier with the gain of 25db, 512 data are obtained by scanning the sampler at a time point, and the 512 data respectively represent the emission intensity of different position points in the body. Under the control of a slow-time sampling clock, the echo intensities of the various positions are sampled over time. The acquired echo intensity is subjected to analog-to-digital conversion to obtain a two-dimensional digital sampling sequence with slow time and fast time, and the two-dimensional digital sampling sequence is input into the digital signal processing module. The respiratory wave and heartbeat wave of a static human body have the characteristic of periodic variation, and the respiratory wave and heartbeat wave are extracted by adopting weak signal filtering and wavelet restoration methods.
The invention has the beneficial effects that: a wireless ultra-wideband pulse transmitting and receiving device is utilized, a digital signal processing module is combined to monitor physiological signals including changes of respiration signals and changes of heartbeat signals, and the changes are input into a biofeedback training software program to form a set of finished feedback system. The system method does not need to use the traditional respiration detection instrument and the electrocardiograph, is non-contact, has little influence and interference on the human physiology and nervous system, is easy to popularize, and has many advantages which are not possessed by the traditional method.
Drawings
The invention is described in detail below with reference to the drawings and the detailed description;
FIG. 1 is a block diagram of the system of the present invention;
Fig. 2 is a block diagram of a contactless wireless physiological sensor according to the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 1-2, the following technical solutions are adopted in the present embodiment: the utility model provides a biofeedback training system of non-contact, is including installing biofeedback procedure software 1 on the computer or the smart mobile phone to and can gather the wireless physiological sensor 2 of non-contact that respiratory signal changes and heart rate signal change in real time, wireless physiological sensor adopt bluetooth or wireless local area network wifi to be connected with the computer or the smart mobile phone that install biofeedback system.
The non-contact biofeedback training method comprises the following steps: when a patient or a trainer uses the system, the biofeedback training software on a computer or a smart phone is firstly opened, and then the non-contact wireless physiological sensor is placed in front of or behind the patient or the trainer. Then, the training start button is clicked to start training based on biofeedback.
The extraction of the respiratory wave signal of the present embodiment: firstly, the system adopts a slow-time and fast-time average filtering method to remove various noise interferences in wireless signal propagation. The formula is as follows:
N and M are the smoothing time window lengths in the fast and slow times, respectively.
The amplitude and frequency of the respiratory wave signal have certain regular changes, so the respiratory wave signal is restored by adopting a wavelet decomposition and filtering method, and the formula of the wavelet change is
And T is an observation time window. Y (t) is an average of a plurality of values of Y (N, t) at M points above and below the fast time center point at which the echo amplitude is maximum. ω is the wavelet basis function. Because the respiratory wave signal is in the low-frequency area, the high-frequency wavelet component is filtered out, and then the wavelet inverse transformation is adopted, so that the waveform of the respiratory wave can be extracted.
The extraction of the heartbeat wave signal of the present embodiment: the beating of heart tissue and valves caused by the heartbeat is much smaller in amplitude than the breathing-induced thoracic fluctuations. The amplitude of the heartbeat wave is much weaker. In addition to removing noise interference and stray interference by adopting a fast time and slow time moving average method in the extraction of the respiratory wave signals, the influence of the respiratory wave signals on the heartbeat signals is also removed. The respiratory wave signals comprise slightly provided signals of the upper body and show periodic slow change, the respiratory and body micro-motion signals are restored from the collected echo signals by adopting a multi-parameter second-order sine-cosine curve method and then are subtracted by the original signals to remove, and the processing formula is as follows
Wherein the content of the first and second substances,
From a0 to a4 are the fitting parameters estimated from Y (N, t).
Because the amplitude of the heart beat wave is too small, a short-time correlation method is adopted to enhance the echo signal of the heart beat,
The processing formula is 
T is a time window of short-term correlation, typically 5 times the length of a heartbeat cycle.
And filtering out other components by using a wavelet filtering and restoring method to extract the heartbeat wave component. The wavelet filtering method is adopted because it is a method capable of performing multi-resolution filtering and decomposition simultaneously in the time domain, having a stronger spatio-temporal resolution. The wavelet basis function is the Morlet function of the adjustable parameter.
Where the beta factor is used to adjust the balance between time resolution and frequency resolution. The adjustment is made to achieve a frequency resolution of 0.1hz to accurately extract the components of the heart beat wave.
For Wf (s, b) of U (N, t) after wavelet transformation, attenuating 80% of component of respiratory wave in the range of 2-th to 4-th harmonic 6 of respiratory wave to obtain 
Then reconstructing by wavelet inverse transformation to recover heartbeat A wave signal.
This embodiment bluetooth and wifi transmission: the digital sequence of the respiratory wave and the heartbeat wave output from the digital signal processing module is digitally compressed and then transmitted to a biofeedback program on a computer or a smart phone through Bluetooth or wif, so as to be used for monitoring physiological indexes in real time, calculating HRV energy, respiratory rate change and the like and serve as algorithm input data of the biofeedback module.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. A non-contact biofeedback training system is characterized by comprising biofeedback program software installed on a computer or a smart phone and a non-contact wireless physiological sensor capable of collecting respiratory signal changes and heart rate signal changes in real time, wherein the wireless physiological sensor is connected with the computer or the smart phone provided with the biofeedback system by Bluetooth or a wireless local area network wifi;
The biofeedback training program software comprises a prompting module, a feedback module and a course management module, wherein the prompting module mainly realizes a breathing metronome and a multimedia player;
The breathing metronome mainly displays a dynamically-changing breathing prompter on a user interface on biofeedback computer software or a biofeedback smart phone APP, a rolling ball rolls along a parabola, inspiration is represented in a rolling ascending period, expiration is represented in a descending period, and therefore a patient or a trainer is prompted to exhale and inhale according to the prompt of the breathing metronome;
The multimedia player can play preset wonderful music or beautiful pictures, and the preset wonderful music or beautiful pictures are alternately played to prompt a patient or a trainer to adjust the breathing and the emotion of the patient or the trainer according to the prompt;
The rolling or playing frequency and rhythm of the breathing metronome and the multimedia player can be adjusted in real time according to the change of physiological signals sent by the feedback module from the non-contact wireless physiological sensor; finally, the frequency is adjusted to the corresponding physiological resonance frequency according to the condition of the patient and is kept for a period of time in the state, thereby achieving the purposes of feedback and training;
The feedback module is a central control module for controlling the whole biofeedback training process and comprises a playing frequency of a breathing metronome in the generation prompting module, a rhythm and a frequency for playing multimedia files, a rolling frequency of the breathing metronome is adjusted according to the breathing signal change and the heartbeat signal change received from the wireless physiological sensor, and the playing frequency and the content of the multimedia files are adjusted; the feedback module comprises a user interface, and displays the score obtained by training of the user along with the change of time to the user in real time, if the score reaches the standard, the user is prompted to continue to keep by using color, and if the score does not reach the standard, the user is prompted to not reach the standard by using color; the user can adjust the breathing and the emotion according to the prompt of the prompt interface more strictly; the wireless physiological sensor can detect the respiratory waveform and the heartbeat waveform of a patient or a training person and transmits the respiratory waveform and the heartbeat waveform to the feedback module by Bluetooth or wifi;
The course management module can design a default threshold value and a default respiratory frequency of the patient or the trainer in the training according to a curve recorded by the scores of the patient or the trainer in multiple previous training;
The non-contact wireless physiological sensor consists of a wireless ultra-wideband pulse transmitting and receiving machine and a digital signal processing module, wherein the wireless ultra-wideband pulse transmitting and receiving machine is connected with the digital signal processing module; when an ultra-wideband narrow-pulse radio-frequency signal meets a static human body, different echo waves are generated by different human body parts for transmitting wireless signals along with time change, a wireless receiving circuit receives the echo waves, the echo wave signals enter a fast time scanning type time delay sampler controlled by 512 different time delay devices through low-noise amplification with the gain of 25db, 512 data are obtained by scanning the sampler at one time point, and the 512 data respectively represent the transmitting intensity of different position points in the human body; under the control of a slow time sampling clock, the echo intensity of each position which changes along with the time is sampled; the acquired echo intensity is subjected to analog-to-digital conversion to obtain a two-dimensional digital sampling sequence with slow time and fast time, and the two-dimensional digital sampling sequence is input into a digital signal processing module; the respiratory wave waveform and the heartbeat wave waveform of a static human body have the characteristic of periodic variation, and the respiratory wave and the heartbeat wave are extracted by adopting a weak signal filtering and wavelet restoration method;
The method for extracting the respiratory wave signals comprises the following steps: removing various noise interferences in wireless signal transmission by adopting a slow time and fast time average filtering method, performing wavelet decomposition on the denoised signals, filtering high-frequency wavelet components because the respiratory wave signals are in a low-frequency region, and further extracting the waveform of the respiratory wave through wavelet inverse transformation;
The method for extracting the heartbeat wave signal comprises the following steps: aiming at the condition that the amplitude of the fluctuation of heart tissues and valves caused by heartbeat is far smaller than the amplitude of the fluctuation of a chest caused by respiration, a fast time and slow time averaging method is used for removing noise interference, a multi-parameter second-order sine-cosine curve method is used for restoring respiratory and body micro signals, the restored respiratory and body micro signals are subtracted from the denoised signals, a short-time correlation method is used for enhancing the signals after the noise interference caused by respiration is removed, in order to filter other interference signals, a Morlet function with wavelet basis functions as adjustable parameters is used for carrying out wavelet transformation on the enhanced signals, the components of the signals obtained after the wavelet transformation in a range from 2 to 4 harmonic waves are attenuated by 80%, and the heartbeat wave signals are restored through wavelet inverse transformation.
2. The system of claim 1, wherein the score of the training is calculated and tracked by a process including self-feedback, and the specific process comprises:
(1) Calculating HRV energy in real time, wherein the interval of adjacent peak points of the heartbeat waveform is consistent with the interval of an electrocardiogram RR wave; calculating various time domain indexes and frequency domain indexes of the HRV according to a calculation formula of the HRV (heart rate variability) according to a digital sequence generated at the RR wave interval; the time domain indexes mainly comprise statistical variables such as mean, total standard deviation, mean standard deviation and the like, the frequency domain indexes mainly comprise total energy TF, very low frequency energy VLF, low frequency energy LF and high frequency energy HF,
HRV total power TF: TF = VLF + LF + HF;
(2) Calculating the respiratory rate according to the respiratory waveform, if the respiratory rate of the patient is greater than the frequency of the respiratory metronome, adjusting the frequency of the respiratory metronome to be low, and if not, adjusting the frequency to be high;
(3) If the LF/TF ratio of HRV energy is increased after the prompted breathing metronome frequency is reduced, the reduction is continued until the LF/TF ratio is stable, and the breathing frequency is the resonance frequency;
(4) Breathing at the resonance frequency RF +/-0.5 BPM, and improving the LF/TF as much as possible, wherein the higher the LF/TF ratio is, the higher the score is; the LF/TF ratio exceeding 35 percent reaches the standard; this threshold may be increased to increase the difficulty of training.
3. A non-contact biofeedback training system as recited in claim 1, wherein said step of using said non-contact biofeedback training system comprises: when a patient or a trainer uses the system, firstly, biofeedback training software on a computer or a smart phone is started, and then the non-contact wireless physiological sensor is placed in front of or behind the patient or the trainer; then, the training start button is clicked to start the biofeedback training.
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| CN104873173A (en) * | 2015-05-19 | 2015-09-02 | 上海兆观信息科技有限公司 | Non-contact type sleep stage classification and sleep breathing disorder detection method |
| CN105797257A (en) * | 2016-05-06 | 2016-07-27 | 包磊 | Biofeedback training system and method and intelligent terminal |
| CN109528214A (en) * | 2018-11-07 | 2019-03-29 | 深圳市新元素医疗技术开发有限公司 | A kind of Multifunctional wrist BOLD contrast |
| CN111568423B (en) * | 2019-02-15 | 2023-03-03 | 南京昇翠人工智能有限公司 | Method and device for measuring resonance frequency estimation value of user breath |
| CN112827136A (en) * | 2019-11-25 | 2021-05-25 | 京东方科技集团股份有限公司 | Respiration training method and device, electronic equipment, training system and storage medium |
| CN113017600B (en) * | 2021-04-20 | 2023-08-18 | 浙江大学医学院附属邵逸夫医院 | Training evaluation system and method for medical image examination |
| CN113273978B (en) * | 2021-05-21 | 2022-04-29 | 电子科技大学 | Ultra-wideband radar-based human body respiration and heartbeat frequency detection method |
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