CN109330583A - Based on the detection method for crossing oxygen consumption after ECG signal sampling movement - Google Patents
Based on the detection method for crossing oxygen consumption after ECG signal sampling movement Download PDFInfo
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- 230000036284 oxygen consumption Effects 0.000 title claims abstract description 8
- 238000001514 detection method Methods 0.000 title abstract description 4
- 230000033001 locomotion Effects 0.000 title abstract description 4
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- 239000000835 fiber Substances 0.000 claims abstract description 6
- VZCCETWTMQHEPK-QNEBEIHSSA-N gamma-linolenic acid Chemical compound CCCCC\C=C/C\C=C/C\C=C/CCCCC(O)=O VZCCETWTMQHEPK-QNEBEIHSSA-N 0.000 claims abstract 7
- 238000000034 method Methods 0.000 claims description 13
- 150000002978 peroxides Chemical class 0.000 claims description 6
- 125000004122 cyclic group Chemical group 0.000 claims description 5
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 125000000864 peroxy group Chemical group O(O*)* 0.000 claims description 2
- 238000005259 measurement Methods 0.000 abstract description 4
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- 238000012549 training Methods 0.000 description 9
- 230000000694 effects Effects 0.000 description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 4
- 239000004744 fabric Substances 0.000 description 4
- 229910052760 oxygen Inorganic materials 0.000 description 4
- 239000001301 oxygen Substances 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- JVTAAEKCZFNVCJ-UHFFFAOYSA-M Lactate Chemical compound CC(O)C([O-])=O JVTAAEKCZFNVCJ-UHFFFAOYSA-M 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
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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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
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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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/04—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/75—Measuring physiological parameters of the user calorie expenditure
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Abstract
The invention discloses the detection methods that oxygen consumption is crossed after a kind of movement based on ECG signal sampling, include the following steps: to acquire human ecg signal by pro-skin conductive fiber signal acquisition unit；It is electrocardiogram (ECG) data by above-mentioned ECG's data compression, obtains heart rate number HR；Zmount of oxygen consumption VO is calculated according to HR2；Oxygen consumption EPOC was measured using the supply and demand differential equation.The invention has the following beneficial effects: measurement facilitate it is accurate.
Description
Technical Field
The invention relates to a method for detecting overconsumption after exercise based on electrocardiosignals.
Background
In the field of exercise and fitness measurement, assessment and tracking of post-exercise peroxygen consumption (EPOC) can provide users and their coaches with important data regarding cardiopulmonary function, personal training performance, and optimal references to training recovery programs. EPOC refers to the measurable increase in post-exercise oxygen uptake rate, often greater than or equal to the maximum oxygen uptake (VO) of an individual after exercise2max) of 50 to 60%. The main reason for the generation of EPOC is that exercise significantly impairs the rate of oxygen uptake in vivo andthe balance of oxygen consumption rates, and therefore the need to increase the post-exercise oxygen uptake rate to restore the body from a new to resting equilibrium state. As a result of the existing research, EPOC is found to be related to four factors, namely exercise intensity, duration, exercise type and personal physical fitness level. Prior studies have shown that EPOC is linear with motion intensity and motion duration. Furthermore, untrained people have slower EPOC recovery times relative to people who are often trained. During strenuous exercise, when the body cannot maintain the current exercise intensity by means of the energy it generates from the aerobic reaction, the energy from the anaerobic reaction provides additional energy to the body, and also produces lactate as a byproduct. Because the process of removing lactic acid occurs during EPOC, EPOC is correlated with the lactate profile.
At present, wearable equipment based on signal monitoring such as heart rate, speed, myoelectricity provides good monitoring and guiding effects for daily life and physical training of people, but with the increasing development of wearable equipment and the pursuit of people for improving the quality of life, people not only can meet the monitoring of physiological data such as heart rate, electrocardio, myoelectricity and the like, but also can have strong requirements for advanced physiological indexes calculated based on the primary physiological indexes, such as EPOC (post-exercise peroxygen consumption) and the like, so that a reasonable exercise plan more suitable for people can be made. Extensive research has been carried out based on primary physiological indicators of the cardiac electrical signal and advanced physiological indicators derived therefrom.
Disclosure of Invention
The invention provides a method for detecting overconsumption after exercise based on electrocardiosignals, which comprises the following steps:
collecting electrocardiosignals of a human body through a signal skin-friendly conductive fiber collecting unit;
processing the electrocardiosignals into electrocardio data to obtain heart rate times HR;
calculating oxygen consumption from HRVO2；
According to HR (Reserve), i.e., the difference between HR (max) and HR (rest), and VO2(reserve), i.e. VO2(max) and VO2(rest) difference, calculate VO2(max) percent;
EPOC is determined for peroxide consumption using supply and demand differential equations.
Preferably, the first and second electrodes are formed of a metal,
the supply and demand differential equation is:
EPOC(t+1)=EPOC(t)+a*eb*V-c*ed*(1-V)*EPOC(t)
wherein,
v is VO2(max) percent;
EPOC (0) =0 (initialization);
a, b, c and d are empirical parameters; dynamic correction is carried out by adopting a deep learning algorithm based on a cyclic neural network; setting initial values of a prior model of a, b, c and d according to empirical parameters;
e is a natural logarithm base number;
t is time.
EPOC can be calculated recursively according to the following equation,
dEPOC /dt=a*eb*V-c*ed*(1-V)*EPOC
initial value, EPOC (0) = 0;
EPOC（t+1）=EPOC（t）+a*eb*V-c*ed*(1-V)*EPOC(t)
the conventional device for determining the EPOC needs more auxiliary equipment, is not suitable for wearable equipment, is difficult for a user to carry a whole set of equipment to carry out free sports, particularly outdoor strenuous sports and sports scenes needing cooperation of a plurality of equipment, and the complexity of the equipment greatly influences the use and the test. In addition, most of the existing algorithms are comprehensively operated by signals collected from a plurality of sensors, and the calculation amount and the transmission amount are large, so that the method is not suitable for small wearable equipment.
The invention mainly uses a painless and noninvasive method to collect the electrocardiosignals of a user through a skin surface electrode, processes the electrocardiosignals into electrocardio data, and calculates the overconsumption data after exercise according to the electrocardio data, thereby providing certain guidance for the activities of the user such as exercise, study, life and the like.
Traditional methods of measuring peroxygen consumption (EPOC) have been mainly based on ventilator detection, indirect calorimetry, and blood draw measurements, which are difficult to use in routine training. The skin surface electrode of the signal acquisition unit is embedded in the garment, so that the heart rate of a user can be measured in real time during daily training of the user, and the peroxide consumption data after exercise can be calculated. With the elimination of the traditional aerobic exercise fat-reducing method and the rise of a high intensity cyclic training method (HIIT), the consumption of the peroxide is an effective means for evaluating the training effect of the high intensity cyclic training method, the exercise fat-reducing effect of the training can be effectively evaluated, and the traditional exercise fat-reducing effect is difficult to complete by indexes such as heart rate and respiration. The invention can determine the peroxide consumption data after exercise, and can be hooked with a human physiology model to obtain other additional physiological parameters such as hormone level change, body composition change and other physiological fluctuations or physiological behaviors.
The wearable skin surface electrode is mainly used for collecting electrocardiosignals, and the algorithm is adopted to calculate the post-exercise peroxygen consumption (EPOC), so that the problems of high cost, discomfort and invasion in the body of the traditional indirect calorimetry, blood drawing measurement method and the like are solved.
The skin surface electrode of the signal acquisition unit is embedded in the garment, the skin surface electrode is a pasting point for acquiring electrocardiosignals in the garment, and the skin surface electrode has great influence on the electrocardiosignal acquisition quality, the garment comfort, the garment service life and the like.
The prior medical disposable spinning graphite collecting electrode has the problems of poor skin affinity, short service life, difficult repeated use, great difficulty in integrating with clothes and the like.
The invention optimizes the skin-friendly conductive fiber, which has the characteristics of good conductivity, good skin affinity, oxidation resistance, wear resistance, water washing resistance, aging resistance, long service life and the like, and is suitable for being made into flexible fabrics.
The skin surface electrode used by the invention is a flexible fabric made of the skin-friendly conductive fiber, has a soft hand feeling which is almost the same as that of a garment fabric, has no stimulation to skin, is embedded in the garment fabric, is comfortable to wear, and can be used as a sticking point for collecting electrocardiosignals.
The invention has the beneficial effects that: the measurement is convenient and accurate.
Detailed Description
The following further describes embodiments of the present invention with reference to examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The technical scheme of the specific implementation of the invention is as follows:
a detection method for detecting overconsumption after exercise based on electrocardiosignals comprises the following steps:
collecting electrocardiosignals of a human body through a signal skin-friendly conductive fiber collecting unit;
processing the electrocardiosignals into electrocardio data to obtain heart rate times HR;
calculating oxygen consumption VO from HR2；
According to HR (Reserve), i.e., the difference between HR (max) and HR (rest), and VO2(reserve), i.e. VO2(max) and VO2（rest) Difference, calculate VO2(max) percent;
EPOC is determined for peroxide consumption using supply and demand differential equations.
Preferably, the first and second electrodes are formed of a metal,
the supply and demand differential equation is:
EPOC(t+1)=EPOC(t)+a*eb*V-c*ed*(1-V)*EPOC(t)
wherein,
v is VO2(max) percent;
EPOC (0) =0 (initialization);
a, b, c and d are empirical parameters; dynamic correction is carried out by adopting a deep learning algorithm based on a cyclic neural network; setting initial values of a prior model of a, b, c and d according to empirical parameters;
e is a natural logarithm base number;
t is time.
EPOC can be calculated recursively according to the following equation,
dEPOC /dt=a*eb*V-c*ed*(1-V)*EPOC
initial value, EPOC (0) = 0;
EPOC（t+1）=EPOC（t）+a*eb*V-c*ed*(1-V)*EPOC(t)
the foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the technical principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (2)
1. The method for detecting the peroxy consumption after exercise based on the electrocardiosignal is characterized by comprising the following steps:
collecting electrocardiosignals of a human body by a skin-friendly conductive fiber signal collecting unit;
processing the electrocardiosignals into electrocardio data to obtain heart rate times HR;
calculating oxygen consumption VO from HR2；
According to HR (Reserve), i.e., the difference between HR (max) and HR (rest), and VO2(reserve), i.e. VO2(max) and VO2(rest) difference, calculate VO2(max) percent;
EPOC is determined for peroxide consumption using supply and demand differential equations.
2. The method for detecting post-exercise peroxygen consumption based on electrocardiographic signals of claim 1, wherein the differential equations of supply and demand are:
EPOC(t+1)=EPOC(t)+a*eb*V-c*ed*(1-V)*EPOC(t)
wherein,
v is VO2(max) percent;
EPOC (0) =0 (initialization);
a, b, c and d are empirical parameters; dynamic correction is carried out by adopting a deep learning algorithm based on a cyclic neural network; setting initial values of a prior model of a, b, c and d according to empirical parameters;
e is a natural logarithm base number;
t is time.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170105664A1 (en) * | 2014-03-03 | 2017-04-20 | Lifeq Global Limited | Real-Time and Continuous Determination of Excess Post-Exercise Oxygen Consumption and the Estimation of Blood Lactate |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201538129A (en) * | 2014-03-03 | 2015-10-16 | Global Nutrition & Health Inc | Real-time and continuous determination of excess post-exercise oxygen consumption and the estimation of blood lactate |
| CN108392199A (en) * | 2018-04-16 | 2018-08-14 | 泉州海天材料科技股份有限公司 | A kind of intelligent cardiac underwear and its moulding process |
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2018
- 2018-08-31 CN CN201811006516.1A patent/CN109330583A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201538129A (en) * | 2014-03-03 | 2015-10-16 | Global Nutrition & Health Inc | Real-time and continuous determination of excess post-exercise oxygen consumption and the estimation of blood lactate |
| CN108392199A (en) * | 2018-04-16 | 2018-08-14 | 泉州海天材料科技股份有限公司 | A kind of intelligent cardiac underwear and its moulding process |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170105664A1 (en) * | 2014-03-03 | 2017-04-20 | Lifeq Global Limited | Real-Time and Continuous Determination of Excess Post-Exercise Oxygen Consumption and the Estimation of Blood Lactate |
| US11291392B2 (en) * | 2014-03-03 | 2022-04-05 | LifeQ Limited Limited | Real-time and continuous determination of excess post-exercise oxygen consumption and the estimation of blood lactate |
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