TW201537375A - Exercise guiding system, exercise guiding method and anaerobic threshold measuring method - Google Patents

Abstract

The present disclosure provides an exercise guiding system including a sensing module, a computing module, a converting module and an output module. The sensing module keeps recording an R-R interval of a user doing exercise. The computing module receives the R-R interval from the sensing module and performs heart rate variability on the R-R interval to generate a first output. The converting module receives the first output from the computing module, recognizes a threshold output of the first output according to a threshold and acquires an anaerobic threshold corresponding to the user according to the threshold output, wherein anaerobic threshold corresponding to the user is a first heart rate corresponding to the threshold output in the R-R interval. The output module receives the anaerobic threshold from the converting module and output the guide exercise guide of the user according to the anaerobic threshold.

Description

Motion guidance system, motion guidance method, and measurement method of anaerobic threshold

The present disclosure relates to a motion guidance system, a motion guidance method, and an anaerobic threshold measurement method, and more particularly to a motion guidance system, a motion guidance method, and an anaerobic threshold using information conversion anaerobic threshold values during a user's heartbeat. Measurement method.

A comprehensive view of various fitness methods and projects, according to their energy metabolism and energy supply methods, can be attributed to three basic forms of exercise, aerobic exercise, anaerobic exercise and mixed exercise involving aerobic exercise and anaerobic exercise. In general, aerobic exercise is a long-term, low-intensity exercise form that produces carbon dioxide and water through fat metabolism, which can consume body fat during exercise and can lose weight. Anaerobic exercise is a short-term, high-intensity exercise form, which mainly consumes a lot of muscle glycogen and liver glycogen during exercise, mainly through ATP, phosphocreatine metabolism and glucose metabolism, so the effect of slimming is lower. In addition, the metabolite of anaerobic exercise is lactic acid or lactate. If the human body has been in a state of anaerobic exercise, lactic acid or lactate will accumulate rapidly, resulting in Muscle fatigue causes people to stop exercising.

Commonly used anaerobic exercise discriminating indicators include Maximal Oxygen Uptake (VO ₂ max) and Anaerobic Threshold (AT).

The maximum oxygen uptake is the maximum amount of oxygen that tissue cells can consume or utilize when they are engaged in the most intense exercise at sea level. The maximum oxygen uptake can be used to assess individual aerobic energy and cardio endurance, and can be used to set the athlete's endurance exercise intensity. In general, the unit of maximum oxygen uptake can be expressed in L/min with absolute oxygen intake, or in ml/kg/min with relative unit weight intake. Generally, the maximum oxygen uptake of adult males is about 30-40ml/kg/min, while the maximum oxygen uptake of professional athletes such as bicycle players or joggers can reach 80ml/kg/min. Since the method of estimating the maximum oxygen uptake requires the user to engage in intense exercise, it is not suitable for young people or seniors. In addition, the maximum oxygen uptake detection instrument is expensive, which is not conducive to promotion to the mass market.

The anaerobic threshold is the turning point of the body's energy system from aerobic exercise to anaerobic exercise, that is, the turning point of metabolism when the human body begins to accumulate lactic acid. The anaerobic threshold varies with each person's fitness status. The determination of the anaerobic threshold includes direct measurement of the lactic acid value, ventilation rate, and heart rate of the blood. In the actual measurement, the measurement of the first two is inconvenient (the blood must be drawn or expensive equipment is required), and the measurement of the heart rate is the easiest.

The related art discloses a method for determining an anaerobic threshold by using heartbeat specific data. However, the method is based on a system-predetermined personal data, including age, weight, sex, and the like, to obtain a maximum heart rate value as a basis for determining an anaerobic threshold. However, The heart rate of anaerobic respiration is not necessarily the highest heart rate. In other words, when two users of the same age but different physical fitness conditions use this method to estimate the anaerobic threshold, an error with the actual anaerobic threshold may occur.

Therefore, how to improve the anaerobic threshold determination method, and provide an effective and convenient way to analyze the anaerobic threshold according to the individual fitness status, and provide a motion guidance system for motion guidance, which is an issue to be solved in the industry.

The present disclosure provides a motion guidance system, a motion guidance method, and an anaerobic threshold measurement method for measuring a user's heartbeat period information without using an expensive instrument to generate an anaerobic threshold of a corresponding user, thereby providing User appropriate exercise guidelines.

The exemplary embodiment of the present disclosure provides a motion guidance system including a sensing module, a computing module, a conversion module, and an output module. The sensing module continuously records the information during the heartbeat of the user while exercising. The computing module is coupled to the sensing module, wherein the computing module receives the heartbeat period information corresponding to the user from the sensing module and performs heart rate variability analysis on the heartbeat information to obtain the first output value. The conversion module is coupled to the computing module, wherein the conversion module receives the first output value from the computing module, identifies a critical output value among the first output values according to the threshold value, and acquires the corresponding user according to the critical output value. The oxygen threshold, wherein the anaerobic threshold corresponding to the user is the first heart rate value corresponding to the critical output value in the information during the heartbeat. The output module is coupled to the conversion module, wherein the output module receives the anaerobic threshold from the conversion module and is based on the anaerobic threshold Output the user's movement guidelines.

The exemplary embodiment of the present disclosure proposes a motion guidance method that includes continuously recording information during a heartbeat during a user's exercise. The exercise guidance method also includes performing heart rate variability analysis on the information during the heartbeat to obtain the first output value. The motion guiding method also includes identifying a critical output value among the first output values according to the threshold value, and acquiring an anaerobic threshold value corresponding to the user according to the critical output value, wherein the corresponding user's anaerobic threshold value is corresponding to the information during the heartbeat period The first heart rate value of the critical output value. The exercise guidance method further includes outputting the user's exercise guide according to the anaerobic threshold.

The exemplary embodiment of the present disclosure proposes an anaerobic threshold measurement method that includes calculating a time series corresponding to a user's heartbeat period information. The method of measuring the anaerobic threshold also includes calculating a time series to generate a self-similarity parameter during heartbeat. The method for measuring the anaerobic threshold further comprises: identifying a critical parameter among the self-similarity parameters during the heartbeat according to the threshold value, and acquiring an anaerobic threshold corresponding to the user according to the critical parameter, wherein the anaerobic threshold of the corresponding user is during the heartbeat period The first heart rate value corresponding to the critical parameter in the information.

Based on the above, the motion guidance system, the motion guidance method, and the anaerobic threshold measurement method of the exemplary embodiment of the present disclosure can measure the heartbeat period information during the user's exercise, calculate the self-similarity parameter during the heartbeat according to the heartbeat period information, and obtain a correspondence. The user's anaerobic threshold provides the user with appropriate exercise guidelines.

The above described features and advantages of the present invention will be more apparent from the following description.

1, 6‧‧‧ Sports Guidance System

11, 61‧‧‧ Sensing Module

12, 62‧‧‧ Calculation Module

13, 63‧‧‧ conversion module

14, 64‧‧‧ Output Module

65‧‧‧Database Module

66‧‧‧ calibration module

S21, S23, S25, S27‧‧‧Steps for calculating the first output value

Steps for S41, S43, S45, S47‧‧‧ exercise guidance methods

S51, S53‧‧‧Steps for calculating the first output value

S1001, S1003, S1005, S1007‧‧‧ steps of the exercise guidance method

FIG. 1 is a block diagram of a motion guidance system according to a first exemplary embodiment of the present disclosure.

2 is a flow chart of calculating a first output value according to a first exemplary embodiment of the disclosure.

3 is a table of first output values and corresponding heart rate values calculated by the motion guidance system of the present disclosure.

FIG. 4 is a flowchart of a motion guidance method according to a first exemplary embodiment of the disclosure.

FIG. 5 is a flowchart of calculating a first output value according to a second exemplary embodiment of the disclosure.

FIG. 6 is a block diagram of a motion guidance system according to a third exemplary embodiment of the present disclosure.

7 is a comparison table of anaerobic threshold values calculated by a gas analyzer for a maximum oxygen uptake test and a motion guidance system according to the present disclosure.

FIG. 8 is a comparison table of anaerobic threshold values detected by the maximum oxygen uptake test using the general age formula, the motion guidance system of the present disclosure, and the gas analyzer.

Figure 9 is a comparison of the percentage of the heart rate value at the point of the maximum heart rate by the general age formula, the motion guidance system of the present disclosure, and the anaerobic threshold detected by the maximum oxygen uptake test using the gas analyzer. table.

FIG. 10 is a motion guidance method according to a third exemplary embodiment of the disclosure. Flow chart.

FIG. 11 is a comparison table of the energy supply mode analysis of the motion guidance system and the maximum oxygen uptake test using the gas analyzer.

The explanations of the terms used in the following disclosure will be defined as follows.

Anaerobic Threshold: refers to the metabolic turning point of the human body from the oxygen to the anaerobic energy system during exercise. The anaerobic threshold can be specifically digitized according to the blood lactate value, ventilation rate and heart rate value. The present disclosure indicates that the heart rate value at the point when the user engages in exercise into anaerobic respiration represents an anaerobic threshold.

Maximum Heart Rate (Maximal Heart Rate): The maximum value of the heartbeat frequency reached by a person as a result of increased exercise intensity. It is an indicator used to measure the appropriate exercise intensity. The general calculation formula expresses the maximum heartbeat frequency by (220-age). It is generally believed that when the exercise intensity causes the athlete's heart rate to reach 80% of its maximum heart rate, the athlete begins to enter the anaerobic exercise, that is, the general anaerobic threshold is calculated as (220-age)*80%. .

R-R Interval: The interval between heartbeat and heartbeat, usually represented by a continuous heart rate RR interval (R-R Interval). On the electrocardiogram, the R wave is a more significant waveform and is easily detected. The R pitch represents the rate of beat of the heart, so the RR interval is often used to represent the heartbeat interval. That is to say, the heartbeat period is the interval between adjacent R waves on the electrocardiogram.

Heart Rate Variability (HRV): Heart Rate Variance Analysis, also known as heart rate variability analysis, is a method for measuring the degree of change in continuous heart rate, which is an important method for assessing the function of the autonomic nervous system. The calculation method mainly analyzes the time series of heartbeat and heartbeat interval obtained by electrocardiogram or pulse measurement. In addition to the beating caused by its own rhythmic discharge, the heart is also regulated by the Autonomic Nervous System (ANS). In the past, there have been many studies showing that the regulation of the autonomic nervous system has a significant relationship with cardiovascular disease-related mortality, such as sudden cardiac death, hypertension, hemorrhagic shock, and septic shock. Clinically, heart rate variability analysis has also been found to be an indicator for predicting mortality after myocardial infarction and to predict the prognosis of patients with advanced liver cancer, or for a variety of pediatric diseases including congenital heart disease, myocarditis, diabetes, neonatal respiratory distress Symptoms, sudden death in infants, etc. The analysis mode can be divided into Time Domain analysis or Frequency Domain analysis. The present disclosure uses time domain and frequency domain analysis of heart rate variability analysis.

Maximal Oxygen Uptake (VO ₂ max): The maximum amount of oxygen that a tissue cell can consume or utilize while exercising the most intense exercise. The maximum oxygen uptake can be used to assess individual aerobic exercise energy and cardiorespiratory endurance, and can be used to set the athlete's exercise training intensity.

Maximum oxygen uptake test: Using a stationary exercise bike, the exercise is carried out by gradually increasing the load. During the exercise, the gas analyzer is used to collect and analyze the oxygen uptake, and the heart rate value is used as the exercise intensity to enter the anaerobic threshold. The principle of judgment is that when the Respiratory Exchange Ratio (RER) is greater than 1, the user enters the anaerobic threshold.

[First Exemplary Embodiment]

FIG. 1 is a block diagram of a motion guidance system according to a first exemplary embodiment of the present disclosure.

Referring to FIG. 1, the motion guidance system 1 of the present disclosure can calculate an anaerobic threshold for a user to engage in an anaerobic respiration period when the user engages in a motion state of the exercise, and provide a motion guide according to the anaerobic threshold. It should be noted that the motion guidance system 1 of the present disclosure can be installed on products such as electronic products, portable electronic products, watches, wearable devices, sports equipment, bicycles, treadmills, glasses, and biosensors, and the disclosure is disclosed. The exercise guidance system 1 can target at least one of a bicycle, aerobic exercise and running.

The motion guidance system 1 includes a sensing module 11, a computing module 12, a conversion module 13, and an output module 14.

The sensing module 11 can continuously record the information of the complex array heartbeat during the user's exercise. The information collected during the heartbeat can be transmitted through any external sensor that can detect the heartbeat of the human body. The external sensor can be coupled to the sensing module 11 so that the sensing module can record the information during the heartbeat of the user. For example, the heartbeat period information disclosed herein is an RR interval (R-R Interval). It should be noted that although the sensing module 11 of the present disclosure directly obtains the RR interval of the sensed user heartbeat from the external sensor, the disclosure is not limited thereto. For example, in another exemplary embodiment, the sensing module 11 can also calculate the RR interval of the heartbeat of the user according to the number of heartbeats of the user detected by the external sensor.

The computing module 12 is coupled to the sensing module 11 . The calculation module 12 from the sense The test module 11 receives the heartbeat period information corresponding to the user and performs heart rate variability analysis on the heartbeat period information to obtain the first output value α1.

2 is a flow chart of calculating a first output value according to a first exemplary embodiment of the disclosure.

Referring to FIG. 2, in step S21, the calculation module 12 calculates a time series of information of the user's heartbeat period. For example, in step S21, the computing module 12 calculates each heartbeat S ( i ) and the average heartbeat in the user's heartbeat period information S. Cumulative dispersion to generate time series .

Next, in step S23, the calculation module 12 operates on the time series to generate a heartbeat period trend value. For example, in step S23, the module 12 will calculate the time series C (k) is cut into sections having a predetermined length n, and each section is calculated local trend of the values C _n (k) by using the least squares method, to Generates a trend value during heartbeat.

Then, in step S25, the computing module 12 operates on the heartbeat trend value to generate a heartbeat fluctuation function. For example, the computing module 12 subtracts the local trend value C _n ( k ) of each segment from the time series C ( k ) and calculates the root mean square value of each segment to generate a heartbeat fluctuation function F ( n ) :

Where N represents the total length of the time series.

Finally, in step S27, the calculation module 12 recurs the plan according to the fluctuation function during the heartbeat and uses the points on the plan to obtain the self-similarity parameter during the heartbeat, and the self-similarity parameter during the center hop is the first output value α1. For example, the calculation module 12 plots the log ₁₀ F ( n ) relative to log ₁₀ ( n ), and calculates the linear equation of the point in the plan using the least squares method, and calculates the slope of the linear equation to obtain the self during the heartbeat. The similarity parameter (ie, the first output value α1).

Referring to FIG. 1 again, the conversion module 13 is coupled to the computing module 12 . Conversion The module 13 receives the first output value α1 from the calculation module 12, identifies a critical output value among the first output values α1 according to the threshold value, and acquires an anaerobic threshold value corresponding to the user according to the critical output value. In the present exemplary embodiment, the conversion module 13 uses the first heart rate value corresponding to the critical output value among the heartbeat period information as the anaerobic threshold of the corresponding user. It is worth noting that the anaerobic threshold can vary depending on the user's fitness status. The details will be described below with reference to FIG. 3.

3 is a first output value calculated by the motion guidance system according to the present disclosure. And a table of corresponding heart rate values. In FIG. 3, the motion guidance system 1 adopts an increasing load method to allow the user to step on the stationary exercise vehicle, and the load wattage is increased by 20 to 30 every 3 minutes, thereby calculating the first according to the calculation module 12. The value α1 is output to determine the heart rate value at which the user enters the anaerobic respiration point as the anaerobic threshold. Here, the threshold value of the present disclosure can be set to 1, when the first output value α1 is changed from greater than 1 to less than 1, the user can be judged to enter the anaerobic movement from aerobic exercise, and the first time less than 1 can be defined. An output value α1, which is 0.933, is the critical output value, so the anaerobic threshold corresponding to the user is the first heart rate value 136. However, the disclosure is not limited thereto. In order to confirm that the user enters the anaerobic threshold, the disclosure may also define a first output value α1 that is less than 1 and lasts for more than one minute, that is, 0.856, which is a critical output value, and the corresponding user's anaerobic threshold is the first heart rate value. 145. Taking the first output value α1 that is less than 1 and lasts for more than one minute as the critical output value avoids that only a single first output value α1 is less than 1, The subsequent first output value α1 is greater than 1, thereby increasing the accuracy of the disclosure. However, the present disclosure further defines that the first output value α1 entering the range of 1±δ for the first time is a critical output value, wherein δ can be adjusted as appropriate, for example, δ=0.1.

Referring to FIG. 1 again, the output module 14 is coupled to the conversion module 13 . The output module 14 receives an anaerobic threshold from the conversion module 13 and outputs a user's motion guidance based on the received anaerobic threshold. The exercise guidelines provide advice on exercise, including exercise time, mileage, optimal user heartbeat frequency, and the user's primary energy system (including fat, carbohydrates).

It should be noted that the sensing module 11 in the exemplary embodiment may be a sensor or a sensing circuit, and record information of the user's heartbeat and store it in the memory of the motion guiding system 1, and calculate the mode. The group 12 and the conversion module 13 can be implemented in the form of software or firmware. The processor of the motion guidance system 1 executes the information during the heartbeat and retrieves the information from the memory to generate an anaerobic threshold. However, the disclosure is not limited thereto. The calculation module 12 and the conversion module 13 can also be implemented as a calculation circuit and a conversion circuit. The calculation circuit receives the input heartbeat period information and outputs the anaerobic threshold value by the conversion circuit. After calculating the anaerobic threshold, the processor can extract the motion guidance of the user from the memory according to the anaerobic threshold and output it by the output module 14. The output module 14 can be an output device such as a display or a speaker that can visually or audibly let the user know the motion guidance.

FIG. 4 is a flowchart of a motion guidance method according to a first exemplary embodiment of the disclosure.

Referring to FIG. 4, in step S41, the sensing module 11 continues to record. The user engages in information during the heartbeat during exercise.

In step S43, the calculation module 12 performs heart rate variability analysis on the heartbeat information to obtain a first output value.

In step S45, the conversion module 13 identifies the critical output value among the first output values according to the threshold value, and acquires the anaerobic threshold of the corresponding user according to the critical output value.

In step S47, the output module 14 outputs the user's motion guidance according to the anaerobic threshold.

[Second exemplary embodiment]

The motion guidance system of the second exemplary embodiment is essentially the same as the motion guidance system of the first exemplary embodiment, wherein the difference is that in the second exemplary embodiment, the calculation module performs heart rate variability on the information during the heartbeat according to the time series. The analysis is followed by sorting in time series, and then the sorted time series is subjected to frequency domain parameter calculation of heart rate variability analysis, and the first output value is calculated by using frequency domain parameters.

The structure of the motion guidance system of the second exemplary embodiment is the same as that of the motion guidance system of the first exemplary embodiment. Therefore, the difference between the second exemplary embodiment and the first exemplary embodiment will be described below with reference to FIG.

Referring to FIG. 1 , the motion guidance system 1 of the second exemplary embodiment includes a sensing module 11 , a computing module 12 , a conversion module 13 , and an output module 14 .

The sensing module 11 can continuously record the information of the complex array heartbeat during the user's exercise. The information collected during the heartbeat can be transmitted through any external sensor that can detect the heartbeat of the human body, and the external sensor can be coupled to the sensing module 11 so that the sensing module can be Record the user's heartbeat information. For example, the heartbeat period information disclosed herein is an RR interval (R-R Interval). It should be noted that although the sensing module 11 of the present disclosure directly obtains the RR interval of the sensed user heartbeat from the external sensor, the disclosure is not limited thereto. For example, in another exemplary embodiment, the sensing module 11 can also calculate the RR interval of the heartbeat of the user according to the number of heartbeats of the user detected by the external sensor.

The computing module 12 is coupled to the sensing module 11 , and the computing module 12 receives the heartbeat period information of the user from the sensing module 11 and performs heart rate variability analysis on the heartbeat information to obtain the first output value α1. .

FIG. 5 is a flowchart of calculating a first output value according to a second exemplary embodiment of the disclosure.

Referring to FIG. 5, in step S51, the calculation module 12 performs heart rate variability analysis on the time series according to the information of each group of heartbeat periods. For example, in step S51, the computing module 12 calculates that the user's heartbeat period information is sorted in time series.

Next, in step S53, the calculation module 12 performs frequency domain parameter calculation of the heart rate mutation analysis on the sorted time series, and calculates the first output value α1 using the frequency domain parameters. For example, in step S53, the calculation module 12 converts the time series into the high frequency parameter HF and the low frequency parameter LF , and obtains the first output value α1 through the formula (1).

11~2/(1+ ^HF / _LF ).......................................... ..........(1)

It is worth noting that the high frequency parameters range from 0.15 to 0.40 Hz. The number of variances in the heartbeat period of the frequency range of 0.15 to 0.40 Hz is mainly affected by respiration and represents the activity index of the parasympathetic nerve. The low-frequency parameters are taken from the frequency range of 0.04 to 0.15 Hz, and the low-frequency parameters are the variation of the information during the heartbeat period of the frequency range of 0.04 to 0.15, which represents the activity index of the sympathetic nerve or the index of simultaneous regulation of the sympathetic and parasympathetic nerves.

Referring to FIG. 1 again, the conversion module 13 is coupled to the computing module 12, and converts The module 13 receives the first output value α1 from the calculation module 12, identifies the critical output value among the first output values α1 according to the threshold value, and acquires the anaerobic threshold of the corresponding user according to the critical output value. In the present exemplary embodiment, the conversion module 13 uses the first heart rate value corresponding to the critical output value among the heartbeat period information as the anaerobic threshold of the corresponding user. The output module 14 is coupled to the conversion module 13. The output module 14 receives an anaerobic threshold from the conversion module 13, and outputs a motion guidance of the user according to the received anaerobic threshold.

It should be noted that the sensing module 11 can be a sensor or a sensing circuit. The calculation module 12 and the conversion module 13 can be implemented in a software or firmware format, or can be implemented as a calculation circuit and a conversion circuit. The output module 14 can be used for visual or audible use of a display, a speaker, or the like. Learn about the output of the exercise guide.

[Third exemplary embodiment]

The motion guidance system of the third exemplary embodiment is essentially the same as The motion guidance system of the second exemplary embodiment, wherein the motion guidance system of the third exemplary embodiment further includes a database module for storing user status information, and includes a correction module for providing compliance with the user's body based on the status information. State correction The signal is calculated to calculate a more accurate first output value.

FIG. 6 is a motion guidance system according to a third exemplary embodiment of the present disclosure. The block diagram.

Referring to FIG. 6, the motion guidance system 6 of the present disclosure can be engaged to a user. The exercise state of the exercise calculates the anaerobic threshold at which the user engages in the anaerobic respiration period and provides exercise guidance based on the anaerobic threshold. The motion guidance system 6 includes a sensing module 61, a computing module 62, a conversion module 63, an output module 64, a database module 65, and a calibration module 66. It should be noted that the motion guidance system 6 of the present disclosure can be installed on products such as electronic products, portable electronic products, watches, wearable devices, sports equipment, bicycles, treadmills, glasses, and biosensors, and the disclosure is disclosed. The exercise guidance system 6 can target at least one of a bicycle, aerobic exercise and running.

The sensing module 61 can continuously record the complex array of hearts when the user engages in sports During the hopping period, the information collected during the heartbeat can be transmitted through any external sensor that can detect the heartbeat of the human body. The external sensor can be coupled to the sensing module 61, so that the sensing module can record the information of the user during the heartbeat period. . For example, the heartbeat period information disclosed herein is an RR interval (R-R Interval). It should be noted that although the sensing module 61 of the present disclosure directly obtains the RR interval of the sensed user heartbeat from the external sensor, the disclosure is not limited thereto. For example, in another exemplary embodiment, the sensing module 61 can also calculate the RR interval of the heartbeat of the user according to the number of heartbeats of the user detected by the external sensor.

The computing module 62 is coupled to the sensing module 61. The calculation module 62 from the sense The test module 61 receives the heartbeat period information corresponding to the user and performs heart rate variability analysis on the heartbeat information to obtain the first output value α1.

The conversion module 63 is coupled to the calculation module 62. The conversion module 63 receives the first output value α1 from the calculation module 62, identifies the critical output value among the first output values α1 according to the threshold value, and acquires the anaerobic threshold value of the corresponding user according to the critical output value, wherein the corresponding user The anaerobic threshold is the first heart rate value corresponding to the critical output value among the information during the heartbeat.

The calibration module 66 is coupled to the conversion module 63. The calibration module receives the status information and provides correction information to the conversion module 63 based on the status information. The conversion module 63 acquires the anaerobic threshold of the corresponding user according to the critical output value and the correction information. The status information may be at least one of a sports mode and a health information, and the correction information may include various heart rate values, exercise intensity, exercise time, heart rate variability parameter values, rate of heart rate variability parameter changes, and time domain of heart rate variability of the user. Information to set the point at which the user enters anaerobic breathing.

The database module 65 is coupled to the correction module 66 and stores state information.

The output module 64 is coupled to the conversion module 63. The output module 64 receives an anaerobic threshold from the conversion module 63 and performs an energization mode analysis based on the received anaerobic threshold to output a user's motion guidance. The exercise guidelines provide advice on exercise, including exercise time, mileage, optimal user heartbeat frequency, and the user's primary energy system (including fat, carbohydrates).

It should be noted that, similar to the first exemplary embodiment, the sensing module 61, the computing module 62, the conversion module 63, and the output module 64 in the exemplary embodiment are applicable. The same is true for the sensing module 11, the computing module 12, the conversion module 13, and the output module 14 in the first exemplary embodiment. In other words, the sensing module 61 can be a sensor or a sensing circuit, and the computing module 62 and the conversion module 63 can be implemented in a software or firmware format, or can be implemented as a computing circuit and a conversion circuit. The output module 64 can be an output device such as a display or a speaker that can visually or audibly let the user know the motion guidance. In addition, in the present exemplary embodiment, the database module 65 can be included in the memory of the motion guidance system 6 and store state information, and the calibration module 66 can be implemented in software or firmware. The processor of the motion guidance system 6 executes the state information from the memory and converts it into correction information and transmits it to the conversion module 63 to correct the first output value α1. However, the disclosure is not limited thereto. The correction module 66 can also be used as a correction circuit to receive input state information and output correction information to the conversion module 63.

Figure 7 shows the maximum oxygen uptake test using a gas analyzer and The anaerobic threshold comparison table calculated by the disclosed exercise guidance system. In the maximum oxygen uptake test, an incremental load method is adopted to allow three 27-year-old users to use a stationary exercise bike. The load wattage is increased by 20 to 30 watts per 3 minutes, while using a gas analyzer and the disclosed The exercise guidance system is analyzed. The gas analyzer performs a Respiratory Exchange Rate (RER) analysis to determine that the user enters an anaerobic respiration state when the value of the respiratory exchange rate RER is equal to one. In the motion guidance system of the present disclosure, when the first output value α1 is less than 1 and lasts for more than one minute, it is determined that the user enters the anaerobic respiration state, and the heart rate value (HR, Heart rate) according to the point of entering the anaerobic respiration point. As an anaerobic threshold.

In Figure 7, a total of three samples were tested. Sample 1 is 17 minutes at the time when the motion guidance system of the present disclosure enters the anaerobic threshold, the corresponding heart rate is 145 bpm (the number of beats per minute), and the time at which the anaerobic threshold is entered using the respiratory exchange rate is 19 minutes, corresponding to The heart rate is 153 bpm. Sample 2 was 23 minutes at the time when the motion guidance system of the present disclosure entered the anaerobic threshold, and the corresponding heart rate value was 167 bpm, while the time to enter the anaerobic threshold using the respiratory exchange rate analysis was 21 minutes, and the corresponding heart rate value was 163 bpm. Sample 3 is 23 minutes when the motion guidance system of the present disclosure enters the anaerobic threshold, the corresponding heart rate value is 122 bpm (the number of beats per minute), and the time point for entering the anaerobic threshold using the respiratory exchange rate analysis is 21 minutes, corresponding to The heart rate is 119 bpm.

FIG. 8 is a comparison table of anaerobic threshold values detected by the maximum oxygen uptake test using the general age formula, the motion guidance system of the present disclosure, and the gas analyzer. In Fig. 8, since the ages of samples 1 to 3 are both 27 years old, the anaerobic thresholds calculated by the general formula are the same as 154 bpm, and both samples 2 and 3 are the same as those based on the maximal oxygen uptake test. There is a significant difference in oxygen thresholds because users of the same age have different anaerobic thresholds depending on the fitness status.

Figure 9 is a comparison of the percentage of the heart rate value at the point of the maximum heart rate by the general age formula, the motion guidance system of the present disclosure, and the anaerobic threshold detected by the maximum oxygen uptake test using the gas analyzer. table. In FIG. 9, the results from the sample 1 to the sample 3 show that the motion guidance system and the maximum oxygen uptake test of the present disclosure have a heart rate value as a percentage of the maximum heart rate, and only have 4%, 1%, and 1%, respectively. error.

Referring to FIG. 7, FIG. 8 and FIG. 9, the motion guidance system of the present disclosure has a relatively accurate anaerobic threshold compared with the general formula. Compared with the maximal oxygen uptake test, the disclosed motion guidance system can achieve accurate detection of the anaerobic threshold without using expensive equipment or wearing a respiratory analyzer during exercise.

FIG. 10 is a flowchart of a motion guidance method according to a third exemplary embodiment of the disclosure.

Referring to FIG. 10, in step S1001, the sensing module 61 continuously records the information of the heartbeat period during the user's exercise.

In step S1003, the calculation module 62 performs heart rate variability analysis on the heartbeat information to obtain a first output value.

In step S1005, the conversion module 63 corrects the threshold according to the motion mode or the health information of the correction module 66, and obtains a critical output value according to the corrected threshold value, and acquires a corresponding anaerobic threshold according to the critical output value.

In step S1007, the output module 64 performs an energization mode analysis based on the anaerobic threshold to output a user's motion guidance.

FIG. 11 is a comparison table of the energy supply mode analysis of the motion guidance system and the maximum oxygen uptake test using the gas analyzer. In Fig. 11, when the gas analyzer's respiratory exchange rate RER is less than 0.85, it means that it is engaged in low-intensity exercise and consumes fat. The respiratory exchange rate RER is between 0.85 and 1 means that it is at rest and consumes protein. The respiratory exchange rate RER is greater than 1 for Is engaged in high-intensity exercise and consumes glucose. When the first output value α1 of the present disclosure is smaller than the critical output value and the motion state represents that the low-intensity motion is being performed and the fat is consumed, the first output value α1 is smaller than the critical output value. And in the non-moving state, it is in a non-moving state and consumes protein, and the first output value α1 is greater than the critical output value, indicating that high-intensity exercise is being performed and glucose is consumed. With Figure 11, the energy supply mode analysis can be performed correctly, and the user's motion guidance can be output.

In summary, the disclosed motion guidance system utilizes heart rate variability analysis The correction data such as the exercise mode and the health information are used to derive the anaerobic threshold of the user during exercise, that is, the heart rate value when the user enters the anaerobic exercise from the aerobic exercise. The anaerobic threshold calculated by the motion guidance system of the present disclosure is compared with the anaerobic threshold detected by the gas analyzer for the maximum oxygen uptake test, and the heart rate value at the point of the reverse enthalpy movement is the percentage of the maximum heart rate. There is only a gap of less than 10%, and the motion guidance system disclosed herein does not require an expensive gas analyzer. In summary, the disclosed motion guidance system can provide an individualized calculation of an anaerobic threshold without user age limitation, and use heart rate variability analysis to derive an anaerobic threshold, and can calculate high accuracy at a relatively low cost. The anaerobic threshold.

The present disclosure has been disclosed in the above embodiments, but it is not intended to limit the disclosure, and any person skilled in the art can make some changes and refinements without departing from the spirit and scope of the disclosure. The scope of protection of this disclosure is subject to the definition of the scope of the appended claims.

S1001, S1003, S1005, S1007‧‧‧ steps of the exercise guidance method

Claims (26)

A motion guidance system includes: a sensing module that continuously records information during a complex array of heartbeats during a motion; a computing module coupled to the sensing module, wherein the computing module The sensing module receives the heartbeat period information corresponding to the user and performs a heart rate variability analysis on the information during the heartbeat period of the complex array to obtain a plurality of first output values; a conversion module coupled to the computing module The conversion module receives the first output values from the computing module, identifies a critical output value of the first output values according to a threshold value, and obtains a corresponding user according to the critical output value. An anaerobic threshold, wherein the anaerobic threshold corresponding to the user is a first heart rate value corresponding to the critical output value of the information during the heartbeat period of the complex array; and an output module coupled to the conversion module, The output module receives the anaerobic threshold from the conversion module, and outputs a motion guide of the user according to the anaerobic threshold. The motion guidance system of claim 1, wherein for each of the complex array heartbeat information, the calculation module calculates a cumulative deviation of each heartbeat of the user from an average heartbeat to generate a a time series, wherein the computing module further cuts the time series into a plurality of segments having a predetermined length, and calculates a local trend value of each segment of the segments by using a least square method, wherein the calculating The module further subtracts the time series from each of the sections The local trend value and calculating a root mean square value of each of the segments to generate a wave function, wherein the computing module further plots a pair of the wave function with respect to the predetermined length of the pair A plan view, wherein the calculation module further calculates a linear equation of a point in the plan view by using the least square method, and calculates a slope of the linear equation to obtain each of the first output values. The motion guidance system of claim 1, further comprising: a correction module coupled to the conversion module, the calibration module receiving a status information and providing a correction information to the conversion according to the status information The module, wherein the conversion module obtains the anaerobic threshold corresponding to the user according to the critical output value and the correction information. The motion guidance system of claim 3, further comprising: a database module coupled to the calibration module, wherein the database module stores the status information, and the status information is a motion mode At least one of the information with a health message. The motion guidance system of claim 1, wherein the calculation module performs the heart rate variability analysis according to a time series for each of the complex array heartbeat information, and converts the time series into a frequency domain parameter. And using the frequency domain parameter to calculate each of the first output values. The motion guidance system of claim 5, wherein the frequency domain parameter comprises at least one high frequency parameter and a low frequency parameter. The motion guidance system according to claim 6, wherein the high frequency parameter is extracted from a frequency range of 0.15 to 0.40 Hz, and the high frequency parameter is a variation of information of the complex array heartbeat during the frequency band range. . The motion guidance system of claim 6, wherein the low frequency parameter is extracted from a frequency band range of 0.04 to 0.15 Hz, and the low frequency parameter is a variation of the information of the complex array heartbeat during the frequency band range. A motion guidance method includes: continuously recording information of a complex array heartbeat during a motion of a user; performing a heart rate variability analysis on the information during the heartbeat of the complex array to obtain a plurality of first output values; and identifying the threshold value according to a threshold value a threshold output value of the first output value, and obtaining an anaerobic threshold corresponding to the user according to the critical output value, wherein the anaerobic threshold corresponding to the user is in the information during the heartbeat period of the complex array A first heart rate value of the critical output value should be; and a motion guidance of the user is output based on the anaerobic threshold. The motion guidance method according to claim 9, wherein the step of performing a heart rate variability analysis on the information of the complex array heartbeat to obtain the first output values comprises: calculating, for each of the complex array heartbeat period information, a cumulative deviation of each heartbeat of the user from an average heartbeat to generate a time series corresponding to each of the complex array heartbeats; cutting the time series into a plurality of segments having a predetermined length, and Use one of the most The Xiaoping method calculates a local trend value for each of the segments; subtracts the local trend value for each segment of the segments from the time series and calculates each segment of the segments a root mean square value to generate a wave function; a plan view of a pair of numbers of the wave function relative to the predetermined length; and a linear equation for calculating a point in the plan view using the least squares method, and calculating the A slope of the linear equation to obtain each of the first output values. The method of motion guidance according to claim 9 further includes: receiving a status information and providing a correction information according to the status information; and obtaining the corresponding user according to the critical output value and the correction information Oxygen threshold. The method of motion guidance according to claim 11 further includes: storing the status information, wherein the status information is at least one of a sport mode and a health message. The motion guidance method according to claim 9, wherein the step of performing a heart rate variability analysis on the information of the complex array heartbeat to obtain the first output values comprises: for each of the complex array heartbeat period information, The heart rate variability analysis is performed on a time series, and the time series is converted into a frequency domain parameter, and each of the first output values is calculated using the frequency domain parameter. The motion guidance method of claim 13, wherein the frequency domain parameter comprises at least one high frequency parameter and a low frequency parameter. The motion guidance method according to claim 14, wherein the high frequency parameter is extracted from a frequency band of 0.15 to 0.40 Hz, and the high frequency parameter is a variation of information of the complex array heartbeat during the frequency band range. . The motion guidance method according to claim 14, wherein the low frequency parameter is extracted from a frequency band range of 0.04 to 0.15 Hz, and the low frequency parameter is a variation of the information of the complex array heartbeat during the frequency band range. An anaerobic threshold measurement method includes: calculating a plurality of time series corresponding to a user's complex array heartbeat period information; performing operations on the time series to generate a plurality of heartbeat self-similarity parameters; and according to a threshold The value identifies a critical parameter among the self-similarity parameters during the heartbeat, and obtains an anaerobic threshold corresponding to the user according to the critical parameter, wherein the anaerobic threshold corresponding to the user is the information during the heartbeat of the complex array A first heart rate value corresponding to the critical parameter. The method for measuring an anaerobic threshold value according to claim 17, wherein the step of calculating the time series of the complex array heartbeat period information of the user comprises: calculating the information of the complex group heartbeat period of the user A cumulative dispersion of each heartbeat and an average heartbeat to produce the time series. The method for measuring an anaerobic threshold according to claim 18, wherein the step of calculating the time series to generate the self-similarity parameters during the heartbeat comprises: Performing operations on the time series to generate a plurality of heartbeat period trend values; calculating the heartbeat period trend values to generate a plurality of heartbeat period wave function; and retrieving the plurality of floor plans according to the heartbeat period wave function, and utilizing The points on the plan views yield the self-similarity parameters during the heartbeat. The method for measuring an anaerobic threshold value according to claim 19, wherein the step of calculating the time series to generate the trend value during the heartbeat comprises: cutting the time series into a predetermined length A plurality of segments, and a local trend value for each segment of the segments is calculated using a least squares method to generate the heartbeat period trend values. The method for measuring an anaerobic threshold according to claim 20, wherein the step of calculating the heartbeat trend value to generate the heartbeat fluctuation function comprises: subtracting the time series from the time series The local trend value for each segment of the segment and a root mean square value for each segment of the segments is calculated to generate the heartbeat fluctuation function. The method for measuring an anaerobic threshold according to claim 21, wherein the plan views are derived according to the heartbeat fluctuation function, and the points on the plan views are used to obtain the self-similarity parameters during the heartbeat period. The steps include: plotting the plan views of the logarithm of the wave function during the heartbeat relative to the logarithm of the predetermined length; A linear equation for the points in the plan views is calculated using the least squares method, and the slope of the linear equation is calculated to obtain the self-similarity parameters during the heartbeats. The method for measuring an anaerobic threshold according to claim 17, wherein the step of calculating the time series to generate the self-similarity parameters during the heartbeat comprises: converting the time series into a frequency domain Parameters, and using the frequency domain parameters to calculate the self-similarity parameters during the heartbeats. The method for measuring an anaerobic threshold according to claim 23, wherein the frequency domain parameter comprises at least one high frequency parameter and a low frequency parameter. The method for measuring an anaerobic threshold value according to claim 24, wherein the high frequency parameter is extracted from a frequency band range of 0.15 to 0.40 Hz, and the high frequency parameter is the complex array heartbeat period of the frequency band range. The number of variations in the information. The method for measuring an anaerobic threshold value according to claim 24, wherein the low frequency parameter is extracted from a frequency band range of 0.04 to 0.15 Hz, and the low frequency parameter is information of the complex array heartbeat period of the frequency band range. The number of variations.