WO2017152612A1 - 估算最大耗氧量和下次总运动时间的方法 - Google Patents

估算最大耗氧量和下次总运动时间的方法 Download PDF

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Publication number
WO2017152612A1
WO2017152612A1 PCT/CN2016/098945 CN2016098945W WO2017152612A1 WO 2017152612 A1 WO2017152612 A1 WO 2017152612A1 CN 2016098945 W CN2016098945 W CN 2016098945W WO 2017152612 A1 WO2017152612 A1 WO 2017152612A1
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exercise time
total
estimating
sensor
oxygen consumption
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PCT/CN2016/098945
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English (en)
French (fr)
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程士恒
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博晶医电股份有限公司
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Definitions

  • the invention relates to a motion monitoring method. More specifically, the present invention relates to a motion monitoring method that utilizes the user's physical strength to monitor the user's real-time maximum oxygen consumption and/or predict the outcome of the user's next motion.
  • the present invention provides a method of estimating maximum oxygen consumption, comprising the steps of:
  • the processing module estimates a rate of physical exertion based on the physiological data
  • the processing module calculates a current total exercise time based on the physical power consumption rate
  • the processing module estimates athletic performance based on the current total exercise time
  • the processing module calculates kinetic energy consumption based on the motion data
  • the processing module estimates an average oxygen consumption based on the kinetic energy consumption
  • the processing module estimates the maximum oxygen consumption based on the average oxygen consumption and the exercise capacity
  • the user interface displays the maximum oxygen consumption.
  • the invention also provides a method for estimating the total total exercise time, comprising the following steps:
  • the processing module estimates a rate of physical exertion based on the physiological data
  • the processing module calculates a current total exercise time based on the physical power consumption rate
  • the processing module estimates athletic performance based on the current total exercise time
  • the processing module calculates kinetic energy consumption based on the motion data
  • the processing module estimates an average oxygen consumption based on the kinetic energy consumption
  • the processing module estimates a maximum oxygen consumption based on the average oxygen consumption and the exercise capacity
  • the processing module estimates the next total exercise time based on the maximum oxygen consumption and the default displacement
  • the user interface displays the next total exercise time.
  • the invention further provides a method for estimating the total total exercise time, comprising the following steps:
  • the terminal device Receiving a historical motion pattern from the terminal device, wherein the historical motion pattern includes a set of heart rates and a set of displacements corresponding to the set of heart rates;
  • the data array is transmitted to the terminal device.
  • the method provided by the invention uses a sensor to monitor the movement of the user in real time and uses the processing module to perform related data processing to estimate the maximum oxygen consumption and the next total exercise time, and the method is simple and practical.
  • FIG. 1 is a schematic block diagram of a motion monitoring device provided by at least one embodiment of the present invention.
  • FIG. 2 is a schematic diagram showing the relationship between the concentration of lactic acid and the heart rate of a user in at least one embodiment of the present invention.
  • FIG. 3 is a diagram showing changes in the lactic acid concentration and the physical strength level of a user in motion in the same period of time in at least one embodiment of the present invention.
  • FIG. 4 is a schematic diagram showing the relationship between the physical strength level of the user and the current total exercise time in at least one embodiment of the present invention.
  • FIG. 5 is a schematic diagram showing a relationship between a user's exercise ability and a current total exercise time in at least one embodiment of the present invention.
  • FIG. 6 is a schematic diagram of factors affecting the relationship between exercise ability and current total exercise time in FIG. 5 of at least one embodiment of the present invention.
  • FIG. 7 is a flow chart of a method for estimating maximum oxygen consumption provided by at least one embodiment of the present invention.
  • FIG. 8 is a flow chart of a method for estimating a total total exercise time provided by at least one embodiment of the present invention.
  • FIG. 9 is a schematic diagram showing the correspondence between the physical strength level and the RPE in at least one embodiment of the present invention.
  • FIG. 10 is a flow chart of a method for estimating maximum oxygen consumption provided by at least one embodiment of the present invention.
  • FIG. 11 is a flowchart of a method for estimating a total total exercise time according to another embodiment of the present invention.
  • FIG. 12 is a schematic block diagram of a motion monitoring device with a heart rate sensor and a GPS provided by at least one embodiment of the present invention.
  • FIG. 13 is a schematic block diagram of a motion monitoring device having an external sensing module and an external user interface, in accordance with at least one embodiment of the present invention.
  • Figure 14 is an illustration of the data array generated in Figure 11.
  • Motion monitoring equipment 100 Sensing module 101 Processing module 102 User Interface 103 Storage module 104 Heart rate sensor 201 GPS 202
  • FIG. 1 is a schematic block diagram of a motion monitoring device provided by at least one embodiment of the present invention.
  • the motion monitoring device 100 includes a sensing module 101, a processing module 102, a user interface 103, and a storage module 104.
  • the sensing module 101 can include at least one physiological sensor for sensing and measuring physiological signals of the user.
  • the physiological signal includes at least one of the following: an electrocardiogram (EKG) signal, a pulse, a heart rate, a breathing pattern, a glycogen concentration, and a pulse oximeter.
  • EKG electrocardiogram
  • SpO2 oxygen concentration obtained from a tissue oximeter
  • StO2 oxygen concentration obtained from a tissue oximeter
  • the tissue oximeter may be a near-infrared spectroscopy tissue oximeter or the like.
  • the oxygen concentration in the frontal lobe is related to the person's rating of perceived exertion (RPE).
  • the sensing module 101 can include a plurality of sensors for sensing and measuring physiological and non-physiological signals of the user as previously described.
  • the sensing module 101 can include various types of non-physiological sensors, such as pedometers, speedometers, accelerometers, gyroscopes, gravity sensors, and the like.
  • the processing module 102 is hardware having an auxiliary circuit, such as a processor, microcontroller or microprocessor, that performs the motion monitoring device by performing basic arithmetic, logic, and input/output operations.
  • Program instructions A variety of different products on the market can be used as the processing module 102, such as, but not limited to, Nordic Semiconductor's nRF52832, STMicroelectronics' STM32L476.
  • the user interface 103 includes at least one output unit (not shown) and/or at least one input unit (not shown), or any combination thereof.
  • the output can be a display, a vibrating assembly or a speaker, or any combination for illustrating the physiological condition of the user during or after exercise.
  • the physiological condition may include at least one of the following: a measurement of a physiological signal, a physical level, a kinetic energy consumption, a maximum oxygen consumption, and the like.
  • the input can be any human-machine interface capable of receiving biological information from the user such as height, weight, age and gender, such as a touchpad, voice receiver or button.
  • the user interface 103 can be adapted to send information directly to the sensing module 101, the processing module 102, or the storage module 104.
  • the entered information can be processed by the processing module 102 and sent to the output to inform the user of the current physical condition of the user. For example, BMI value, etc.
  • the storage module 104 can be any type of volatile or non-volatile memory for storing computer program instructions executed by the processing module 102, biological information entered by the user through the user interface 103. And motion information from the sensing module 101 and/or the processing module 102.
  • the term "physical strength” refers to the ability of a user to use his/her own strength and effectively maintain a period of time. The smaller a person's physical strength, the shorter his or her continuous exercise time of the same exercise intensity without rest.
  • FIG. 2 is a schematic diagram showing the relationship between the lactic acid concentration of the user and the heart rate in at least one embodiment of the present invention.
  • the user's average heart rate is positively correlated with lactic acid concentration and can be fitted using a linear regression model, a non-linear regression model, a piecewise function, other mathematical models, or any combination thereof. Therefore, the lactic acid concentration associated with the lactic acid accumulated in the bloodstream can be estimated based on the user's heart rate.
  • FIG. 3 is a graph showing changes in lactic acid concentration and physical strength levels of a user in motion during a same period of time in at least one embodiment of the present invention.
  • a concentration of lactic acid of 4 millimoles per liter is considered to be a threshold between aerobic and anaerobic motion.
  • oxygen is delivered to the muscles through the user's breathing to provide the energy needed to maintain the work.
  • anaerobic exercise the exercise intensity is high enough to cause the production of lactic acid, resulting in persistent discomfort and fatigue.
  • the user's physical strength level is set to 100%.
  • the lactic acid concentration increases and the physical strength level decreases.
  • the exercise monitoring apparatus 100 suggests that the user choose to reduce the exercise intensity to metabolize the accumulated lactic acid. Therefore, in the time interval from t1 to t2, the physical strength level is restored from 0% to 100%.
  • FIG. 4 is a schematic diagram showing the relationship between the physical strength level of the user during exercise and the current total exercise time in at least one embodiment of the present invention.
  • the physical strength level is 100% at T1, 70% at T2, and 50% at T3. Obviously, the level of physical strength will continue to drop until the user is physically unable to continue exercising. In other words, when the user moves to a physiological state that is doing his best, the physical level should be 0%, where "current total exercise time" is defined as the time required to reach the full physiological state. Since the motion monitoring device 100 continuously monitors the level of physical strength, the current total exercise time can be estimated accordingly.
  • the current total motion time in Figure 4 can be estimated by a linear regression model, a non-linear regression model, a piecewise function, other mathematical models, or any combination thereof.
  • FIG. 5 is a schematic diagram showing a relationship between a user's exercise ability and a current total exercise time in at least one embodiment of the present invention.
  • the fraction of maximal oxygen consumption (FVO 2 ) of the user is shown in FIG. 5 as the current total exercise time increases.
  • the athletic ability described later is defined as FVO 2 .
  • athletic ability is the ratio of the average oxygen consumption of the user to his/her maximum oxygen consumption during the exercise that is exhausted.
  • athletic ability can also be used to describe the user's athletic efficiency. This concept should be understood as follows, that is, it takes 15 seconds for a person to sprint 100 meters, but it takes 1 hour for the same person to run 5000 meters, and 72 seconds for every 100 meters. Therefore, the shorter the total total exercise time, the higher the exercise efficiency. Refer to FIG.
  • Figure 6 is a schematic illustration of the factors affecting the relationship between exercise capacity and current total exercise time in Figure 5, in accordance with at least one embodiment of the present invention.
  • the three main components of the FVO 2 (exercise ability) curve over time are shown in Figure 6. These three main components are anaerobic capacity, maximum aerobic capacity and aerobic endurance.
  • the area of the A1 area under the curve indicates the anaerobic capacity, and the larger the area of A1, the greater the anaerobic energy that one can use.
  • Tn represents the current total exercise time at any point after T1, where the slope (steepness) of the curve between T1 and Tn is aerobic endurance.
  • aerobic endurance is the ability of a person to effectively maintain FVO 2 as close as possible to 1.0. Therefore, the steeper the curve, the lower the aerobic endurance becomes.
  • FVO 2 f (T, maximum aerobic capacity, anaerobic capacity, aerobic endurance), where T is the current total exercise time.
  • an average of anaerobic capacity, maximum aerobic capacity, and aerobic endurance can be obtained by big data analysis of athlete performance.
  • the average of anaerobic capacity, maximum aerobic capacity, and aerobic endurance can be further configured using the human biological information mentioned in the section of Figure 1.
  • FIG. 7 is a flow chart of a method for estimating maximum oxygen consumption using the motion monitoring device 100 of FIG. 1 in at least one embodiment of the present invention.
  • the following steps may estimate the maximum oxygen consumption of the user based on the physiological data and the motion data of the user.
  • Step S101 receiving physiological data of a user of the physiological sensor from the sensing module 101;
  • Step S102 The processing module 102 estimates a physical strength consumption rate of the user based on the physiological data.
  • Step S103 The processing module 102 calculates a current total exercise time of the user based on the physical power consumption rate
  • Step S104 The processing module 102 estimates the user's exercise ability based on the current total exercise time
  • Step S105 receiving motion data of the non-physiological sensor from the sensing module 101;
  • Step S106 The processing module 102 calculates a kinetic energy consumption of the user based on the motion data.
  • Step S107 The processing module 102 estimates the average oxygen consumption of the user based on the kinetic energy consumption
  • Step S108 The processing module 102 estimates the maximum oxygen consumption of the user based on the average oxygen consumption amount and the exercise capability;
  • Step S109 The maximum oxygen consumption is transmitted to the user interface 103 by the processing module 102 for display;
  • Step S110 The user interface 103 displays the maximum oxygen consumption.
  • the storage module 104 may include a plurality of linear regression models, nonlinear regression models, segmentation functions, other mathematical models, or any combination thereof corresponding to FIGS. 2-5 for processing the module 102. Steps S102-S104 and S106-S107 are performed.
  • steps S101-S104 may be performed after steps S105-S107.
  • the sensing module 101 can include at least one physiological sensor for sensing and measuring physiological signals of the user.
  • the physiological signal includes at least one of the following signals: EKG signal, pulse rate, heart rate, breathing pattern, glycogen concentration, oxygen concentration obtained by pulse oximeter (SpO2), oxygen obtained by tissue oximeter Concentration (StO2) and oxygen concentration measured by human frontal lobe.
  • the tissue oximeter may be a near infrared spectroscopy (NIRS) tissue oximeter or the like.
  • Body composition can include the percentage of body fat, bone, moisture, and muscle.
  • the non-physiological sensor is capable of acquiring athletic data including various types of motion parameters, such as motion displacement, exercise time, running speed, riding power, climbing height, and the like.
  • the motion displacement may be a running distance, a climbing height, a riding distance, and the like.
  • the motion monitoring device 100 can include a motion sensor, a riding power meter, a pedometer, or any other speed or speed related sensor as a non-physiological sensor in the sensing module 101, recording corresponding motion data.
  • the user can connect an external motion sensor, a riding power meter, a pedometer or any other speed or speed related sensor to the sensing module 101 of the motion monitoring device 100 for recording motion data and transmitting it to Processing module 102.
  • the motion sensor includes at least one of an accelerometer, a gyroscope, and a magnetometer.
  • kinetic energy consumption can be estimated by converting motion data, such as converting running speed to kinetic energy expressed in power (Joules).
  • power (t) f (speed (t), t), where "power (t)” is the power at time t, and "speed (t)” is the speed at time t. Therefore, kinetic energy consumption is a function of speed, where kinetic energy consumption is positively correlated with motion speed.
  • oxygen consumption generates energy, so a person's average oxygen consumption (VO 2 ) can be estimated by kinetic energy consumption.
  • VO 2 f (power (t), t), that is, the average oxygen consumption amount (VO 2 ) required for the user to apply kinetic energy during exercise is estimated in step S106.
  • velocity is used as a parameter in the present embodiment, other motion parameters may be used, such as climbing height, cycle power, and the like.
  • the physiological sensor may be a combination of sensors that detect various physiological parameters, such as heart rate obtained by an optical heart rate sensor, oxygen concentration obtained by NIRS, and the like.
  • the non-physiological sensor may also be a combination of sensors that detect various non-physiological parameters, such as acceleration obtained by the motion sensor, position obtained by GPS, ambient temperature obtained by the thermometer, angular acceleration obtained by the gyroscope, and the like.
  • FIG. 8 is a flow chart of a method for estimating a total total exercise time provided by at least one embodiment of the present invention.
  • the following steps may estimate the user's next total exercise time based on the user's physiological data, exercise data, and default displacement:
  • Step S301 receiving physiological data of a user of the physiological sensor from the sensing module 101;
  • Step S302 The processing module 102 estimates a physical consumption rate of the user based on the physiological data
  • Step S303 The processing module 102 calculates a current total exercise time of the user based on the physical power consumption rate
  • Step S304 The processing module 102 estimates the user's exercise ability based on the current total exercise time
  • Step S305 receiving motion data of the non-physiological sensor from the sensing module 101;
  • Step S306 The processing module 102 calculates a kinetic energy consumption of the user based on the motion data.
  • Step S307 The processing module 102 estimates the average oxygen consumption of the user based on the kinetic energy consumption
  • Step S308 The processing module 102 estimates the maximum oxygen consumption of the user based on the average oxygen consumption and the exercise capability;
  • Step S309 The processing module 102 estimates the next total exercise time based on the maximum oxygen consumption and the default displacement;
  • Step S310 The processing module 102 transmits the next total exercise time to the user interface 103 for display;
  • Step S311 The user interface 103 displays the next total exercise time.
  • the storage module 104 may include a plurality of linear regression models, non-linear regression models, segmentation functions, other mathematical models, or any combination thereof corresponding to FIG. 2-5, for performing step S302. -S204 and S306-S307.
  • steps S301-S304 may be performed after steps S305-S307.
  • the next total exercise time is based on the user's maximum oxygen consumption to estimate the performance of the user's full force exercise, but in fact the user does not perform full force exercise.
  • the user can use the exercise monitoring device 100 to complete the running for 5 kilometers and learn that he/she completes the best performance of running 10 kilometers without actually completing the running of 10 kilometers, regardless of whether the 5 kilometer running is Complete with his/her best energy. That is to say, the user can easily complete the exercise but still knows the performance of the full-power exercise that he/she has not performed. Therefore, estimating the total total exercise time is especially useful for anyone who wants to know his/her best performance without having to exhaustively complete the full exercise.
  • the default displacement may be a distance or a set of distances stored in the storage module 104 of the motion monitoring device 100, wherein the distance or the set of distances may be from 3 kilometers, 5 kilometers, Choose from 10 kilometers, 21 kilometers, 42 kilometers, etc. Alternatively, it can be set by the user of the motion monitoring device 100 through the input unit of the user interface 103. It should be noted that the default displacement may be the displacement of the movement, such as the running distance, the climbing height, the riding distance, and the like.
  • FIG. 9 is a schematic diagram showing the correspondence between the physical strength level and the RPE in at least one embodiment of the present invention.
  • the correspondence between the physical strength level and the self-inductive fatigue level is disclosed, wherein the type of the RPE may be a Borg Rating of the Perceived Exertion Scale.
  • the RPE value blood lactic acid concentration (lactic acid concentration in the blood flow), or real-time exercise load decreases, the physical strength level recovers.
  • the degree of fatigue the concentration of blood lactic acid (the concentration of lactic acid in the bloodstream), or the real-time exercise load rises, the level of physical strength decreases.
  • the physical strength level can be presented within a certain range, such as 100% to 0%.
  • the level of physical strength is at least partially related to the linear or non-linear relationship of the RPE.
  • an RPE value of about 12 indicates that the exercise intensity performed by the user of the exercise monitoring device 100 is a medium level. That is to say, the user can experience "slight" muscle fatigue and breathing is heavier than when no exercise is performed, so an RPE value of about 12 can correspond to a physical strength level of 100%.
  • an RPE value between 15 and 17 indicates that the exercise intensity of the user of the exercise monitoring device 100 is a higher level. That is to say, the user can experience "stiff/heavy" muscle fatigue and breathing much more than when not doing any exercise, so the RPE value between 15 and 17 can correspond to 0% of the physical level.
  • the value of the RPE is linearly or nonlinearly related to the heart rate, and thus the level of physical strength is also linearly or nonlinearly related to the heart rate.
  • the physical strength level of each user is normalized to a fixed range corresponding to the maximum and minimum heart rates.
  • the physical strength level may be a negative value, as shown in FIG. 9, wherein a negative physical strength level may trigger the exercise monitoring device 100 to automatically adjust an estimate of the physical strength level according to the user's exercise intensity, thereby calibrating the physical strength level accordingly.
  • FIG. 10 is a flow chart of a method for estimating maximum oxygen consumption provided by at least one embodiment of the present invention.
  • the following steps may estimate the maximum oxygen consumption of the user based on the user's RPE value and motion data:
  • Step S501 Receive an RPE value of a user from an input unit of the user interface 103;
  • Step S502 The processing module 102 estimates a physical consumption rate of the user based on the RPE value
  • Step S503 The processing module 102 calculates a current total exercise time based on the physical power consumption rate
  • Step S504 The processing module 102 estimates the exercise capability based on the current total exercise time
  • Step S505 receiving motion data of the non-physiological sensor from the sensing module 101;
  • Step S506 The processing module 102 calculates kinetic energy consumption based on the motion data
  • Step S507 The processing module 102 estimates an average oxygen consumption amount based on the kinetic energy consumption
  • Step S508 The processing module 102 estimates a maximum oxygen consumption amount based on the average oxygen consumption amount and the exercise capability;
  • Step S509 the processing module 102 transmits the maximum oxygen consumption to the user interface 103 for display;
  • Step S510 The user interface 103 displays the maximum oxygen consumption.
  • the storage module 104 may include a plurality of linear regression models, nonlinear regression models, segmentation functions, other mathematical models, or any combination thereof corresponding to FIG. 2-5, for performing step S502. -S504 and S506-S507.
  • steps S501-S504 may be performed after steps S505-S507.
  • step S108 of FIG. 7, step S308 of FIG. 8, and step S508 of FIG. 10 estimating the maximum oxygen consumption can be accomplished by a function of exercise capacity and average oxygen consumption, wherein the exercise capability Negatively correlated with maximum oxygen consumption, the average oxygen consumption is positively correlated with the maximum oxygen consumption.
  • VO 2 Max f (VO 2 , FVO 2 ), where VO 2 Max is the maximum oxygen consumption, VO 2 is the average oxygen consumption, and FVO 2 is the exercise capacity.
  • the same concept of estimating the maximum oxygen consumption can be used in any of the other embodiments of the present invention.
  • 11 is a flow chart of a method for estimating a total total exercise time provided by at least one embodiment of the present invention.
  • the following steps may estimate the user's maximum oxygen consumption based on the user's physiological data and motion data:
  • Step S701 Receive a historical motion mode from a terminal device, where the historical motion mode includes a set of heart rates and a set of displacements corresponding to the heart rate;
  • Step S702 Calculate a set of heart rate percentages based on the group of heart rates
  • Step S703 Calculating a set of speeds based on the set of displacements
  • Step S704 Estimating a maximum oxygen consumption amount based on the group heart rate percentage and the group speed, wherein the maximum oxygen consumption amount is negatively correlated with the group heart rate percentage, and is positively correlated with the group speed;
  • Step S705 Estimating the next total exercise time based on a default displacement and the maximum oxygen consumption, the next total exercise time is positively correlated with the default displacement, and the next total exercise time is negatively correlated with the maximum oxygen consumption;
  • Step S706 Receive an environmental condition from the terminal device.
  • Step S707 calibrating the next total exercise time with the environmental condition to generate a total exercise time of a specific environment
  • Step S708 generating a data array including the maximum oxygen consumption, the next total exercise time, and the total exercise time of the specific environment;
  • Step S709 The data array is transmitted to the terminal device.
  • steps S702 and S703 is interchangeable.
  • the environmental conditions may affect a person's athletic performance, wherein the environmental conditions may be steepness, altitude, atmospheric pressure, wind speed, ambient temperature, and the like.
  • These environmental conditions can be obtained through a variety of non-physiological sensors, such as ambient temperature sensors, anemometers, GPS sensors, level sensors, and atmospheric pressure sensors. For example, when a person runs up the slope, the running speed will be greatly slowed down. It should be noted that the calibration of the next total exercise time in step S707 can also be applied to the methods shown in FIGS. 7, 8, and 10.
  • the heart rate percentage is calculated as the denominator of the highest heart rate in the set of heart rates, and each heart rate as a numerator.
  • the percentage of heart rate when a person is exercising should be about 60% or above. If any percentage of heart rate calculated in step S702 is less than 60%, the person may be considered not in motion, so the percentage of heart rate below 60% may be omitted from the estimated maximum oxygen consumption in step S704. It should be understood that the 60% value is used as an example, and the percentage of heart rate that each person is considered to be in motion may be different. Therefore, the percentage of heart rate that is considered to be in motion can be customized in the exercise monitoring device 100.
  • the data array may include the maximum oxygen consumption estimated in step S704, the next total exercise time estimated in step S705, and the total exercise time of the particular environment corrected in step S707. Additionally, the data array can include set default displacements such as 3 kilometers, 5 kilometers, 10 kilometers, 21 kilometers, 42 kilometers, and the like. Each default shift may correspond to a next total exercise time estimated in step S705 and a total exercise time in a particular environment corrected in step S707.
  • the data array may also include recommendations for maximum oxygen consumption for warm-up, wherein the recommended warm-up maximum oxygen consumption can be used to determine if the warm-up before exercise is sufficient. For example, the user can know the recommended maximum warm oxygen consumption of the warm-up by the method shown in FIG.
  • the user wearing the motion monitoring device 100 can read the user's maximum oxygen consumption from the user interface 103 in real time.
  • the user can know that he is ready for exercise by achieving the recommended maximum warm-up oxygen consumption by real-time maximum oxygen consumption.
  • the terminal device may be the motion monitoring device 100.
  • the heart rate is acquired by a physiological sensor of the motion monitoring device 100, the displacement being acquired by a non-physiological sensor of the motion monitoring device 100.
  • the terminal device may be any other monitoring device having a physiological sensor and a non-physiological sensor.
  • a portable device with a heart rate sensor and a GPS sensor may include an output jack (not shown), wherein the output jack enables the motion monitoring apparatus 100 to transmit the historical motion data of step S701 or the data array of step S709 through the transmission line.
  • the motion monitoring device 100 may also include a wireless communication module (not shown), wherein the wireless communication module enables the motion monitoring device 100 to wirelessly transmit the historical motion data of step S701 or receive the data array of step S709.
  • the terminal device may be a computer device having a user input function, and the heart rate and displacement are input to the terminal device by a user.
  • the heart rate and displacement can be sent to the computer device wirelessly or by wire by any other monitoring device.
  • the computer device can be a personal computer, a laptop, a tablet, a mobile device, or the like.
  • various physiological parameters can be used as additional factors for estimating maximum oxygen consumption, such as heart rate changes, body temperature, body composition, blood glucose, blood pressure, and the like.
  • the body composition can include the percentage of fat, bone, water, and muscle in the body.
  • the user of the exercise monitoring device 100 can personalize the estimate of the maximum oxygen consumption.
  • FIG. 12 is a schematic block diagram of a motion monitoring device having a heart rate sensor and a GPS provided by at least one embodiment of the present invention.
  • the motion monitoring device 100 includes a sensing module 101, a processing module 102, a user interface 103, and a storage module 104.
  • the sensing module 101 further includes a heart rate sensor 201 and a GPS 202, as compared to FIG.
  • the heart rate sensor 201 can be used to record a person's heart rate changes and send it to the processing module 102 as physiological data.
  • the GPS 202 can record the coordinates of a person to obtain the displacement and speed of the person, and the GPS can transmit the speed as motion data to the processing module 102.
  • the processing module 102 can execute at least one of the methods as previously described stored in the storage module 104, such as the method illustrated in FIG.
  • the heart rate sensor 201 includes at least two electrodes (not shown), wherein the at least two electrodes are electrically connected to the skin of the user to detect the heart rate of the user.
  • the motion monitoring device 100 may further include an analog front end (not shown), such as an AFE, through which the center rate sensor 201 can send an analog signal to the processing module 102, wherein the analog front end can But not limited to the AD8232 from Analog Devicede1 and the ADS1191 from Texas Instruments.
  • an analog front end such as an AFE, through which the center rate sensor 201 can send an analog signal to the processing module 102, wherein the analog front end can But not limited to the AD8232 from Analog Devicede1 and the ADS1191 from Texas Instruments.
  • the heart rate sensor 201 includes at least one light source (not shown), wherein the heart rate sensor 201 is an optical heart rate sensor that detects a user's heart rate.
  • the GPS 202 can be, but is not limited to, SiRFstarV 5E from CSR (Cambridge Silicon Radio), EVA-M8M from U-Blox.
  • FIG. 13 is a schematic block diagram of an external sensing module 101 and an external user interface 103 of the motion monitoring device 100 provided by at least one embodiment of the present invention.
  • the motion monitoring apparatus 100 may include only the processing module 102 and the storage module 104, according to an embodiment of the present invention.
  • the sensing module 101 can be a wearable device capable of wirelessly communicating with the motion monitoring device 100 to transmit physiological data (eg, heart rate) and motion data (speed) to the processing module 102 via wireless communication.
  • physiological data eg, heart rate
  • motion data speed
  • the user interface 103 can be implemented by a mobile device or a portable device.
  • a user of the motion monitoring device 100 can estimate a maximum oxygen consumption based on the heart rate and speed wirelessly received from the sensing module 101 and transmit the maximum oxygen consumption to the user's mobile phone in real time, wherein the movement The phone can display the maximum oxygen consumption.
  • Figure 14 is an illustration of the data array generated in Figure 11.
  • the data array exemplified therein includes a maximum oxygen consumption, a total total exercise time, and a total exercise time for a particular environment.
  • the data array can include, but is not limited to, a set of default displacements, such as 3 kilometers, 5 kilometers, 10 kilometers, 21 kilometers, 42 kilometers, and the like.
  • each default displacement may correspond to the total total exercise time and the total exercise time of the particular environment.
  • the data array may also include recommendations for maximum oxygen consumption for warm-up.
  • the default displacement may be set by the user of the motion monitoring device 100 through the input unit of the user interface 103. It should be noted that the default displacement may be the displacement of the movement, such as the running distance, the climbing height, the riding distance, and the like.

Abstract

一种通过获取个人生理数据和运动数据估算最大耗氧量的方法，包括如下步骤：接收来自第一传感器的生理数据；处理模块基于该生理数据估算体力消耗速率；该处理模块基于该体力消耗速率计算当前总运动时间；该处理模块基于该当前总运动时间估算运动能力；接收来自第二传感器的运动数据；该处理模块基于该运动数据计算动能消耗；该处理模块基于该动能消耗估算平均耗氧量；该处理模块基于该平均耗氧量和该运动能力估算该最大耗氧量；传送该最大耗氧量至用户界面；该用户界面显示该最大耗氧量。还提供了一种估算下次总运动时间的方法，及通过环境条件校正下次总运动时间得到特定环境的总运动时间的方法。

Description

估算最大耗氧量和下次总运动时间的方法 技术领域
本发明涉及一种运动监测方法。更具体地，本发明涉及一种利用用户的体力来监测用户的实时最大耗氧量和/或预测用户下次运动的结果的运动监测方法。
背景技术
在现代社会运动已经变得越来越流行。除了专业运动员，许多人不仅为了健康进行运动，也想要知道他们在运动中的表现。传统地，这只能通过在实验室用多种检测设备进行体能测试来准确的实现。或者，通过聘请私人健身教练来帮助他们了解自身的表现。
近来，各种类型的运动表现评价快速地发展并被专业运动员、体育爱好者、健身教练甚至个人使用。在所有类型的运动表现评价中，最大耗氧量被广泛地运用于并被证明是有效的。然而，为了监测和分析呼吸速率、耗氧量、二氧化碳产生量等的变化，不花费一小时带着用于接收呼吸的吹嘴在实验室的跑步机上就不能容易地获取每个人的最大耗氧量。因此，这种方法并不便于获取一个人在日常运动练习或训练中的运动表现。
鉴于上述情况，需要一种可靠地表明任何用户在任何地方运动的实时最大耗氧量的运动监测方法，而无需在实验室中佩戴吹嘴进行测试。
发明内容
本发明提供了一种估算最大耗氧量的方法，包括以下步骤：
接收来自第一传感器的生理数据；
处理模块基于该生理数据估算体力消耗速率；
该处理模块基于该体力消耗速率计算当前总运动时间；
该处理模块基于该当前总运动时间估算运动能力；
接收来自第二传感器的运动数据；
该处理模块基于该运动数据计算动能消耗；
该处理模块基于该动能消耗估算平均耗氧量；
该处理模块基于该平均耗氧量和该运动能力估算该最大耗氧量；
传送该最大耗氧量至用户界面；
该用户界面显示该最大耗氧量。
本发明还提供了一种估算下次总运动时间的方法，包括以下步骤：
接收来自第一传感器的生理数据；
处理模块基于该生理数据估算体力消耗速率；
该处理模块基于该体力消耗速率计算当前总运动时间；
该处理模块基于该当前总运动时间估算运动能力；
接收来自第二传感器的运动数据；
该处理模块基于该运动数据计算动能消耗；
该处理模块基于该动能消耗估算平均耗氧量；
该处理模块基于该平均耗氧量和该运动能力估算最大耗氧量；
该处理模块基于该最大耗氧量和默认位移估算该下次总运动时间；
传送该下次总运动时间至用户界面；
该用户界面显示该下次总运动时间。
本发明进一步提供了一种估算下次总运动时间的方法，包括以下步骤：
接收来自终端设备的历史运动模式，其中该历史运动模式包括一组心率和对应于该一组心率的一组位移；
基于该一组心率计算一组心率的百分比；
基于该一组位移计算一组速度；
基于该一组心率的百分比和该一组速度估算最大耗氧量，其中该最大耗氧量与该一组心率的百分比负相关并与该一组速度正相关；
基于默认位移和该最大耗氧量估算下次总运动时间，其中该下次总运动时间与该默认位移正相关，该下次总运动时间与该最大耗氧量负相关；
生成一个包括该最大耗氧量和该下次总运动时间的数据阵列；
将该数据阵列传送至该终端设备。
本发明提供的方法使用传感器对用户的运动进行实时监测并使用处理模块进行相关数据处理以估算最大耗氧量和下次总运动时间，方法简单实用。
 附图说明
附图示出了本发明的一个或多个实施方式，并与说明书一起用于解释本发明的原理。在可能的情况下，相同的附图标号在整个附图中被用于指代一个实施方式中相同或相似的元件，其中：
图1为本发明的至少一实施方式所提供的运动监测设备的示意性框图。
图2为本发明的至少一实施方式中乳酸浓度与用户心率之间关系的示意图。
图3为本发明的至少一实施方式中运动中的用户的乳酸浓度与体力水平在同一时间段的变化。
图4为本发明的至少一实施方式中用户的体力水平与当前总运动时间之间关系的示意图。
图5为本发明的至少一实施方式中用户的运动能力与当前总运动时间之间关系的示意图。
图6为本发明的至少一实施方式图5中影响运动能力与当前总运动时间之间关系的因素的示意图。
图7为本发明的至少一实施方式所提供的估算最大耗氧量的方法流程图。
图8为本发明的至少一实施方式所提供的估算下次总运动时间的方法流程图。
图9为本发明的至少一实施方式中体力水平与RPE之间对应关系的示意图。
图10为本发明的至少一实施方式所提供的估算最大耗氧量的方法流程图。
图11为本发明的另一实施方式所提供的估算下次总运动时间的方法流程图。
图12为本发明的至少一实施方式所提供的具有心率传感器和GPS的运动监测设备的示意性框图。
图13为本发明的至少一实施方式所提供的具有外部传感模块和外部用户界面的运动监测设备的示意性框图。
图14为图11中产生的数据阵列的示例。
依照惯例，附图中描述的各项特征并不是按比例绘制的，仅旨在强调本发明的相关特征。相同的附图标号在附图和说明书中用于表示相似的元件。
主要元件符号说明
运动监测设备 100
传感模块 101
处理模块 102
用户界面 103
存储模块 104
心率传感器 201
GPS 202
如下具体实施方式将结合上述附图进一步说明本发明。
具体实施方式
现参照附图对本发明进行更加全面的描述，其中本发明的示例性的实施方式被示出。本发明可以以多种不同的方式实施，并不受限于此处阐述的实施方式。相反，这些实施方式的提供旨在彻底和完整的揭露本发明，并将充分地向本领域的技术人员表达本发明的范围。文中相似的附图标号用于指代相似的元件。
本文中所用的术语旨在描述具体的实施方式，并非意在限制本发明的范围。如文中所使用的单数形式的“一”也意图包含其复数形式，除非上下位另有说明。本文中所使用的“包含”、“包括”、“具有”用于指明所陈述的特征、区域、整体、步骤、操作、元件和/或部件的存在，但不排除存在或增加一个或多个其他特征、区域、整体、步骤、操作、元件、部件和/或其组合。
应当理解的是，词语“和/或”包括一个或多个所列的相关项目的任意和所有的组合。还应当理解的是，虽然词语“第一”、“第二”、“第三”等在此用于描述各种元件、组件、区域、部件和/或部分，这些元件、组件、区域、部件和/或部分并不应当受到这些词语的限制。这些词语仅用于区分一个元件、组件、区域、部件和/或部分与另一个元件、组件、区域、部件或部分。因此，下面讨论的第一元件、组件、区域、部件和/或部分可以被称为第二元件、组件、区域、部件和/或部分而不脱离本发明的意旨。
除非另有定义，本文中所使用的所有术语（包括科技和科学术语）与本发明所属的技术领域中普通技术人员通常理解的含义相同。进一步地，除非文中有明确的定义，常用字典中已有定义的词语应当被解释为与其在相关技术内容和本发明上下文中的含义一致的意思，而不是被解释为理想化的或过于正式的含义。
下面将结合附图图1-13描述本发明的实施方式。参照附图将对本发明进行详细地描述，图中所示的元件并不是按比例示出的，并且其中相同或相似的元件在多个视角中被标示为相同或相似的附图标号及相同或相似的术语。
图1为本发明的至少一实施方式所提供的运动监测设备的示意性框图。
运动监测设备100包括传感模块101、处理模块102、用户界面103和存储模块104。
在本发明的一个实施方式中，传感模块101可以包括至少一个用于感应和测量用户生理信号的生理传感器。例如，该生理信号包括下述信号中的至少一种：心电 (electrocardiogram, EKG) 信号、脉搏、心率、呼吸模式 (breathing pattern)、糖原浓度、由脉搏血氧计 (pulse oximeter) 得到的氧气浓度 (SpO2)、由组织血氧计 (tissue oximeter) 得到的氧气浓度 (StO2) 和由人的额叶测得的氧气浓度。该组织血氧计可以是近红外光谱组织血氧计等。额叶的氧气浓度与人的自感劳累等级(rating of perceived exertion, RPE) 相关。
在本发明的另一实施方式中，传感模块101可以包括多个用于感应和测量如前所述的用户的生理信号和非生理信号的传感器。对于非生理信号，传感模块101可以包括各种类型的非生理传感器，例如计步器、速度计、加速计、陀螺仪、重力传感器等。
在本发明的至少一个实施方式中，处理模块102是具有辅助电路的硬件，例如处理器、微控制器或微处理器，通过执行基本的算术、逻辑和输入/输出操作完成运动监测设备的计算机程序指令。市场上多种不同的产品可以被用作处理模块102，诸如但不仅限于Nordic Semiconductor公司的nRF52832，意法半导体 (STMicroelectronics) 公司的STM32L476。
在本发明的一个实施方式中，用户界面103包括至少一个输出单元（未示出）和/或至少一个输入单元（未示出），或它们的任何组合。该输出可以是显示器、振动组件或扬声器、或用于在运动中或运动后说明用户生理状况的任何组合。其中，该生理状况可以包括以下至少一种：生理信号、体力水平、动能消耗、最大耗氧量等的测量。该输入可以是任何能够接收来自用户的生物信息如身高、体重、年龄和性别等的人-机界面，例如触摸板、语音接收器或按钮。此外，用户界面103可适用于直接向传感模块101、处理模块102或存储模块104发送信息。输入的信息可以被处理模块102处理并发送至该输出以使用户了解用户当前的身体状况。例如，体重指数值 (BMI value) 等。
在本发明的一个实施方式中，存储模块104可以是任何类型的易失性或非易失性存储器，用于存储被处理模块102执行的计算机程序指令、用户通过用户界面103输入的生物学信息及来自传感模块101和/或处理模块102的运动信息。
应当注意的是，术语“体力”指的是用户运用他/她自己的力量并有效地维持一段时间的能力。一个人的体力越小，他/她在不休息的条件下进行相同运动强度的持续运动时间越短。
图2为本发明的至少一实施方式中用户的乳酸浓度与心率之间关系的示意图。
参照图2，用户的平均心率与乳酸浓度正相关，可以用线性回归模型、非线性回归模型、分段函数、其他数学模型或它们的任意组合拟合。因此，基于用户的心率能够估算与血流中积累的乳酸相关的乳酸浓度。
图3为本发明的至少一实施方式中运动中的用户在同一时间段的乳酸浓度与体力水平的变化。
本领域所周知的，4毫摩尔/升的乳酸浓度被认为是有氧运动和无氧运动之间的阈值。有氧运动中，氧气通过用户的呼吸被传送到肌肉提供维持工作所需的能量。无氧运动中，运动强度高到足以引起乳酸的产生，从而导致持续的不适和疲劳感。
参照图3，当乳酸浓度在2-6毫摩尔/升的范围时，用户的体力水平被设定为100%。当用户持续运动时，乳酸浓度增加而体力水平下降。当乳酸浓度达到阈值时，即乳酸在t1时间增加到L1，用户的体力水平基本上为0%，运动监测设备100建议用户选择降低运动强度以代谢累积的乳酸。因此，在t1到t2的时间区间，体力水平从0%恢复到100%。应当注意的是，由0%恢复到100%并不是必要的，但是从0%恢复到一个具体的百分比有助于用户提高他/她在运动或竞赛中的表现。还应当注意的是，不同用户完全恢复所需的时间，如体力水平从0%到100%，是相似的，例如，约8至12分钟。因此，一个人的乳酸浓度与这个人的体力水平负相关。
应当注意的是，虽然图1-3中揭露了上述用心率对应标准化体力水平的方法，其他生理信号也可以被用于实现该目的，例如EKG信号、脉搏、心率、呼吸模式、糖原浓度、由脉搏血氧计得到的氧气浓度 (SpO2)、由组织血氧计得到的氧气浓度 (StO2) 和由人的额叶测得的氧气浓度。该组织血氧计可以是近红外光谱（NIRS）组织血氧计等。此外，这一设想可应用于本发明的任一实施方式。
图4为本发明至少一实施方式中用户在运动中的体力水平与当前总运动时间之间的关系的示意图。图4中体力水平在T1时为100%，T2时为70%，T3时为50%。显然的，体力水平将持续下降直到用户生理上不能继续运动。换言之，当用户运动到竭尽全力的生理状态，体力水平应当为0%，其中“当前总运动时间”被定义为达到竭尽全力的生理状态所需的时间。由于运动监测设备100持续地监测体力水平，该当前总运动时间可以被相应的估算。例如，图4中的当前总运动时间可以被线性回归模型、非线性回归模型、分段函数、其他数学模型或它们的任意组合估算。
图5为本发明的至少一实施方式中用户的运动能力与当前总运动时间之间的关系的示意图。
图5中示出了用户的最大耗氧量的分数 (fraction of maximal oxygen consumption, FVO2) 随当前总运动时间的增加而减少。后续所述的运动能力被定义为FVO2。换言之，运动能力是在竭尽全力的运动过程中用户的平均耗氧量与他/她最大耗氧量之比。可选地，运动能力也可以被用于描述用户的运动效率。这一理念应被理解如下，即，一个人冲刺100米可需要15秒，但同一个人跑5000米需要1小时，平均每100米需要72秒。因此，当前总运动时间越短，运动效率越高。再次参阅图5的FVO2，用户用非常短的时间运动到耗尽全力（100米冲刺）的FVO2应当相对高于马拉松（跑42千米）的FVO2。鉴于以上情况，用户的当前总运动时间越长，该用户的运动能力越低。因此，运动能力非线性负相关于当前总运动时间。这对于任何想要管理他/她的运动强度完成特定运动以达到他/她的最高表现的人是非常有用的，其中运动强度直接关系到一个人的耗氧量，运动强度越高，更多氧气被人吸入以满足个人的需求，当前总运动时间越短。
图6为本发明的至少一个实施方式图5中影响运动能力与当前总运动时间之间关系的因素的示意图。
图6中示出了随时间影响FVO2 (运动能力) 曲线的三个主要成分。这三个主要成分是无氧能力、最大有氧能力和有氧耐力。曲线下的A1区域面积表示无氧能力，其中A1的面积越大则一个人可以运用的无氧能量越大。曲线下的A2区域面积表示有氧能量，T1时为最大有氧能力FVO2=1.0，其中T1为一个人停止使用无氧能量的时间。图6中最大有氧能力被标准化为FVO2=1.0，实际上时刻T1因人而异。Tn表示在T1之后任一点的当前总运动时间，其中T1和Tn之间的曲线的斜率（陡度）为有氧耐力。换言之，有氧耐力是一个人将FVO2有效地保持在尽可能的接近1.0的能力。因此，该曲线越陡，有氧耐力变得越低。应当注意的是，不同的人由于他们身体条件如年龄、性别等可以具有不同的无氧能力、最大有氧能力和有氧耐力。因此，运动能力与全力运动之间的关系因人而异。例如，FVO2 = f ( T, 最大有氧能力，无氧能力，有氧耐力)，其中，T为当前总运动时间。在实践中，无氧能力、最大有氧能力和有氧耐力的一平均值可以通过对运动员表现的大数据分析获取。此外，无氧能力、最大有氧能力和有氧耐力的平均值也可以用图1部分提到的人的生物信息进一步配置。
图7为本发明的至少一实施方式中使用图1中的运动监测设备100估算最大耗氧量的方法流程图。
参照图7，以下步骤可以基于用户的生理数据和运动数据对用户的最大耗氧量进行估算。
步骤S101：接收来自传感模块101的生理传感器的用户的生理数据；
步骤S102：处理模块102基于该生理数据估算用户的体力消耗速率；
步骤S103：处理模块102基于该体力消耗速率计算用户的当前总运动时间；
步骤S104：处理模块102基于该当前总运动时间估算用户的运动能力；
步骤S105：接收来自传感模块101的非生理传感器的运动数据；
步骤S106：处理模块102基于该运动数据计算用户的动能消耗；
步骤S107：处理模块102基于该动能消耗估算用户的平均耗氧量；
步骤S108：处理模块102基于该平均耗氧量和该运动能力估算用户的最大耗氧量；
步骤S109：将该最大耗氧量用处理模块102传送至用户界面103以供显示；
步骤S110：用户界面103显示该最大耗氧量。
在本发明的一个实施方式中，存储模块104可以包含多个对应于图2-5的线性回归模型、非线性回归模型、分段函数、其他数学模型或它们的任意组合， 用于处理模块102执行步骤S102-S104及S106-S107。
在本发明的一个实施方式中，应当注意的是，步骤S101-S104可以在步骤S105-S107之后进行。
在本发明的一个实施方式中，传感模块101可以包括至少一个用于感应和测量用户生理信号的生理传感器。例如，该生理信号包括下述信号中的至少一种：EKG信号、脉搏、心率、呼吸模式、糖原浓度、由脉搏血氧计得到的氧气浓度 (SpO2)、由组织血氧计得到的氧气浓度 (StO2) 和由人的额叶测得的氧气浓度。该组织血氧计可以是近红外光谱 (NIRS) 组织血氧计等。身体成分可以包括人体的脂肪、骨骼、水分和肌肉的百分比。
在本发明的至少一个实施方式中，非生理传感器能够获取包括各种类型运动参数的运动数据，例如运动位移、运动时间、跑步速度、骑行功率、攀爬高度等。该运动位移可以是跑步距离、攀爬高度、骑行距离等。例如，运动监测设备100可以包括运动传感器、骑行功率计、计步器或其他任何速度或速度相关的传感器作为传感模块101中的非生理传感器，记录相应的运动数据。可选地，用户可以将外部运动传感器、骑行功率计、计步器或其他任何速度或速度相关的传感器连接至运动监测设备100的传感模块101，用于记录运动数据并将其发送至处理模块102。该运动传感器包括加速度计、陀螺仪和磁强计中的至少一种。
在本发明的至少一个实施例中，动能消耗可以通过转换运动数据估算，例如将跑步速度转换为用功率（焦耳）表述的动能。例如，功率(t) = f (速度(t), t)，其中“功率(t)”为时刻t时的功率，“速度(t)”为时刻t时的速度。因此，动能消耗是速度的函数，其中动能消耗与运动速度正相关。此外，耗氧产生能量，因此一个人的平均耗氧量 (VO2) 可以用动能消耗估算。例如，VO2 = f (功率(t), t)，即，在运动中用户施加动能所需的平均耗氧量 (VO2) 在步骤S106中被估算。虽然在本实施例中使用速度作为参数，其他运动参数也可以被使用，如攀爬高度、循环功率等。
在本发明的一个实施方式中，生理传感器可以是检测各种生理参数的传感器的组合，例如由光学心率传感器得到的心率、由NIRS得到的氧气浓度等。相同的理念适用于本发明的任何实施例。因此，非生理传感器也可以是检测各种非生理参数的传感器的组合，例如由运动传感器得到加速度、由GPS得到位置、由温度计得到环境温度、由陀螺仪得到角加速度等。
图8为本发明的至少一实施方式所提供的估算下次总运动时间的方法流程图。
参照图8，以下步骤可以基于用户的生理数据、运动数据和默认位移估算用户下次总运动时间：
步骤S301：接收来自传感模块101的生理传感器的用户的生理数据；
步骤S302：处理模块102基于该生理数据估算用户的体力消耗速率；
步骤S303：处理模块102基于该体力消耗速率计算用户的当前总运动时间；
步骤S304：处理模块102基于该当前总运动时间估算用户的运动能力；
步骤S305：接收来自传感模块101的非生理传感器的运动数据；
步骤S306：处理模块102基于该运动数据计算用户的动能消耗；
步骤S307：处理模块102基于该动能消耗估算用户的平均耗氧量；
步骤S308：处理模块102基于该平均耗氧量和该运动能力估算用户的最大耗氧量；
步骤S309：处理模块102基于该最大耗氧量和默认位移估算下次总运动时间；
步骤S310：处理模块102传送该下次总运动时间至用户界面103以供显示；
步骤S311：用户界面103显示该下次总运动时间。
在本发明的一个实施方式中，存储模块104可以包含多个对应于图2-5的线性回归模型、非线性回归模型、分段函数、其他数学模型或它们的任意组合， 用于执行步骤S302-S204和S306-S307。
应当注意的是，步骤S301-S304可以在步骤S305-S307之后进行。
在本发明的一个实施例中，该下次总运动时间是基于用户的最大耗氧量对用户全力运动的表现进行估算的，然而事实上用户并未进行全力运动。例如，用户可以用运动监测设备100完成跑步5千米并得知他/她完成跑步10千米的最佳表现，而无需实际上完成10千米的跑步，无论该5千米的跑步是否是用他/她的最佳精力完成的。也即是说，用户可以轻松地完成运动但仍能够知道他/她并未进行的全力运动的表现。因此，估算下次总运动时间对于任何想要知道他/她的最佳表现又不用疲惫地完成全力运动的人特别有用。
在本发明的一个实施方式中，该默认的位移可以是保存于运动监测设备100的存储模块104的距离或一组距离，其中该距离或该一组距离可以从3千米、5千米、10千米、21千米、42千米等中选择。可选地，它可以被运动监测设备100的用户通过用户界面103的输入单元设定。应当注意的是，默认位移可以是运动的位移，如跑步距离、攀登高度、骑行距离等。
图9为本发明的至少一实施方式中体力水平与RPE之间对应关系的示意图。
参照图9，揭露了体力水平与自感劳累等级 (RPE) 的对应关系，其中RPE的类型可以是自感劳累程度 (Perceived Exertion Scale) 的博格评级 (Borg Rating)。当RPE值、血液乳酸浓度（血流中的乳酸浓度）或实时运动负荷下降时，体力水平恢复。另一方面，当疲劳程度、血液乳酸浓度（血流中的乳酸浓度）或实时运动负荷上升时，体力水平下降。
请再次参阅图9，体力水平可以在一定范围内被呈现，如100%到0%。此外，体力水平至少部分地与RPE的数值线性或非线性相关。例如，RPE值约为12表明运动监测设备100的用户进行的运动强度为中等水平。也即是说，用户可以体验到“轻微”的肌肉疲劳及呼吸比不做任何运动时加重，因此RPE值约为12可以对应于100%的体力水平。
另一方面，RPE值在15至17之间表明运动监测设备100的用户的运动强度为较高的水平。也即是说，用户可以体验到“僵硬/沉重”的肌肉疲劳及呼吸比不做任何运动时重得多，因此RPE值在15至17之间可以对应于0%的体力水平。
在本发明的另一实施方式中，以心率作为运动监测设备100的输入为例，RPE的数值与心率线性或非线性相关，因此体力水平也与心率线性或非线性相关。在另一实施方式中，每个用户的体力水平被标准化为对应于最大和最小心率的一个固定范围。
应当注意的是，体力水平可以为负值，如图9所示，其中体力水平为负值可以触发运动监测设备100自动调整根据用户运动强度的体力水平的估算，从而相应地校准体力水平。
图10为本发明的至少一实施方式所提供的估算最大耗氧量的方法流程图。
参照图10，以下步骤可以基于用户的RPE值和运动数据对用户的最大耗氧量进行估算：
步骤S501：接收来自用户界面103的输入单元的用户的RPE值；
步骤S502：处理模块102基于该RPE值估算用户的体力消耗速率；
步骤S503：处理模块102基于该体力消耗速率计算当前总运动时间；
步骤S504：处理模块102基于该当前总运动时间估算运动能力；
步骤S505：接收来自传感模块101的非生理传感器的运动数据；
步骤S506：处理模块102基于该运动数据计算动能消耗；
步骤S507：处理模块102基于该动能消耗估算平均耗氧量；
步骤S508：处理模块102基于该平均耗氧量和该运动能力估算最大耗氧量；
步骤S509：处理模块102传送该最大耗氧量至用户界面103以供显示；
步骤S510：用户界面103显示该最大耗氧量。
在本发明的一个实施方式中，存储模块104可以包含多个对应于图2-5的线性回归模型、非线性回归模型、分段函数、其他数学模型或它们的任意组合，用于执行步骤S502-S504 和S506-S507。
应当注意的是，步骤S501-S504 可以在步骤S505-S507 之后进行。
在本发明的一个实施方式中，图7的步骤S108、图8的步骤S308和图10的步骤S508中，估算最大耗氧量可通过一运动能力和平均耗氧量的函数完成，其中运动能力与最大耗氧量负相关，平均耗氧量与最大耗氧量正相关。例如，VO2Max = f (VO2, FVO2), 其中VO2Max为最大耗氧量，VO2为平均耗氧量，FVO2为运动能力。相同的估算最大耗氧量的理念可以被用于本发明的任何其他实施方式。
图11为本发明的至少一个实施方式所提供的估算下次总运动时间的方法流程图。
参照图11，以下步骤可以基于用户的生理数据和运动数据对用户的最大耗氧量进行估算：
步骤S701：从一终端设备接收历史运动模式，其中该历史运动模式包括一组心率和一组对应于心率的位移；
步骤S702：基于该组心率计算一组心率百分比；
步骤S703：基于该组位移计算一组速度；
步骤S704：基于该组心率百分比和该组速度估算最大耗氧量，其中该最大耗氧量与该组心率百分比负相关，与该组速度正相关；
步骤S705：基于一默认位移和该最大耗氧量估算下次总运动时间，该下次总运动时间与该默认位移正相关，该下次总运动时间与该最大耗氧量负相关；
步骤S706：接收来自该终端设备的环境条件；
步骤S707：用该环境条件校准该下次总运动时间以生成特定环境的总运动时间；
步骤S708：生成一个包括该最大耗氧量、该下次总运动时间和该特定环境的总运动时间的数据阵列；
步骤S709：将该数据阵列传送至该终端设备。
在本发明的一个实施方式中，步骤S702和S703的顺序是可以互换的。
在本发明的一个实施方式中，该环境条件可能影响一个人的运动表现，其中该环境条件可以是陡度、高度、大气压力、风速和环境温度等。这些环境条件可以通过各种非生理传感器获取，如环境温度传感器、风速计、GPS传感器、水平传感器和大气压力传感器等。例如，当一个人跑上斜坡时跑步速度会大大减慢。应当注意的是，步骤S707中校准下次总运动时间也可以被用于图7、图8和图10中所示的方法。
在本发明的一个实施例中，该心率百分比是将该组心率中的最高心率作为分母、每个心率作为分子计算的。
在本发明的一个实施方式中，一个人做运动时的心率百分比应该约为60%或之上。如果步骤S702计算得到的任何心率百分比低于60%，这个人会被认为没有在运动，因此在步骤S704中该低于60%的心率百分比可能从估算最大耗氧量中被省略。应当理解的是，该60%的数值被用于举例，每个人被认为是运动状态的心率百分比都可能是不同的。因此，该被认为是运动状态的心率百分比在运动监测设备100中可以是定制的。
在本发明的一个实施方式中，该数据阵列可以包括步骤S704估算的最大耗氧量、步骤S705估算的下次总运动时间和步骤S707校正的特定环境的总运动时间。此外，该数据阵列可以包括设定的默认位移如3千米、5千米、10千米、21千米、42千米等。每个默认位移可对应于步骤S705估算的一下次总运动时间和步骤S707校正的一特定环境的总运动时间。此外，该数据阵列也可以包括对热身最大耗氧量的建议，其中建议的热身最大耗氧量可以被用于确定运动前的热身是否足够。例如，通过图11中所示的方法用户可以得知该建议的热身最大耗氧量。进行热身时，戴有运动监测设备100的用户可以从用户界面103实时地读取该用户的最大耗氧量。由此，在热身进行中，用户可以通过实时的最大耗氧量达到该建议的热身最大耗氧量知道自己已为运动做好准备。
在本发明的一个实施方式中，该终端设备可以是运动监测设备100。因此，所述心率由该运动监测设备100的生理传感器采集，所述位移由该运动监测设备100的非生理传感器采集。可选地，该终端设备可以是任何其它具有生理传感器和非生理传感器的监测设备。例如，具有心率传感器和GPS传感器的便携式设备。此外，该运动监测设备100可包括输出插口（未示出），其中该输出插口能够使该运动监测设备100通过传输线发送步骤S701的历史运动数据或接收步骤S709的数据阵列。可选地，该运动监测设备100也可以包括无线通讯模块（未示出），其中该无线通讯模块能够使该运动监测设备100无线地发送步骤S701的历史运动数据或接收步骤S709的数据阵列。
在本发明的另一实施方式中，该终端设备可以是具有用户输入功能的电脑设备，所述心率和位移被用户输入至该终端设备。可选地，该心率和位移可以由任何其它监测设备无线或有线地发送到该电脑设备。该电脑设备可以是个人电脑、笔记本电脑、平板电脑、移动设备等。
在本发明的一个实施方式中，各种生理参数可作为附加因子用于估算最大耗氧量，如心率变化、体温、身体组成、血糖、血压等。该身体组成可包括人体内脂肪、骨骼、水和肌肉的百分比。因此，运动监测设备100的用户可以个性化最大耗氧量的估算。
应当注意的是，当任何其它附加运动因素被加入估算时，上述正相关或负相关不是一直正确的。例如一个具有优异跑步技能的人，该特征可作为一附加运动因素，当保持相同的最大耗氧量时可增加位移。
图12为本发明的至少一个实施方式所提供的具有心率传感器和GPS的运动监测设备的示意性框图。
运动监测设备100包括传感模块101、处理模块102、用户界面103和存储模块104，其中，对比于图1，该传感模块101进一步包括心率传感器201和GPS 202。
在本发明的一个实施方式中，该心率传感器201可用于记录一个人的心率变化并将它作为生理数据发送至该处理模块102。该GPS 202可记录一个人的坐标以获取这个人的位移及速度，该GPS可将速度作为运动数据发送至该处理模块102。该处理模块102可执行存储于存储模块104的如前所示的方法中的至少一种，如图7所示的方法。
在本发明的一个实施方式中，该心率传感器201包括至少两个电极（未示出），其中该至少两个电极与用户的皮肤电性连接以检测用户的心率。
在本发明的一个实施方式中，该运动监测设备100还可以包括模拟前端（未示出），如AFE，其中心率传感器201可通过该模拟前端发送模拟信号至处理模块102，其中该模拟前端可以但不仅限于是Analog Devicede1公司的AD8232，德州仪器 (Texas Instruments) 公司的ADS1191。
本发明的一个实施方式中，该心率传感器201包括至少一个光源（未示出），其中该心率传感器201是检测用户心率的光学心率传感器。
本发明的一个实施方式中，该GPS202可以但不仅限于是CSR (Cambridge Silicon Radio) 公司的 SiRFstarV 5E，U-Blox公司的EVA-M8M。
图13为本发明的至少一个实施方式所提供的运动监测设备100的外部传感模块101和外部用户界面103的示意性框图。
参照图13，根据本发明的一个实施方式，运动监测设备100可以只包括处理模块102和存储模块104。在这种情况下，传感模块101可以是能够与运动监测设备100无线通讯的的可穿戴设备，从而通过无线通讯发送生理数据（如心率）和运动数据（速度）至处理模块102。相同的理念也可以被用于用户界面103，其中该用户界面103可由移动设备或便携式设备实现。例如，该运动监测设备100的用户可以基于从传感模块101无线接收的所述心率和速度估算最大耗氧量，并将该最大耗氧量实时地发送至该用户的移动电话，其中该移动电话可以显示该最大耗氧量。
图14为图11中产生的数据阵列的示例。
参照图14，其中示例的数据阵列包括最大耗氧量、下次总运动时间及特定环境的总运动时间。此外，该数据阵列可以包括但不仅限于一组默认位移，如3千米、5千米、10千米、21千米、42千米等。并且，每个默认位移可对应于下次总运动时间和特定环境的总运动时间。此外，该数据阵列也可以包括对热身最大耗氧量的建议。可选地，该默认位移可以被运动监测设备100的用户通过用户界面103的输入单元设定。应当注意的是，默认位移可以是运动的位移，如跑步距离、攀登高度、骑行距离等。
以上描述的仅为本发明的实施方式，并不旨在限制本发明的范围。根据本发明所揭露的内容的各种变化和替代方式也包括在本发明的范围内。此外，每个实施方式和权利要求并不一定要满足所揭露的所有优点或特性。而且，摘要和标题仅用于便于检索专利文件而并不旨在一任何方式限缩本发明的的范围。

Claims (30)

  1. 一种最大耗氧量的估算方法，包括：
    接收来自第一传感器的生理数据；
    处理模块基于所述生理数据估算体力消耗速率；
    所述处理模块基于所述体力消耗速率计算当前总运动时间；
    所述处理模块基于所述当前总运动时间估算运动能力；
    接收来自第二传感器的运动数据；
    所述处理模块基于所述运动数据计算动能消耗；
    所述处理模块基于所述动能消耗估算平均耗氧量；
    所述处理模块基于所述平均耗氧量和所述运动能力估算所述最大耗氧量；
    传送所述最大耗氧量至用户界面；
    所述用户界面显示所述最大耗氧量。
  2. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第一传感器为心率传感器，所述生理数据为心率。
  3. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第一传感器包括至少两个电极。
  4. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第一传感器包括光学心率传感器。
  5. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第一传感器包括光学氧气浓度传感器。
  6. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第二传感器包括GPS传感器，所述运动数据为速度。
  7. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第二传感器包括骑行功率计。
  8. 如权利要求1所述的最大耗氧量的估算方法，其特征在于，所述第二传感器包括运动传感器。
  9. 如权利要求8所述的最大耗氧量的估算方法，其特征在于，所述第二传感器还包括陀螺仪。
  10. 一种下次总运动时间的估算方法，包括：
    接收来自第一传感器的生理数据；
    处理模块基于所述生理数据估算体力消耗速率；
    所述处理模块基于所述体力消耗速率计算当前总运动时间；
    所述处理模块基于所述当前总运动时间估算运动能力；
    接收来自第二传感器的运动数据；
    所述处理模块基于所述运动数据计算动能消耗；
    所述处理模块基于所述动能消耗估算平均耗氧量；
    所述处理模块基于所述平均耗氧量和所述运动能力估算最大耗氧量；
    所述处理模块基于所述最大耗氧量和默认位移估算所述下次总运动时间；
    传送所述下次总运动时间至用户界面；
    所述用户界面显示所述下次总运动时间。
  11. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第一传感器为心率传感器，所述生理数据为心率。
  12. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第一传感器包括至少两个电极。
  13. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第一传感器包括光学心率传感器。
  14. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第一传感器包括光学氧气浓度传感器。
  15. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第二传感器包括GPS传感器，所述运动数据为速度。
  16. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第二传感器包括骑行功率计。
  17. 如权利要求10所述的下次总运动时间的估算方法，其特征在于，所述第二传感器包括运动传感器。
  18. 如权利要求17所述的下次总运动时间的估算方法，其特征在于，所述第二传感器还包括陀螺仪。
  19. 一种下次总运动时间的估算方法，包括：
    接收来自终端设备的历史运动模式，其中所述历史运动模式包括一组心率和对应于所述一组心率的一组位移；
    基于所述一组心率计算一组心率的百分比；
    基于所述一组位移计算一组速度；
    基于所述一组心率的百分比和所述一组速度估算最大耗氧量，其中所述最大耗氧量与所述一组心率的百分比负相关并与所述一组速度正相关；
    基于默认位移和所述最大耗氧量估算下次总运动时间，其中所述下次总运动时间与所述默认位移正相关，所述下次总运动时间与所述最大耗氧量负相关；
    生成一个包括所述最大耗氧量和所述下次总运动时间的数据阵列；
    将所述数据阵列传送至所述终端设备。
  20. 如权利要求19所述的下次总运动时间的估算方法，进一步包括：
    接收来自所述终端设备的环境条件；
    用所述环境条件校准所述下次总运动时间以生成特定环境的总运动时间；
    其中，所述数据阵列进一步包括所述特定环境的总运动时间。
  21. 如权利要求20所述的下次总运动时间的估算方法，其特征在于，所述环境条件通过GPS获取。
  22. 如权利要求20所述的下次总运动时间的估算方法，其特征在于，所述环境条件为环境温度。
  23. 如权利要求19所述的下次总运动时间的估算方法，其特征在于，所述心率采集于生理传感器。
  24. 如权利要求23所述的下次总运动时间的估算方法，其特征在于，所述生理传感器为包括至少两个电极的心率传感器。
  25. 如权利要求23所述的下次总运动时间的估算方法，其特征在于，所述生理传感器为光学心率传感器。
  26. 如权利要求19所述的下次总运动时间的估算方法，其特征在于，所述一组位移采集于非生理传感器。
  27. 如权利要求26所述的下次总运动时间的估算方法，其特征在于，所述非生理传感器为GPS。
  28. 如权利要求26所述的下次总运动时间的估算方法，其特征在于，所述非生理传感器为运动传感器。
  29. 如权利要求19所述的下次总运动时间的估算方法，其特征在于，通过无线传输从所述终端设备接收所述历史运动模式。
  30. 如权利要求19所述的下次总运动时间的估算方法，其特征在于，通过有线传输从所述终端设备接收所述历史运动模式。
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