Patent Application
20170258367
The present invention relates generally to an exercise monitoring method and device. More specifically, the present invention relates to an exercise monitoring method and device utilizing stamina of a user for monitoring a real-time maximal oxygen consumption of a user and/or predicting a result of future exercise of the user.
Doing exercise has become more and more popular in modern societies. Other than professional athletes, many individuals not only doing exercise for health considerations, but also seek to know how well do they perform during the exercise. Traditionally, this could only be achieved accurately by having fitness tests in a laboratory with various testing equipment. Alternatively, individuals can hire personal fitness coach to assist themselves understanding their own performance.
Recently, various types of exercise performance evaluation have been rapidly developed and applied by professional athletes, sports enthusiasts, fitness coach, or even individuals. Among all sorts of exercise performance evaluation, maximal oxygen consumption is widely used and proven to be effective. However, the maximal oxygen consumption of any individuals cannot be easily assessed without spending an hour on a treadmill in a laboratory with a mouthpiece which receives the individuals' breathing in order to monitor and analyze changes in breathing rate, oxygen consumption, carbon dioxide production, etc. Therefore, it is not convenient to obtain an individual's exercise performance while doing daily exercise practices or training.
In view of the above, what is needed is an exercise monitoring device and method which reliably indicates a maximal oxygen consumption of any user while doing exercise anywhere under a real-time basis without wearing a mouthpiece in the laboratory.
The accompanying drawings illustrate one or more embodiments of the invention and together with the written description, serve to explain the principles of the invention. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like elements of an embodiment, and wherein:
In accordance with common practice, the various described features are not drawn to scale and are drawn to emphasize features relevant to the present disclosure. Like reference characters denote like elements throughout the figures and text.
The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown. This invention can, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like reference numerals refer to like elements throughout.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” or “includes” and/or “including” or “has” and/or “having” when used herein, specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood that the term “and/or” includes any and all combinations of one or more of the associated listed items. It will also be understood that, although the terms first, second, third etc. can be used herein to describe various elements, components, regions, parts and/or sections, these elements, components, regions, parts and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, part or section from another element, component, region, layer or section. Thus, a first element, component, region, part or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the present invention.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The description will be made as to the embodiments of the present invention in conjunction with the accompanying drawings in
The exercise monitoring device 100 comprises a sensor module 101, a processing module 102, a user interface 103, and a storage module 104.
In one embodiment of the present invention, the sensor module 101 can comprise at least one physiological sensor for sensing and measuring physiological signals of a user. For example, the physiological signal comprises at least one of the following: EKG signal, pulse, heart rate, breathing pattern, glycogen concentration, oxygen concentration (SpO2) from pulse oximeter, oxygen concentration (StO2) from tissue oximeter, and oxygen concentration measured from front lobe of a person. The tissue oximeter can be Near Infra-Red Spectroscopy tissue oximeter, etc. The oxygen concentration at front lobe is correlated to RPE of a person.
In another embodiment of the present invention, the sensor module 101 can comprise a plurality of sensors for sensing and measuring both physiological signals of a user like mentioned before and non-physiological signals. In terms of non-physiological signals, the sensor module 101 can comprise various types of non-physiological sensors such as pedometer, speedometer, accelerometer, gyroscope, G-sensor, etc.
In at least one embodiment of the present invention, the processing module 102 is a hardware such as a processor, a microcontroller or a microprocessor with auxiliary circuits that carries out instructions of a computer program by performing the basic arithmetical, logical, and input/output operations of the exercise monitoring device. Many different products on the market can be used as the processing module 102 such as for example but not limited to nRF52832 from Nordic Semiconductor, STM32L476 from STMicroelectronics.
In one embodiment of the present invention, the user interface 103 comprises 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 component or a speaker, or any combination thereof for stating the user's physiological status during the exercise or after the exercise, wherein the physiological status can comprise at least one of the following: measurement of physiological signal, stamina level, kinetic energy consumption, maximal oxygen consumption, etc. The input can be any human-machine interface such as a touch-panel, a voice receiver or a button that is capable of receiving biological information from the user, such as height, weight, age, gender and so forth. In addition, the user interface 103 can be adapted to send information directly to the sensor module 101, the processing module 102 or the storage module 104. The inputted information can be processed by the processing module 102 and sent to the output for the user to know the user's current body condition. For example, BMI value, etc.
In one embodiment of the present invention, the storage module 104 can be any type of volatile or non-volatile memory for storing instructions of a computer program to be carried out by the processing module 102, biological information inputted by the user using the user interface 103, and exercise information from the sensor module 101 and/or the processing module 102.
It should be noticed that the term, stamina, refers to the ability of a user to exert himself/herself and remain active for a period of time. The less the stamina of a person, the less time the person can continue exercising providing the same exercise intensity without rest.
Referring to
It is well known in the art that a lactic acid concentration of 4 mmol per liter is considered as a threshold between aerobic exercise and anaerobic exercise. With aerobic exercise, oxygen is carried through the user's breath to the muscles giving muscles the energy needed to sustain the effort. With anaerobic exercise, the exercise intensity is high enough to trigger lactic acid formation, which causes discomfort and fatigue at sustained levels.
Referring to
It should be noticed that although the aforementioned heart rate is disclosed for mapping and normalizing with the stamina level in
In
In
Referring to
S101: Receiving a physiological data of the user from a physiological sensor of the sensor module 101;
S102: Estimating a rate of stamina consumption of the user base on the physiological data by the processing module 102;
S103: Calculating an all-out exercise time of the user base on the rate of stamina consumption by the processing module 102;
S104: Estimating an exercise capability of the user base on the all-out exercise time by the processing module 102;
S105: Receiving an exercise data from a non-physiological sensor of the sensor module 101;
S106: Calculating a kinetic energy exertion of the user base on the exercise data by the processing module 102;
S107: Estimating an average oxygen consumption of the user base on the kinetic energy exertion by the processing module 102;
S108: Estimating a maximal oxygen consumption of the user base on the average oxygen consumption and the exercise capability by the processing module 102
S109: Sending the maximal oxygen consumption by the processing module 102 to the user interface 103 for display.
S110: Displaying the maximal oxygen consumption by the user interface 103.
In one embodiment of the present invention, the storage module 104 can comprise a plurality of linear regression models, non-linear regression models, piecewise functions, other mathematical models or any combination thereof, corresponding to
In one embodiment of the present invention, it should also be noticed that the steps S101-S104 can be carried out after S105-S107.
In one embodiment of the present invention, the sensor module 101 can comprise at least one physiological sensor for sensing and measuring physiological signals of a user. For example, the physiological signal comprises at least one of the following: EKG signal, pulse, heart rate, breathing pattern, glycogen concentration, oxygen concentration (SpO2) from pulse oximeter, oxygen concentration (StO2) from tissue oximeter, and oxygen concentration measured from front lobe of a person. The tissue oximeter can be Near Infra-Red Spectroscopy tissue oximeter, etc. The body composition can comprise percentages of fat, bone, water and muscle in human bodies.
In at least one embodiment of the present invention, the non-physiological sensor which can obtain the exercise data that can comprise various types of exercise parameters, such as displacement of exercise, time used for exercise, speed of running, cycling power, altitude of climbing, etc. The displacement of exercise can be a running distance, a climbing altitude, a cycling distance, etc. For example, the exercise monitoring device 100 can comprise a motion sensor, cycling power meter, pedometer, or any other speed or speed related sensors as the non-physiological sensor in the sensor module 101 to record the exercise data accordingly. Alternatively, a user can connect an external motion sensor, cycling power meter, pedometer, or any other speed or speed related sensors to the sensor module 101 of the exercise monitoring device 100 in order to record the exercise data and send to the processing module 102. The motion sensor comprises at least one of an accelerometer, a gyroscope, and a magnetometer.
In at least one embodiment of the present invention, the kinetic energy exertion is estimated by converting the exercise data such as running speed to kinetic energy in term of power (Joules). For example, Power(t)=f(Speed(t), t). Therefore, the kinetic energy exertion is a function of speed, wherein the kinetic energy exertion is positively correlated to the speed of exercise. Moreover, consuming oxygen gives energy, so one's average oxygen consumption (VO2) can be estimated by kinetic energy exertion. For example, VO2=f (Power(t), t). Namely, average oxygen consumption (VO2) needed by the user to exert the kinetic energy is estimated in step S106 during exercise. Although speed is used in the example, other exercise parameters can be used, such as altitude of climbing, cycling power, etc.
In one embodiment of the present invention, the physiological sensor can be a combination of sensors which detects various physiological parameters, such as heart rate from optical heart rate sensor, oxygen concentration from NIRS, etc. The same concept is applicable in any embodiments of the present invention. Thus, the non-physiological sensors can also be a combination of sensors which detects various non-physiological parameters, such as acceleration from motion sensor, location from GPS, ambient temperature from thermometer, angular acceleration from gyroscope, etc.
Referring to
S301: Receiving a physiological data of the user from a physiological sensor of the sensor module 101;
S302: Estimating a rate of stamina consumption of the user base on the physiological data by the processing module 102;
S303: Calculating an all-out exercise time of the user base on the rate of stamina consumption by the processing module 102;
S304: Estimating an exercise capability of the user base on the all-out exercise time by the processing module 102;
S305: Receiving an exercise data from a non-physiological sensor of the sensor module 101;
S306: Calculating a kinetic energy exertion of the user base on the exercise data by the processing module 102
S307: Estimating an average oxygen consumption of the user base on the kinetic energy exertion by the processing module 102;
S308: Estimating a maximal oxygen consumption of the user base on the average oxygen consumption and the exercise capability by the processing module 102
S309: Estimating a future total exercise time base on the maximal oxygen consumption and a default displacement by the processing module 102;
S310: Sending the future total exercise time by the processing module 102 to the user interface 103 for display;
S311: Displaying the future total exercise time by the user interface 103.
In one embodiment of the present invention, the storage module 104 can comprise a plurality of linear regression models, non-linear regression models, piecewise functions, other mathematical models or any combination thereof, corresponding to
It should be noticed that the steps S301-S304 can be carried out after S305-S307.
In one embodiment of the present invention, the future total exercise time is an estimation of the user's performance of an all-out exercise base on the user's maximal oxygen consumption while the user not doing the all-out exercise for real. For example, the user can complete a 5 km running with the exercise monitoring device 100 and know his/her best performance to complete a 10 km running without actually completing a 10 km running whether or not the 5 km running is completed with his/her best effort. Namely, the user can complete an exercise in ease but still being able to understand his/her performance of an all-out exercise which has not happened yet. Thus, estimation of future total exercise time is particularly useful to anyone wants to know his/her best performance without getting exhausted to complete an all-out exercise.
In one embodiment of the present invention, the default displacement can be a distance or a set of distances saved in the storage module 104 of the exercise monitoring device 100, wherein the distance or the set of distances can be chosen from 3 km, 5 km, 10 km, 21 km, 42 km, etc. Alternatively, it can be defined by the user of the exercise monitoring device 100 using the input unit of the user interface 103. It should be noticed that, the default displacement can be displacement of exercise such as a running distance, a climbing altitude, a cycling distance, etc.
Referring to
Continues with
On the other hand, a RPE between 15 and 17 suggests that exercise intensity is being performed at a much higher level by the user of the exercise monitoring device 100. That is to say, the user can experience “hard/heavy” muscle fatigue or breathing much heavier than not doing any exercise, and thus a RPE between 15 and 17 can correspond to a 0% stamina level.
In another embodiment of the present invention, in a perspective that takes heart signal as input of the exercise monitoring device 100 as an example, the RPE scale is linearly or nonlinearly correlative to the heart rate, and thus the stamina level is also linearly or nonlinearly correlative to the heart rate. In another example, the stamina level of each user is normalized to a fixed range according to the maximum and minimum heart rate.
It should be noticed that the stamina level can be a negative value as shown in
Referring to
S501: Receiving a RPE value of the user from an input unit of the user interface 103;
S502: Estimating a rate of stamina consumption base on the RPE value by the processing module 102;
S503: Calculating an all-out exercise time base on the rate of stamina consumption by the processing module 102;
S504: Estimating an exercise capability base on the all-out exercise time by the processing module 102;
S505: Receiving an exercise data from a non-physiological sensor of the sensor module 101;
S506: Calculating a kinetic energy exertion of the user base on the exercise data by the processing module 102
S507: Estimating an average oxygen consumption of the user base on the kinetic energy exertion by the processing module 102;
S508: Estimating a maximal oxygen consumption base on the average oxygen consumption and the exercise capability by the processing module 102
S509: Sending the maximal oxygen consumption by the processing module 102 to the user interface 103 for display.
S510: Displaying the maximal oxygen consumption by the user interface 103.
In one embodiment of the present invention, the storage module 104 can comprise a plurality of linear regression models, non-linear regression models, piecewise functions, other mathematical models or any combination thereof, corresponding to
It should be noticed that the steps S501-S504 can be carried out after S505-S507.
In one embodiment of the present invention, estimation of maximal oxygen consumption in the step S108 in
Referring to
S701: Receiving a historical exercise model from a terminal device, wherein the historical exercise model comprises a plurality of heart rate and a plurality of displacement corresponding to the heart rate;
S702: Calculating a plurality of heart rate percentage based on the plurality of heart rate;
S703: Calculating a plurality of speed based on the plurality of displacement;
S704: Estimating a maximal oxygen consumption based on the plurality of heart rate percentage and the plurality of speed, wherein the maximal oxygen consumption is negative correlated to the plurality of heart rate percentage and is positive correlated to the plurality of speed;
S705: Estimating a future total exercise time based on a default displacement and the maximal oxygen consumption, wherein the future total exercise time is positive correlated to the default displacement, and wherein the future total exercise time is negative correlated to the maximal oxygen consumption;
S706: Receiving an environmental condition from the terminal device;
S707: Calibrating the future total exercise time by the environmental condition to generate an environment specific total exercise time;
S708: Generating a data array comprising the maximal oxygen consumption, the future total exercise time, and the environmental specific total exercise time;
S709: Sending the data array to the terminal device.
In one embodiment of the present invention, the order of step S702 and S703 are interchangeable.
In one embodiment of the present invention, the environmental condition can affect a person's exercise performance, wherein the environmental condition can be steepness, altitude, atmosphere pressure, wind speed, ambient temperature, etc. These environmental conditions can be obtained by various non-physiological sensors such as ambient temperature sensor, anemometer, GPS sensor, level sensor, atmosphere pressure sensor, etc. For example, a person's running speed can be greatly reduced while the person is running upwardly on a slope. It should be noticed that, the calibration of future total exercise time in step S707 can also be applied to the methods in
In one embodiment of the present invention, the heart rate percentage is calculated by taking the highest heart rate from the plurality of heart rate as denominator and each of the heart rate as numerator.
In one embodiment of the present invention, the heart rate percentage of a person while doing exercise should be around 60% or above. If any heart rate percentage calculated in step S702 is below 60%, the person may be considered not exercising, so the particular heart rate percentage below 60% may be omitted from the estimation of maximal oxygen consumption in step S704. It should be understood that the 60% was used as an example, the heart rate percentage of a person considered to be exercising may be different from one to another. Therefore, the heart rate percentage considering exercising may be customizable in the exercise monitoring device 100.
In one embodiment of the present invention, the data array can comprise the maximal oxygen consumption estimated in step S704, the future total exercise time estimated in S705, the environmental specific total exercise time calibrated in step S707. Furthermore, the data array can comprise at set of default displacements such as 3 km, 5 km, 10 km, 21 km, 42 km, etc. And, each of the default displacement may be corresponding to a future total exercise time estimated in step S705 and an environmental specific total exercise time calibrated in step S707. In additional, the data array can also comprise a suggestion of warm up maximal oxygen consumption, wherein the suggested warm up maximal oxygen consumption can be used to determine whether warm up before exercise is enough or not. For example, a user can know the suggested warm up maximal oxygen consumption by the method in
In one embodiment of the present invention, the terminal device can be the exercise monitoring device 100. Therefore, the heart rate is collected from the physiological sensor of the exercise monitoring device 100 and the displacement is collected from the non-physiological sensor of the exercise monitoring device 100. Alternatively, the terminal device can be any other monitoring device with both physiological sensor and non-physiological sensor. For example, a portable device with a heart rate sensor and a GPS sensor. Furthermore, the exercise monitoring device 100 can comprise an output socket (not shown), wherein the output socket enable the exercise monitoring device 100 to send the historical exercise data in step S701 or receive the data array in step S709 via a wire. Alternatively, the exercise monitoring device 100 can also comprise a wireless communication module (not shown), wherein the wireless communication module enables the exercise monitoring device 100 to send the historical exercise data in step S701 or receive the data array in step S709 wirelessly.
In another embodiment of the present invention, the terminal device can be a computing device with user input function, so the heart rate and the displacement are inputted by a user into the terminal device. Alternatively, the heart rate and the displacement can be transmitted from any other monitoring device into the computing device either wirelessly or with wire. The computing device can be such as personal computer, laptop, tablet pc, mobile device, etc.
In one embodiment of the present invention, various physiological parameters can be additional factors in the estimation of maximal oxygen consumption, such as heart rate variability, body temperature, body composition, blood glucose, blood pressure, etc. The body composition can comprise percentages of fat, bone, water and muscle in human bodies. Therefore, the estimation of maximal oxygen consumption can be personalized by the user of exercise monitoring device 100.
It should be noticed that, the aforementioned positive correlation or negative correlation cannot always be true when any other additional exercising factors comes into the estimation, for example a person with excellent running skill, which is an additional exercising factor, can increase displacement while maintaining the same maximal oxygen consumption.
The exercise monitoring device 100 comprises a sensor module 101, a processing module 102, a user interface 103, and a storage module 104, wherein the sensor module 101 further comprises a heart rate sensor 201 and a GPS 202 in comparison to
In one embodiment of the present invention, the heart rate sensor 201 can be used to record heart rate of a person and send it to the processing module 102 as physiological data. The GPS 202 can record a person's coordination in order to obtain the person's displacement and thus speed, and the GPS can send the speed as exercise data to the processing module. The processing module 102 can carry out at least one of the methods which is stored in the storage module 104 as shown previously, for example the method in
In one embodiment of the present invention, the heart rate sensor 201 comprises at least two electrodes (not shown), wherein the at least two electrodes is electrically connected with the user's skin to detect the user's heart rate.
In one embodiment of the present invention, the exercise monitoring device 100 can further comprise an analog front-end (not shown), as known as AFE, wherein the heart rate sensor 201 can send analog signals to the processing module 102 via the analog front-end, and wherein the analog front-end can be for example but not limited to AD8232 from Analog Devices, ADS1191 from Texas Instruments.
In one embodiment of the present invention, the heart rate sensor 201 comprises at least a light source (not shown), wherein the heart rate sensor 201 is an optical heart rate sensor that detects the user's heart rate.
In one embodiment of the present invention, the GPS 202 can be for example but not limited to SiRFstarV 5E from CSR, EVA-M8M from U-Blox.
Referring to
Referring to
Previous descriptions are only embodiments of the present invention and are not intended to limit the scope of the present invention. Many variations and modifications according to the claims and specification of the disclosure are still within the scope of the claimed invention. In addition, each of the embodiments and claims does not have to achieve all the advantages or characteristics disclosed. Moreover, the abstract and the title only serve to facilitate searching patent documents and are not intended in any way to limit the scope of the claimed invention.
