FI121214B - Procedure, apparatus and computer software for observing the physiological state of a person - Google Patents

Description

A method, apparatus, and computer program product for monitoring a person's physiological condition

The invention relates to the measurement and evaluation of physiological functions. In particular, the invention relates to a method for estimating energy consumption and implementing a method for apparatus and computer program product.

One method for estimating energy consumption is known from US 6537227. The method utilizes a reference parameter describing a person's heart rate and a predetermined person's performance on the basis of which the energy consumption during exercise is estimated. The calculation requires information on a person's maximum oxygen consumption (V02max). Precise estimation of this before execution is not simple and erroneous estimation may result in a large calculation error.

Another method of calculation is known from WO 2003099114, which calculates the human-15 density by means of a frequency conversion and a modulation generated by the neural network.

This kind of computation requires a lot of computing and memory capacity, which increases the power consumption of the executing device, the aggregator cost. Further, the energy consumption calculation based on respiratory rate calculated through frequency conversion requires the disclosure or estimation of a person's respiratory volume value or equivalent personal physiological quantity, which must be separately measured to provide an accurate energy consumption estimate, e.g.

WO 2004/073494 describes a method for measuring energy consumption using data from an accelerometer and a heart rate monitor. U.S. Patent Publication 2005/054938 discloses a method for further utilizing the information provided by an altimeter.

The object of the invention is to provide a novel method for monitoring the physiological state of a person, in particular energy consumption, during exercise. In particular, it is an object of the invention to provide a method which is feasible with a lower computational capacity than the known solutions.

It is a further object of the invention to provide a new portable device for monitoring a person's performance status and a new computer program product.

2

The invention is based on the finding that respiratory rate and / or other respiratory parameters can be derived directly from the heart rate signal from the time periodicity of heart rate interval variation (heart rate interval noise).

In the method of the invention, a person's heart rate is measured over time to obtain or receive a heart rate signal comprising successive heart rate periods, and based on the periodicity of heart rate noise, directly at least one parameter describing a person's breathing and further energy consumption is determined.

The device for monitoring the physiological condition of a person according to the invention comprises means for measuring heart rate in time to detect sequential heart rate periods or means for receiving such a heart rate signal. The device further comprises means for determining at least one respiratory parameter of the person over time based on the periodicity of the heart rate interval noise of the heart rate signal and further means for calculating the person's energy consumption. 15

The computer program product of the invention for determining a physiological condition of a person is adapted to receive measurement data of a person's heart rate and determine at least one parameter describing a person's breathing by time stamps based on heart rate observations and further energy consumption by the person.

More specifically, the method according to the invention is characterized by what is said in the characterizing part of claim 1.

The device according to the invention is characterized by what is said in the characterizing part of claim 12.

The computer program product according to the invention is characterized by what is said in the characterizing part of claim 23.

30

The invention provides considerable advantages. The advantage of time-domain determination over frequency-domain analysis is that it requires less computation. Thus, the computation is fast and can be performed with a small amount of processor and program memory, thus also reducing power consumption and making the device more economical. Low power consumption further extends the life of the device and / or enables the use of smaller batteries. Thus, it is well suited for portable devices such as wrist computers. The known methods based on frequency analysis typically use Fourier transform, which makes computation complex and computationally demanding.

5

By using the invention, it is also possible to obtain a sufficiently accurate estimate of the energy consumption during exercise without knowing the maximum amount of oxygen or energy consumption of a person. Thus, the user does not have to perform a stress test or similar test on the user's metabolism before personal energy consumption monitoring can begin. Experiments 10 show that an average error of up to 15% can be achieved in estimating energy consumption without knowing the person's actual maximum oxygen consumption. The accuracy of the method is based on the successful determination of the respiratory rate.

Also, the method does not require calculation of the instantaneous or average heart rate, but by labeling the heart rate described in more detail below, the contribution of the breath to the heart rate signal can be determined directly.

By heart rate, we mean the period when the heart is actually beating, and when, for example, an electronically measured heart rate signal has a strong oscillation of the heart signal caused by a heart beat. By heart rate, we mean again the time between two consecutive heart rate periods. During this time there is variation (noise) which is mainly influenced by breathing. The periodic contribution of respiration to heart rate noise can be distinguished by the method of the invention.

It is characteristic of a time domain determination that the periodicity of the heart rate interval noise is detected without coordinate conversion, for example to the frequency domain. The time domain determination can be made by collecting time stamps based on the identified heart rate periods by signal analysis to detect the periodicity of the time-lapse sequence.

30 By the parameter describing a person's breathing we mean primarily the respiratory rate. However, by utilizing the basic idea of the invention in conjunction with preprogramming parameters that describe a person, it is also possible, for example, to determine the amount of ventilation.

Various embodiments of the invention will now be described with reference to the accompanying drawing.

5 4

Figure 1 is a flowchart showing one embodiment of the present method; Figure 2 is a schematic drawing of one embodiment of the present device; Figure 3 illustrates by way of example a portion of a cardiac signal.

Figure 1 shows one possible way to carry out the method according to the invention. Measurement of the Sy central signal 101 is typically performed electrically by a heart rate sensor, for example, by a heart rate belt attached to the chest or by separate electrodes applied to the skin. The heart rate signal is obtained by analyzing the heart signal to identify heart beats from the signal. The Sy-10 signal is illustrated in Figure 3, with reference numeral 32 denoting a heart rate period and a rest period between heart rate periods by reference numeral 34. Preferably, the heart rate periods identify a well-defined point in time, such as maximum or zero signal 36. Any method well known in the art may be used for identification. In Figure 3, the heart rate interval determined by these points is indicated by reference numeral 38.

15

The heart rate signal is transmitted in step 102 from the heart rate sensor wired or wirelessly to the terminal where the method steps 103-108 described below can be implemented.

In step 103, timestamps corresponding to heartbeats 20 are set based on each individual heart rate signal. In step 104, these timestamps are formed into a reading set for further analysis.

From the time series generated in step 104, a sequence period is determined. This can be found, for example, by counting the other derivatives of the series and examining the zeros of this new series, that is, the 25-digit change points. More specifically, one possible embodiment of this method step is described later in Example 1.

The sequence of the series further determines, at step 106, one or more of a statistically correlated property with respiration, typically respiratory rate, respiratory amplitude, or amount of air transported through the respiratory tract (ventilation). An approximation to the respiratory rate is obtained directly from the sequence of the series, whereas other data typically requires preliminary data to calculate.

5

We have found that, when utilized as described above, the periodicity of heart rate noise is a reliable indicator of respiration. Particularly advantageous in the method described is that the electrically measured heart rate signal alone also provides a respiratory signal with high accuracy and simple time domain computation. The interval of observation can be 5 relatively short periods, whereby the desired breathing parameter can also be updated relatively quickly. When a new heart rate is registered and time stamped, only a few calculations are needed to calculate an updated time series period. At typical exercise-time heart rate and respiratory levels, a review interval of about 5 to 15 heart rate yields a first approximation to the instantaneous respiratory rate. This can be refined, if necessary, at a longer time interval, at the expense of time resolution.

According to the invention, the breath-describing parameter is used to calculate the energy consumption during the run. Typically, at least one prior knowledge of either the person being measured and / or the species performed by him or her will be used. Preliminary data comprises data that can be determined from experiments or data not directly related to oxygen uptake. They may include, for example, a person's activity class, weight, height or sex, or information about the nature of the species the person is performing. The nature of the species primarily refers to whether it is a Sprint type or a durability type. The activity class (typically on a scale of 1-10) can again be determined e.g. based on the person's volume of searches without 20 physical exams. Other personal or species-specific information may also be used. In step 108, the necessary computation is performed, always depending on the pre-information used and the parameter (s) describing the breathing. According to a particularly preferred embodiment, the pre-selected data is used directly as scaling factors for the respiratory parameter or parameters, which further simplifies and speeds up the computation. Different weights can be given different computations in the calculation. Preferably, the final result is converted into absolute instantaneous units of energy consumption (e.g., kcal / min). The cumulative energy consumption of the performance can also be calculated. Consumption may also be reported as some relative value. In step 109, the end result can be presented to the user.

30 Particularly at the beginning or end of the exercise or at any other rhythmic point of the exercise, respiratory rate does not usually correlate directly with current energy expenditure. When a person begins to exercise, his or her breathing does not immediately reach a level comparable to that of instantaneous energy. On the other hand, after exercise or breaks, respiratory rate remains high even though physical exertion is over. However, these factors can be accounted for by observing temporal changes in the measurable quantity of respiratory rate, heart rate, or other performance. If a predetermined change in such a quantity is observed over a period of time, the respiratory rate may be corrected computationally toward a value of respiratory rate that better corresponds to actual energy consumption. For example, real-time mapping may occur by keeping momentary respiratory rates in the buffer memory for a predetermined interval of time, and comparing the latest obtained respiratory rate with historical values of respiratory rate. More specifically, the kayaking process can be implemented as described in Example 2, but one skilled in the art will recognize that computation with a similar effect can be accomplished in many different ways.

10

Preferably, the estimation of the energy consumption estimate is performed intensively. This means that estimates of energy consumption are cooked in proportion to how much the measure of change in rhythm of performance changes. For example, this compensates for a slow change in breathing or heart rate relative to the instantaneous intensity of the exercise. Of course, the measure representing the change in rhythm-15 may also be information obtained through, for example, an accelerometer, whereby enhanced mapping may not be required.

Although this document describes the use of energy consumption calculation and pulse rate change based coefficient calculation in connection with time-dependent respiratory rate determination, it can be used very commonly with other respiratory rate determination methods. However, since the computational methods described are also feasible with low computing capacity and in real time, there is a particular advantage in using them together.

The actual energy consumption is preferably calculated from the second order respiratory rate behavior, which has been found to closely match the actual energy consumption. Further, according to a preferred embodiment, in addition to respiratory rate, only general prerequisite parameters of the subject, which are not directly derived from the individual's metabolic experiments, are used in the calculation, as described above. For example, height and weight (mass) are directly measured by 30 Sl units, which are simple to measure and usually readily known to the user with good accuracy. The activity class can be determined using pre-defined and commonly used tables. These can be used directly as weighting factors for respiratory rate or derived secondary parameter.

7

Known solutions approach the energy calculation problem in a completely different direction: measuring or estimating a person's metabolism, which is used as a basis for energy calculation. The present solution, in turn, is based on gathering sufficient general information about the user to form an estimate of the expected respiratory energy consumption dependence. This can also be considered as a more reliable way in the sense that the impact of a single incorrectly entered pre-information factor on the final result is less than, for example, using a V02max measurement that is incorrectly measured or entered as a basis for calculation. Thus, in the manner described, a user-friendly and reliable way of calculating energy consumption is provided.

The method described with reference to Fig. 2 is preferably performed either wholly or in part on a portable device, preferably a wrist unit 220. The heart signal measurement can be performed by a heart rate belt 210 having transmitter 214 transmitted by electromagnetic radiation 250 to the wrist unit 220. The heart rate measuring electrodes are designated 212.

Preferably, the wrist unit 220 comprises a heart rate signal receiver 202, a processing unit 204 for setting time stamps of heart rate intervals and for determining at least one respiratory parameter in a time domain based on the periodicity of the signal contained in the heart rate intervals of the heart rate signal. Additionally, the device has a buffer memory 206 for storing heart rate data, a timestamp sequence and / or calculated respiratory parameters. The other calculations required for signal processing of the heart rate signal are typically performed in a microprocessor or in separate integrated circuits designed for these functions. The device may also have memory for long-term storage of heart rate, respiratory and / or Energi consumption data. In addition, the device generally also has a display 224 for displaying the result of the calculation.

Due to the method described, the processing unit 204 can be made small and low power consuming.

The method may also be implemented such that the calculation of the respiratory parameter is performed either wholly or in part in the heart rate belt or other sensor means, whereby the entire heart signal need not be transmitted to the terminal. Thus, it is sufficient to transmit to the terminal at certain intervals only the result of the calculation or the intermediate results with the associated time information.

8

The method may also be implemented during or after execution on a computer to which the heart rate signal or heart rate data in memory, time stamps or intermediate results or derived parameters derived therefrom can be transmitted directly from the sensing means or indirectly from, for example, a wrist unit.

5

The above detailed description of embodiments of the invention, the accompanying drawings, and the following examples are not to be construed as limiting the invention, but are to be considered as exemplary ways of practicing the invention only. Accordingly, the invention is to be construed in its entirety and in the light of the equivalence interpretation.

10

Example 1.

This example illustrates how respiratory rate can be determined over time from a heart rate signal in a simple manner. The starting point is that 15 heartbeats have been identified from the heart rate signal and these have been used to select Saija time points from the noise of the heart rate intervals.

The time series used to calculate respiratory rate and / or ventilation, extracted from the heart rate signal, is a reading series consisting of time points. Each time point corresponds to the moment the heartbeat is recognized. For example, time is measured in milliseconds.

20 This gives the reading range of the monotonically increasing time points (in milliseconds, for example, 0, 1010, 1950, 2800, 3650, ...), possibly excluding the time when the variable containing the timestamps will restart from zero as a result of an overflow. The approximation of the first derivative of the signal intensities corresponding to this reading range is (t2-t1) / l = 25 svl. The approximation of the second derivative is (sv2-sv1) / l, where sv2 = (t3-t2) / l. By looking at the change in the sign of the second derivative, we get a new set of digits: ttl, tt2, tt3 „... In this set, ttl = - the time when the sign changed to positive (or negative) + the time when the sign next became positive (or negative). This period describes the periodicity of the heart rate interval noise, from which the respiratory rate and ventilation can be approximated.

9

Example 2.

The respiratory rate correction to account for changes in exercise rhythm (steps 1-6) and further the energy consumption estimate (steps 7-8) can be accomplished, for example, by the following method: 1. Calculate instantaneous respiratory rate 2. Find global respiratory rate minimum (over a period of time) (over a shorter period of time) 4. If the latest respiratory rate is sufficiently below the local maximum (eg 15%), reduce the 10 respiratory rate by a factor of 0 ... 1 (eg 0.7) 5. If the latest respiratory rate is sufficiently above the local minimum (eg 20%), update the local maximum to the last respiratory rate 6. From the pre-treated respiratory rate in steps 1-5, subtract a correction factor, which may be a fixed value or a value adaptive with the global respiratory minimum to 15 (offset correction) 7. Increase the direct treated esitietoparametreilla

Claims (26)

A method for monitoring the physiological status of an individual at runtime, comprising: 5. detecting (101) the heart rate of the subject to obtain a heart rate signal, - determining the periodicity of the heart rate temporal variation based on the heart rate signal (103); at least one person breathing parameter, characterized in that the breathing parameter is used (107,108) to estimate a person's energy consumption. Method according to claim 1, characterized in that the respiratory parameter is the respiratory rate. 15 A method according to claim 1 or 2, characterized in that: - forming (104) a series of time stamps comprising consecutive time points, - determining a sequence period, and - determining a respiratory parameter based on the sequence period. 20 Method according to claim 3, characterized in that the sequence of the series is determined by counting (105) the second derivative of the series and searching for its zeroes. A method according to any one of the preceding claims, characterized by: 25. observing (107) temporal changes in the intensity of a performance, such as respiratory or pulse rate, to detect changes in rhythm during exercise, actual energy consumption. 30 Method according to Claim 5, characterized in that the estimated energy consumption is corrected upwards if an increase in the performance intensity is observed and / or the calculated energy consumption is corrected in an downward fashion if a decrease in the performance intensity is observed. Method according to one of the preceding claims, characterized in that the energy consumption is estimated on the basis of a higher degree of respiratory rate behavior, preferably of a secondary order. 5 A method according to any one of the preceding claims, characterized in that at least one, typically 2 to 4, or all of the following prerequisite data is used to estimate energy consumption: person's activity class, person's weight, person's height, person's sex, nature of species. . 10 Method according to one of the preceding claims, characterized in that the assessment of energy consumption is based on the use of a defined respiratory parameter and pre-data parameters not derived from metabolic tests alone. A method according to any one of the preceding claims, characterized in that it is performed during execution on a portable device such as a wristop computer (220) or on a computer. A method according to any one of claims 1 to 9, characterized in that it is carried out partially or completely in a device unit directly measuring the heart rate signal, such as a heart rate belt (210). A portable device for monitoring the physiological status of an individual at runtime, comprising: a sensor for detecting a heart rate of a heart to generate a heart rate signal, or means (202) for receiving a heart rate signal generated by such a sensor; periodic variation, wherein the processing unit (204) is adapted to determine the capability of pulsatile-3 0 temporal variation with time stamps based on the heart rate signal in the time domain, characterized in that the device is adapted to estimate a person's energy consumption based on a breathing parameter. A portable device according to claim 12, characterized in that the processing unit (204) is adapted to determine respiratory rate. A portable device according to claim 12 or 13, characterized in that the processing unit (204) is arranged to: - form in the memory unit (206) a series of time stamps with successive time points, - determine a sequence and define a breath parameter axon. 10 A portable device according to claim 14, characterized in that the processing unit (204) is adapted to determine the sequence of the sequence by counting the second derivative of the series and searching for its zeroes. Portable device according to claim 14 or 15, characterized in that the device is further adapted to - observe temporal changes in the intensity of any exercise, such as respiratory or pulse rate, to detect changes in rhythm during exercise, and, if detected, to correct energy consumption. estimate towards the assumed actual energy consumption. Portable device according to claim 16, characterized in that it is adapted to correct the energy consumption upwards if the intensity intensity measure 2 is observed to increase and / or to correct the energy consumption downwards if the performance intensity quantity decreases. A portable device according to any one of claims 12 to 17, characterized in that it is adapted to estimate energy consumption based on a higher degree of respiratory behavior, preferably a second degree behavior. A portable device according to any one of claims 12 to 18, characterized in that it is adapted to use at least one, typically 2 to 4, or all of , the sex of the person, the nature of the species performed by the person. A portable device according to any one of claims 12 to 19, characterized in that it is adapted to estimate energy consumption by using a determined breathing parameter and pre-data parameters not derived from metabolic tests alone. A portable device according to any one of claims 12 to 20, characterized in that it is a wristop computer (220) or a computer. 10 A portable device according to any one of claims 12 to 20, characterized in that it is a heart rate belt (210). 23. A computer program product for determining a physiological condition of a person adapted to receive measurement data of a person's heart rate and to determine at least one parameter describing a person's breathing based on the periodicity of temporal fluctuation of the heart rate information contained in the measurement data; characterized in that it is further adapted to calculate an estimate of a person's energy consumption 20 using a calculated breath parameter. A computer program product according to claim 23, characterized in that it is adapted to: - generate a sequence of heart rate observations comprising consecutive time points, 25. determine a Saija period by, for example, calculating a second derivative of saqa and searching for its zeros; A computer program product according to claim 23 or 24, characterized in that it is adapted, in addition to the respiratory parameter, to use only pre-data parameters that are not derived from metabolic tests, preferably at least one, typically 2 to 4, or all of the following category, person's weight, person's height, person's gender, nature of the species the person is performing. 5 A computer program product according to any one of claims 23 to 25, characterized in that it is adapted, based on temporal changes in a measure of intensity of a performance, to crash into an estimate of the actual energy consumption.