WO2008053419A2

WO2008053419A2 - Determining body composition

Info

Publication number: WO2008053419A2
Application number: PCT/IB2007/054362
Authority: WO
Inventors: Klaas R. Westerterp; Marcel Den Hoed; Annelies Goris; Maarten P. Bodlaender
Original assignee: Koninklijke Philips Electronics N.V.
Priority date: 2006-10-30
Filing date: 2007-10-26
Publication date: 2008-05-08
Also published as: WO2008053419A3

Abstract

A device (10) and method of determining body composition are described, in particular for determining a body fat fraction. The method includes receiving predetermined parameters of a body, such as weight, height, and sex. At least one measured parameter of the body is received, e.g. from an accelerometer (21), for detecting physical activity of the body. Subsequently the body composition is calculated by processing said predetermined parameters and measured parameter. Surprisingly the body fat percentage can be accurately determined based on the formula: fat fraction = a + b * heightw + c * agex + d * weighty + e* gender + f * activityz; activity being a measured value indicative of the physical activity of the body.

Description

DETERMINING BODY COMPOSITION

FIELD OF THE INVENTION

The invention relates to a method of determining body composition, the method comprising receiving predetermined parameters of a body, receiving at least one measured parameter of the body, and calculating the body composition by processing said predetermined parameters and measured parameter.

The invention further relates to a device for determining body composition, the device comprising means for receiving predetermined parameters of a body, means for receiving at least one measured parameter of the body, and processing means for calculating the body composition by processing the predetermined parameters and measured parameter. The invention further relates to a computer program product for determining body composition.

Body composition of mammals, in particular the fat fraction of the human body, is an important indicator of health. Body composition measurements are not only of interest for people who want to loose weight, but also for people who sport regularly or people who are generally interested in their health. Most people are aware of the fact that a too high percentage of body fat is unhealthy.

BACKGROUND OF THE INVENTION

In professional environments like medical centers, professional athletic teams, or fitness clubs, various methods are practiced to determine a person's body composition. For example such methods use body composition analyzers and fat callipers, requiring an experienced person who performs the measurement, or underwater weighing, only suitable for research settings.

However, currently there is a need for body composition systems that can be applied without professional support, e.g. in a consumer environment. Current home use body composition analyzers (like the Tanita scale, see www.tanita.com) are based on using personal characteristics and an electrical impedance measurement. Such body composition analyzers apply an algorithm based on personal parameters, such as length and gender, combined with the electrical impedance measurement on the body. The end result which is displayed is a fat percentage of one's body.

A disadvantage of electrical impedance measurements is that these measurements are influenced by differences in hydration status and body temperature. These types of body composition analyzers are therefore difficult to use or too inaccurate for the assessment of small changes in body composition. A change in body composition without change in overall body mass cannot be reliably detected with personal body composition analyzers known today.

SUMMARY OF THE INVENTION

It is an object of the invention to provide a method and device for determining body composition without requiring inconvenient measurements.

For this purpose according to the invention, in the method and device as described in the opening paragraph, said measured parameter is based on detecting physical activity o f the body.

The measures have the effect that the physical activity in a period of time is taken into account for calculating the body composition. Advantageously the physical activity can be easily detected based on an activity monitor, such as an accelerometer or pedometer. Moreover, although a person increasing his/her activity level may loose hardly any weight due to muscle tissue increase, the fatness fraction will change and is indicative of the progress in health.

The invention is also based on the following recognition. Examples are known of estimating the energy expenditure of a person by detecting the physical activity, as for example discussed in the document "Estimation of activity energy expenditure: accelerometer approach", by Choi et al, Proceedings of the IEEE Engineering in Medicine and Biology 27^th Annual Conference, Shanghai, China, pp3830-3833. Whereas a relation between the physical movements of a person and the corresponding energy expenditure has been investigated earlier, no direct relation between activity and body composition has been foreseen. Surprisingly, the inventors have found a relation for processing the activity data and further predetermined body parameters, for generating a reliable estimate of the actual fat fraction of a body.

In an embodiment of the method said determining body composition comprises determining a fat fraction of the body, in a particular case a fat percentage. Although further elements of the body composition may be determined, like muscle fraction, the fat fraction or percentage advantageously carries a clear health indicator for the human user.

In an embodiment of the method said predetermined parameters include one or more of: height; weight; body mass index; age; gender; race; calibrated fat fraction; DNA profile; body dimension; garment size. This has the advantage that some parameters are easily detectable for a user. Other parameters may be determined in earlier measurements, for example in a professional environment. Taking into account the predetermined parameters further increases the accuracy of the body composition.

In an embodiment of the method said calculating comprises applying a predetermined relation comprising a first part that processes the predetermined parameters, and a second part that processes the measured parameter representing the physical activity of the body. This has the advantage that the calculation is based on the predetermined relation and can be easily implemented. The first part needs only to be recalculated when a new predetermined parameter value is entered. In an embodiment of the device the measurement means comprise a tri-axial accelerometer. This has the advantage that the tri-axial accelerometer accurately detects the amount of physical activity.

Further preferred embodiments of the device and method according to the invention are given in the appended claims, disclosure of which is incorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the invention will be apparent from and elucidated further with reference to the embodiments described by way of example in the following description and with reference to the accompanying drawings, in which

Fig. 1 shows a system for determining body composition, and Fig. 2 shows a method for determining body composition. In the Figures, elements which correspond to elements already described have the same reference numerals.

DETAILED DESCRIPTION OF EMBODIMENTS

Figure 1 shows a system for determining body composition. The Figure shows a body 20, for example a human user, wearing an activity monitor 21. The system includes a device 10 having a calculation unit for calculating the body composition, and various data input and output elements for receiving or delivering data.

In a practical embodiment determining body composition may include determining a fat fraction of the body, in a particular case a fat percentage. It is noted that the system may be integrated in a single housing that has a display to show the calculated results, or may have separate elements that are connected when appropriate. For example the output data, such as a calculated body fat percentage, may be transferred to a separate display device or a computer. It is noted that the system may also be used for other bodies, for example animals, by adapting the respective calculation rules. The device 10 for determining a body composition has the following elements.

An activity input unit 11 for receiving measurements from the activity monitor, is coupled to a processing unit 12. A user input unit 14, for example a keyboard, is also coupled to the processing unit 12 for receiving user data including predetermined parameters of the body, such as body weight or length. An output unit 13, for example a display, is also coupled to the processing unit 12 for outputting the body composition. It is noted that the input and output units may also be interfaces to receive or transfer data to further data processing systems. The processing unit may include a processor and memory, or may contain dedicated hardware circuits to perform the calculations as explained below for determining the body composition. The activity monitor 21, e.g. an accelerometer or pedometer, may be integrated in the device 10, or may be a separate unit being arranged for transferring the measurements when needed, e.g. once a week or once a day via a wired or wireless interface like USB or Bluetooth as indicated by arrow 22. Furthermore a combination of a few parameters may be measured, for example a pedometer counting steps in combination with an accelerometer evaluating movements of the body.

The device 10 for determining body composition is arranged for receiving, via input unit 14, predetermined parameters of a body, such as weight, body height, age, gender, and/or various other body size parameters. The user may enter such parameters once, and the device may store the parameters in a memory. Also various users may enter their respective parameters, and may each have their respective activity monitors connecting to a main processing device. Furthermore, via input unit 11, the device receives at least one measured parameter of the body, the measured parameter being based on detecting physical activity of the body in a predetermined period, usually a day. For example count values may be generated for periods of one hour or day, e.g. by detecting and, where appropriate, averaging the activity. Also a sequence of measurements may be transferred to the processing unit 12 to be evaluated and, where appropriate, averaged or extrapolated to cover a predetermined period like a full day or week.

After receiving the predetermined and measured parameter values, the processing unit 12 calculates the body composition by processing the values of the predetermined parameters and the measured parameter according to a predetermined algorithm. Various examples of the algorithm are discussed now. Further embodiments of the algorithm, including specific values for the constants in the example algorithms, can be easily determined by appropriate calibration on a set of measurements on a known population of test persons.

In an embodiment of the system for determining body composition, the measurement unit 21 for detecting physical activity of the body is a tri-axial accelerometer. Practical embodiments of such accelerometers for determining a measure of physical activity are described in US 2003/0074157. Figure 2 shows a method for determining body composition. The method starts at node START 30. In first step REC P AR 31 a number of predetermined parameters of a body is received. In step MEASURE 32 a number of measurements of at least one parameter of the body is acquired. The measurements relate to the movement of the subject, e.g. values provided by a one or two dimensional accelerometer. Hence the measured parameter is based on detecting physical activity of the body. In next step CALCULATE 33 the body composition is calculated by processing the predetermined parameters and measured parameter. Finally the resulting value for the body composition, e.g. a fat percentage, is outputted for further use. In the Figure a next step DISPLAY 34 shows the calculated value to a user. Subsequently at node NEXT 35, a next set of measurements is activated or the process is terminated at node END 36.

The present calculation combines two important factors a person wants to know if participating in sports or dieting, i.e. physical activity and body composition measurements. So the person can immediately check the effect of physical activity on body composition, usually less body fat and more muscles. The activity monitor provides measurements of physical activity which are combined with personal parameters of characteristics. The activity monitor is preferably a tri-axial accelerometer, but pedometers or one/two axial accelerometers can also be used but give a lower accuracy of the body composition estimation. The user wears the activity monitor for several days. More days wearing means a higher accuracy. In combination with personal characteristics like age, weight, height and sex an estimation of the body composition is given by the following formula: fat fraction = a + b * height^w + c * age^x + d * weight^y + engender + f * activity².

The values for the constants a,b,c,d,e,f, and the power factor constants w,x,y,z, are determined by calibration on a test population and the respective activity monitor. In the examples below practical values are given for the constants, whereas the power factor constants w,x,y,z are set to 1. An example of the formula using a practical tri-axial accelerometer for the percentage of body fat is:

Fat (%) = 34.1 - 30.7 height (m) + 0.6 age (y) + 0.4 weight (kg) + 12.7 gender (female=l, male=0) - 1.7 x 10^"9 counts/day.

It is noted that variation of the constants for different conditions is accommodated by adjusting the above formula. An example is:

Fat (%) = 38.2 + 0.39*Weight (kg) - 22.06*Height (m) + 0.44*Age (y) - 12.5*Sex

(0=female; l=male) - 0.0022*Physical activity (MCounts/d) The number of counts of the activity monitor is measured in million counts per day

[MCounts/d].

It is noted that variation of the predetermined parameters is accommodated by adjusting the above formula. A further example is:

Fat (%) = 1.5 + 1.16*BMI (kg/m2) + 0.37*Age (y) - 11.0*Sex (0=female; l=male) - 0.0022*Physical activity (MCounts/d)

The body mass index is a characteristic of a person defined by BMI = weight (kg) / height² (m)

It is noted that in practice the above formulas have an accuracy of about 75%, which is quite acceptable in a consumer device. In embodiments of the method for determining body composition the predetermined parameters may include one or more of the following elements: height; weight; body mass index; age; gender; race; calibrated fat fraction; DNA profile; body dimension; garment size, etc. It is noted that for each of the parameters a relation to the body composition can be determined by clinical tests in ways known as such, by using calibrated body composition techniques.

In professional environments various relations between parameters and body composition are known, for example from "Sports Med. 1996 Sep; 22(3): 146-56. Evaluation of body composition. Current issues. Heyward VH". In the publication the following is described. In the selection of body composition field methods and prediction equations, exercise and health practitioners must consider their clients' demographics. Factors, such as age, gender, and ethnicity influence the choice of method and equation. Also, it is important to evaluate the relative worth of prediction equations in terms of the criterion method used to derive reference measures of body composition for equation development. Given that hydrodensitometry, hydrometry and dual-energy x-ray absorptiometry are subject to measurement error and violation of basic assumptions underlying their use, none of these should be considered as a 'gold standard' method for in vivo body composition assessment. Reference methods, based on whole-body, 2-component body composition models, are limited, particularly for individuals whose fat-free body (FFB) density and hydration differ from values assumed for 2-component models. Use of field method prediction equations developed from 2-component model reference measures of body composition will systematically underestimate relative body fatness of American Indian women, Black men and women, and Hispanic women because the average FFB density of these ethnic groups exceeds the assumed value (1.1 g/ml). Thus, some researchers have developed prediction equations based on multicomponent model estimates of body composition that take into account inter-individual variability in the water, mineral, and protein content of the FFB. One multicomponent model approach adjusts body density (measured via hydrodensitometry) for total body water (measured by hydrometry) and/or total body mineral estimated from bone mineral (measured via dual-energy x-ray absorptiometry). Skinfold (SKF), bioelectrical impedance analysis (BIA), and near-infrared interactance (NIR) are 3 body composition methods used in clinical settings. Unfortunately, the overwhelming majority of field method prediction equations have been developed and cross-validated for White populations and are based on 2-component model reference measures. Because ethnicity may affect the composition of the FFB and regional fat distribution, race-specific prediction equations may need to be developed for some ethnic groups. To date, race-specific SKF (American Indian women, Black men, and Asian adults), BIA (American Indian women and Asian adults), and NIR (American Indian women and White women) equations have been developed. However, these equations need to be cross-validated on additional samples from these ethnic groups. In summary, research strongly suggests that multicomponent models need to be used in order to quantify differences in FFB composition due to ethnicity so that accurate SKF, BIA, and NIR prediction equations can be developed. Assessment of body composition in vivo may be enhanced by using advanced technologies such as dual-energy x-ray absorptiometry and hydrometry to refine hydrodensitometry. Furthermore a calibrated fat fraction for a particular person may be determined once in a professional medical or sports facility, and subsequently entered and stored for use in the calculation unit 12 or method shown in Figure 2.

The invention can be incorporated in a weight-management system; the effect of changes in physical activity (not per se sports exercise) on body fat loss can be easily followed. Furthermore the activity monitor with body composition algorithm can be used in training programs.

It is to be noted that the invention may be implemented in hardware and/or software, using programmable components. A computer program may have software function for the respective processing steps, and may be implemented on a personal computer or on a dedicated measurement system. Although the invention has been mainly explained by embodiments using a dedicated device or measurement system, the system may be integrated in any suitable processing device, for example a personal computer [PC], a personal digital assistant [PDA], a mobile media player or a mobile phone. Furthermore, the examples mainly relate to the human body, but the invention may be used for calculating the body composition of any living being, e.g. cattle.

It is noted, that in this document the word 'comprising' does not exclude the presence of other elements or steps than those listed and the word 'a' or 'an' preceding an element does not exclude the presence of a plurality of such elements, that any reference signs do not limit the scope of the claims, that the invention may be implemented by means of both hardware and software, and that several 'means' or 'units' may be represented by the same item of hardware or software, and a processor may fulfill the function of one or more units, possibly in cooperation with hardware elements. Further, the invention is not limited to the embodiments, and the invention lies in each and every novel feature or combination of features described above.

Claims

CLAIMS:

1. Method of determining body composition, the method comprising receiving predetermined parameters of a body, receiving at least one measured parameter of the body, calculating the body composition by processing said predetermined parameters and measure paramter, the measured parameter being based on detecting physical of the body.

2. Method as claimed in claim 1, wherein said determining body composition comprises determining a fat fraction of the body, in a particular case a fat percentage.

3. Method as claimed in claim 1, wherein said predetermined parameters include one or more of: height; weight; body mass index; age; gender; race; calibrated fat fraction; DNA profile; body dimension; garment size.

4. Method as claimed in claim 1, wherein said calculating comprises applying a predetermined relation comprising a first part that processes the predetermined parameters, and a second part that processes the measured parameter representing the physical activity of the body.

5. Method as claimed in claim 4, wherein said calculating comprises applying as the predetermined relation: fat fraction = a + b * height^w + c * age^x + d * weight^y + e* gender + f * activity² activity being a measured value indicative of the physical activity of the body, and a, b, c, d, e, f being constant values and w, x, y, z being constant values.

6. Method as claimed in claim 4, wherein said calculating comprises applying as the predetermined relation: fat fraction = a + b * body mass index (kg/m²) + c * age (year) + engender (0=female; l=male) + f * activity (counts) activity being a measured value indicative of the physical activity of the body, and a, b, c, e, f being constant values.

7. Method as claimed in claim 1, wherein the method comprises detecting physical activity of the body by an accelerometer or a pedometer.

8. Device for determining body composition, the device comprising - means (14) for receiving predetermined parameters of a body, means (11) for receiving at least one measured parameter of the body, processing means (12) for calculating the body composition by processing the predetermined parameters and the measured parameter, the measured parameter being based on detecting physical activity of the body.

9. System for determining body composition, the system comprising the device of claim 8 and measurement means (21) for detecting physical activity of the body and for providing the measured parameter.

10. System as claimed in claim 9, wherein the measurement means (21) comprise a tri-axial accelerometer.

11. Computer program product for determining body composition, which program is operative to cause a processor to perform the method as claimed in any of the claims 1 to 7.