Tracking energy intake and expenditure using heart rate data

Abstract

A method and system for tracking energy intake (or energy consumption) and / or energy expenditure of an individual by analysing heart rate data. The heart rate data may for example be provided by a wearable electronic device configured to measure heart rate data. The method and system work by quantifying the number of food heartbeats comprised in the heart rate data and using measurable (or derivable from measurements) metabolic parameter(s) to transform the quantified food heartbeats to energy intake. The accuracy of the method and system may further be enhanced by utilizing basal heart rate data and / or activity data. The method and system may further quantify the heart rate data into other components as well, namely, base heartbeats and activity heartbeats and use these, as well as food heartbeats to quantify the energy expenditure of an individual.

Description

[0001] P16006PC00 1

[0002] Tracking Energy Intake and Expenditure Using Heart Rate Data

[0003] TECHNICAL FIELD

[0004] The disclosure relates to the field of energy tracking, more specifically it pertains to using heart rate measurements to determine consumed and / or expended units of energy, i.e., energy intake and / or energy expenditure.

[0005] BACKGROUND

[0006] In the study of human energetics, total energy expenditure (TEE) is divided into three major components, i.e., resting metabolic rate (RMR), physical-activity related energy expenditure (PEE) and diet-induced thermogenesis (DIT). The DIT is often approximated to amount to about 10% of the total energy expenditure and is often referred to as the "thermic effect of food (TEF)".

[0007] TEF is mostly caused by the digestion, absorption, and storage of ingested food. Studies have shown TEF to be dependent on meal composition (highest for protein, lowest for fat) and positively affected by meal size, energy content and physical activity level. TEF is attenuated by physical exercise and appears to be negatively related to ageing and obesity. Ageing is generally associated with a gradual decline in physical activity, loss of muscle mass, increased body fat, and a lowering of RMR. A lowering of TEF is often suggested as an important reason for the gradual body fat increase in ageing and obesity.

[0008] The monitoring of human energetics involves the tracking of energy expenditure and energy intake, usually for the purpose of weight management. The scientifically accepted gold-standard method for measuring energy expenditure is the "doubly labeled water method (DLW)" but a more accessible methodology is "indirect calorimetry (IC)", involving the measurement of gas exchange in a laboratory test. DLW, together with cumulative energy intake curves in a laboratory setting are also the only scientifically accepted methods that provide an accurate quantitative measure of energy intake. However, food records, food frequency questionnaires, 24-h diet recalls and food-logging apps are still the most common methods to assess energy intake, in spite of their notorious inaccuracy when compared to DLW.

[0009] The metabolic equivalent of task (MET) is defined as a ratio of the rate of energy expended by an individual during physical activity to the rate of energy expended by the user at rest (referred to as the resting metabolic rate (RMR)). Many studies have shown that the conventional 1 MET baseline overestimates actual resting oxygen consumption and energy expenditures by about 20% to 30% on average. Therefore, an accurate calculation of MET for a specific individual requires data specific to the user and the activity.

[0010] Prior art includes patented methods for tracking food energy intake, energy expenditure or both through the use of electronic devices in field settings.

[0011] US11653836B2 discloses a solution to estimate ingested calories by analyzing a user's skin to determine energy intake, wherein a spectroscope and a LED are used to measure light wavelengths absorbed, emitted and reflected by the skin. US10667728B2 discloses a method to determine glucose concentration in human blood by measuring the impedance of a human body region at a high frequency (ZHF) and a low frequency (ZLF) to detect patterns related to the osmolarity ofP16006PC00 2

[0012] ingested nutrients. The ingested calories are determined from changes in blood glucose concentration. However, this solution may require a device that is both large and heavy and hence uncomfortable to wear by a user for a period of time.

[0013] US9747411B2 discloses a solution to calculate energy expenditure values from an apparatus configured to be worn on an appendage of a user using motion data such as armswings, step counts and bounce peaks. US10898132B2 discloses a solution for tracking athletic activity such as energy expenditure and intensity based on movement data over a period of time. US11638556B2 discloses a solution for estimating caloric expenditure using a heart rate model specific to predicted motion classes, e.g., model predicts a likelihood of arm motion only, and detects a body component in the motion with a moderate likelihood. US9089285 discloses an activity monitor to detect motion of a user, wherein the activity monitor includes an accelerometer and an on-demand heart rate monitor and if repetitive motions are detected, expenditure based on heart rate may be determined.

[0014] SUMMARY

[0015] An object of the present invention is to provide a solution for substantially effortless tracking of energy intake (also referred to as calorie intake or caloric consumption) and / or energy expenditure of an individual.

[0016] The energy intake and / or expenditure may be computed for a period of time such as one day or longer and then tracked for even longer periods of time. The computing and tracking of energy intake and / or energy expenditure should require only minimal input by the user / individual (herein, the term "user" may be used interchangeably with the term "individual"). It should preferably not require a user / individual to input information about consumed food items (such as beverages and meals) such as the type, quantity and / or caloric content of food items. Input of information regarding consumed food items requires a lot of effort and time by the user. The tracking of energy expenditure should preferably not require a user to input information about activity, in particular, routine activities. Frequent input of information may be discouraging for a user attempting to track his / her energy intake and / or energy expenditure and cause the user to lose interest over time.

[0017] In broad terms, the present invention determines energy parameters such as energy intake and / or energy expenditure using heart rate data. Therefore, the solution provided by the present invention is convenient and requires minimal effort for the user and can, preferably, be used with any type of wearable electronic device such as wrist-worn electronic watches or rings, such devices are already in common use or simply by receiving heart rate data from any source. Alternatively, the solution of the present invention may provide specifically designed and energy-efficient wearable electronic devices such as, but not limited to, specifically designed wrist-worn electronic device configured to measure heart rate and / or activity data. Such a device may be a part of the solution provided by the present invention.

[0018] The heart rate data may be measured by a wearable electronic device configured to measure heart rate as a function of time. The device may further be configured to measure and / or receive physical activity data, such as movement accelerometric data and / or activity heart rate (the heart rate being derivable from physical activity data).P16006PC00 3

[0019] An assumption of the invention is that diet-induced thermogenesis (DIT) is directly reflected in the post-prandial heart rate response of the individual. The energy intake and / or energy expenditure may be determined by assuming that the heartbeats of an individual, and hence the corresponding heart rate, may be separated into three major classes of heartbeats, namely, base heartbeats (BB), food heartbeats (FB) and activity heartbeats (AB). All three classes contribute to the estimate of total energy expenditure, while only the food heartbeats contribute to the estimate of energy intake (or caloric consumption).

[0020] To obtain / quantify the food heartbeats, a heart rate response due to energy intake may be determined by fitting and connecting one or more mathematical functions comprising desired mathematical properties (as outlined below), herein referred to as food waves, to selected data points of the heart rate data. The heart rate response due to energy intake allows the food heartbeats to be determined and subsequently the energy intake of the individual (and the energy expenditure due to food intake).

[0021] The determination or quantification of food heartbeats may be made more accurate by using also physical activity data or physical activity heart rate and by predicting the effect of motion activity on the heart rate to adjust the value of selected data points, preferably, one or more selected points of minimum heart rate, in the heart rate data prior to fitting the one or more food waves.

[0022] In an aspect of the present invention, a computer-implemented method for determining the energy intake of an individual is provided.

[0023] The method includes a step of providing data comprising heart rate data of an individual, as a function of time, to a processing unit, a step of providing or computing at least one metabolic parameter for the individual, in particular at least a food factor, to the processing unit, a step of quantifying, by the processing unit, the heart rate data into one or more heartbeat components, in particular at least into food heartbeats and wherein the quantification is determined by fitting one or more food waves to one or more points in the heart rate data to obtain a heart rate response due to energy intake. The food heartbeats being determinable from the heart rate response. The method further includes a step of determining, by the processing unit, the energy intake of the individual using the food heartbeats and the food factor, and a step of outputting, by an output means, the energy intake to the individual.

[0024] The heart rate data may be given in a unit of quantity of heartbeats per time unit such as, but not limited to, number of heart beats per minute. The heart rate data may be measured and provided by common wearable electronic devices such as, but not limited to, smart watches that are configured to measure heart rate or alternatively by specifically designed wearable electronic devices that may constitute a part of the present invention. The heart rate data may thus be measured and provided in real-time, continuously or in batches, and the energy intake determined and outputted to an individual. The outputting of the energy intake may advantageously allow an individual to manage his / her dietary choices in real-time based on the energy intake feedback.

[0025] In some embodiments, the energy intake may be determined and outputted in real-time, on-the-fly, after the measurement of the heart rate data. Alternatively, and or additionally, the energyP16006PC00 4

[0026] intake may be determined and outputted after an expiry of a period of time, wherein the period of time may be predetermined such as, but not limited to, 12 hours, a day, two days, a week, or wherein the period of time may be the period of time comprising the measurement of heart rate data.

[0027] The heart rate data may be measured over a pre-defined interval of time such as but not limited to 30 minutes or less, or 1 hour, or 2 hours, or 3 hours, or 4 hours, or 8 hours, or 12 hours, or 14 hours, or 16 hours, or 18 hours, or 20 hours, or 22 hours, or 24 hours, or two days, or four days, or seven days, or a month, or any value therebetween.

[0028] An advantage of the present invention is that it allows an individual to seamlessly track their energy intake / calorie consumption without manually logging, or in some embodiments through minimal logging of, information relating to consumed items and in particular the caloric content of the consumed items or macronutrients. This will help individuals to manage their dietary habits and choices, gain insights into eating patterns, and help individuals to stay on track with their fitness and weight management goals. This all serves to improve the general health and wellness of the individual tracking his or her energy intake.

[0029] An assumption of the present invention is that heartbeats may be sorted into different classes of heartbeats, in particular, into three major classes, base heartbeats (BB), activity heartbeats (AB) and food heartbeats (FB), i.e.,

[0030] Total beats = TB = BB + AB + FB

[0031] Under this assumption, an activity heart rate may be considered to correspond to the rise / elevation in heart rate for an individual performing e.g., an activity, hence adding to an accumulation of total heartbeats. These extra accumulated heartbeats may be referred to as activity heartbeats. In the same way, the food heart rate may be considered to correspond to the rise in heart rate that may be attributed to the human consumption of one or more calorie-containing food items such as a meal or a beverage (i.e., energy intake) and hence result in additional accumulation of total heartbeats. These extra accumulated heartbeats may be referred to as food heartbeats. The basal heart rate and base heartbeats refer to the heart rate of an individual and the number of heartbeats associated with the basal heart rate, respectively, in the absence of activity and energy intake, i.e., heart rate and heartbeats due to the basal metabolism of an individual.

[0032] In some embodiments, base heartbeats (BB) are accumulated heartbeats that are attributable to basal physiological function. The base heartbeats may preferably be quantified within a defined time window. BB are determined relative to a basal heart rate reference and exclude heartbeats classified as activity heartbeats (AB) or food heartbeats (FB). Base heartbeats correspond to basal metabolic processes such as, but not limited to, resting autonomic function and sleep.

[0033] In some embodiments, food heartbeats (FB) are accumulated heartbeats attributable to postprandial thermogenic response following nutrient ingestion. FB are quantified as the integrated area under the one or more thermogenic food waves. This quantification may be carried out for heartbeats above a basal heart rate. The quantification may preferably be carried out within a defined timeP16006PC00 5

[0034] window. The accumulated food heartbeats may be used to calculate postprandial energy expenditure and / or infer energy intake using the food factor (FF).

[0035] In some embodiments, activity heartbeats (AB) are accumulated heartbeats attributable to physical activity (such as, movement, stress, or other non-basal physiological activation not associated with postprandial thermogenesis such as, but not limited to, walking, daily chores and exercising. AB may be identified as heartbeats exceeding basal and thermogenic food wave components within a defined time window. In some embodiments, the activity heartbeats may also be classified as the remainder, i.e., all heartbeats that are not attributable to the base heartbeats and food heartbeats. Accordingly, with the base beats and food beats known, the activity beats may readily be computed.

[0036] The metabolic parameters (may also be referred to as metabolic coefficients) are individualized proportionality constants that define the quantitative relationship between accumulated heartbeats and corresponding energy expenditure within distinct physiological domains. The metabolic parameters include, but are not limited to: base factor (BF), activity factor (AF) or intensity-adjusted activity factor (also, synonymously referred to as AF) and food factor (FF). Each metabolic parameter being a conversion ratio between (accumulated) heartbeats and metabolic energy expenditure for a defined heartbeat class.

[0037] The metabolic parameter(s) are individualized and may vary between subjects based on physiological characteristics, including but not limited to aerobic capacity, skeletal muscle mass, mitochondrial density, cardiovascular efficiency, body composition, age, and sex.

[0038] In certain embodiments, variation in metabolic parameters may reflect differences in oxidative capacity and peripheral substrate utilization, which are influenced by the quantity and functional capacity of aerobic muscle tissue. Individuals with greater aerobic capacity or greater aerobic muscle mass may exhibit lower heartbeat-to-energy ratios in one or more domains, whereas individuals with lower oxidative capacity may exhibit higher heartbeat-to-energy ratios for comparable energetic demand. Sex-related variation in metabolic coefficients may arise from systematic differences in body composition, relative skeletal muscle mass, haemoglobin concentration, and cardiorespiratory capacity. However, the metabolic coefficients are not limited to sex classification and may be determined on an individualized basis through calibration.

[0039] The method may further include a step of measuring and / or determining the metabolic parameters. The metabolic parameters may be determined through exercise testing, resting calibration, postprandial testing such as a feeding test, longitudinal monitoring such as monitoring an individual for extended period of time, or other physiological calibration methods.

[0040] In some embodiments, the method may include a step of measuring the at least one metabolic parameter through a standardized physical fitness test of the individual, referred to as metabolic profiling. One or more of the metabolic parameters may be measured from, or computed from one or more performance parameters measured from, metabolic profiling, wherein the metabolic profiling comprises a standardized physical fitness test as disclosed in W02021166000. The fitness test typically involves a sub-maximal session of exercise such as walking, running, biking or rowing,P16006PC00 6

[0041] using standard exercise equipment such as treadmills, stationary bikes or rowers in standard gym settings, but can alternatively be performed through the analysis of heart rate data during daily activities over a period of several days. The output to the user may include all the performance parameters required for the calculation of individualised metabolic parameters. Such a test advantageously allows the one or more metabolic parameters to be determined for an individual using a submaximal test which may be important for individuals with underlying sickness or diseases and elderly individuals.

[0042] The metabolic parameter(s) may be measured directly or computed from the results of such measurements. In the present invention, the metabolic parameter(s) may, e.g., be measured and stored in a data storage to be retrieved at a later time from the data storage. Alternatively, or additionally, one or more parameters of the at least one metabolic parameter may be provided to the processing unit from a data storage.

[0043] The metabolic parameters and / or variable obtained from such measurements may further comprise one or more of the following: basal metabolic rate (BMR), resting metabolic rate (RMR), total energy expenditure (TEE), thermic effect of food (TEF), exercise energy expenditure (EEE), non-exercise activity thermogenesis (NEAT), metabolic flexibility (the ability to switch between carbohydrate and fat as fuel sources), respiratory exchange ratio (RER), fat oxidation rate, carbohydrate oxidation rate, maximum fat oxidation (MFO), glycogen stores, fat stores, muscle glycogen depletion rate, insulin sensitivity, glycaemic response (GR), VO2 (Oxygen Uptake), VO₂max (Maximum Oxygen Uptake), VCO₂ (Carbon Dioxide Production), ventilatory thresholds (VT1 and VT2), maximum heart rate (HRmax), heart rate reserve (HRR), cardiac output (CO), lactate threshold (LT), maximum lactate steady state (MLSS), excess post-exercise oxygen consumption (EPOC).

[0044] Herein, the term "lactate threshold" may be used interchangeably with the term "maximum metabolic steady state" and "lactate turning point" or ventilatory threshold 2 (VT2).

[0045] The food factor (FF) is dependent on attributes of the individual and describes the relationship between the number of heartbeats and quantity of consumed energy (i.e., energy intake). The value of the FF is dependent upon the aerobic capacity of the individual and thus affected by gender, exercise and aging. The FF is specific to the individual and may primarily be assumed to be identical for all the macronutrients, i.e. fats, proteins and carbohydrates. Thus, the food factor describes how many additional heart beats are caused for each unit of energy (e.g. in kJ or kcal) consumed. Thus, the food factor (FF) may be considered to be an individualized metabolic parameter defining the proportional relationship between accumulated food heartbeats (FB) and postprandial energy expenditure (FEE). In certain embodiments, FF represents a ratio of heartbeats per unit of energy attributable to the thermogenic effect of food, i.e., the food factor (FF) is dependent on attributes of the individual and describes the relationship between the number of heartbeats and quantity of consumed energy (i.e., energy intake).

[0046] The food factor may vary between subjects based on physiological characteristics including, but not limited to, aerobic capacity, skeletal muscle mass, mitochondrial density, autonomic regulation, cardiovascular efficiency, body composition, age, and sex.P16006PC00 7

[0047] In certain embodiments, variation in FF may reflect differences in oxidative capacity and peripheral substrate utilization associated with the quantity and functional capacity of aerobic muscle tissue. Individuals with greater aerobic capacity or greater aerobic muscle mass may thus exhibit lower food factor values, corresponding to fewer heartbeats per unit of postprandial energy expenditure. Sex-related variation in FF may arise from systematic differences in skeletal muscle mass, relative aerobic capacity, hemoglobin concentration, hormonal milieu, and autonomic tone. However, FF is determined through individualized calibration and is not limited to categorical sex classification. As stated above, the FF may be assumed to be identical for all the macronutrients, i.e. fats, proteins and carbohydrates. However, in other embodiments, the FF may be dependent on the type of macronutrient. In such embodiments, the method may include a step of an individual inputting the type of macronutrients, e.g., through an input means configured on the wearable electronic device, of a meal and a step of estimating a TEF based on the inputted type of macronutrients.

[0048] In some embodiments, the food factor (FF) may be derived, estimated, or refined using physiological variables associated with aerobic capacity and skeletal muscle composition, wherein the Applicant has discovered that FF may correlate with measures of aerobic capacity, including maximal oxygen uptake (VO₂max), and / or with estimated aerobic muscle mass (AMM).

[0049] In certain embodiments, AMM may be determined as a function of total skeletal muscle mass (SMM) and an estimate of slow (aerobic) muscle fibre proportion: AMM = SMM-E where E represents an endurance or slow-fibre index. SMM may be estimated from performance-derived variables, including maximal speed (Vmax), endurance indices, or other exercise-derived parameters. In one embodiment: SMM = f(Vmax, E). In alternative embodiments, SMM may be measured directly or indirectly using body composition technologies including DEXA, MRI, BIA, anthropometric assessment, or other tissue-composition measurement systems. In certain embodiments, FF may be estimated directly from VO₂max without explicit calculation of SMM or AMM.

[0050] In other embodiments, machine-learning or regression models may be used to determine FF from combinations of aerobic capacity, body composition, and heart-rate calibration parameters.

[0051] The observed relationship between FF and indices of aerobic capacity suggests that FF reflects cardiovascular response efficiency relative to postprandial energetic demand. However, the metabolic tracking method does not require direct measurement of muscle mass or VO₂max and may operate solely on heart-rate-derived calibration parameters. The FF may be determined or computed from metabolic profiling (e.g., by carrying out a standardized submaximal fitness test) using the relationship between cardiorespiratory parameters (obtained from metabolic profiling) at estimated lactate threshold intensity (herein, also referred to as maximum metabolic steady state). In some embodiments it may be computed using the following equation:

[0052]

[0053] where FF equals the food factor (beats per kcal), P2 equals the heart rate at lactate threshold intensity (beats per minute), and T2 equals the velocity at lactate threshold intensity (km per hour).P16006PC00 8

[0054] Note that in W02021166000 intensity is presented on a T-scale and lactate threshold intensity thus as T2.

[0055] In some embodiments, the food factor may be computed using the following equation:

[0056] FF = 70.47 ■ (AMM)-1.08

[0057] where FF equals the food factor (beats per kcal) and AMM equals the aerobic muscle mass (kg). In some embodiments, the food factor may be computed using the following equation:

[0058] FF = 113.5 ■ (VO2max)-1

[0059] where FF equals the food factor (beats per kcal) and VO₂max equals maximal oxygen uptake (ml O2 per kg-1).

[0060] Alternatively, the food factor may be provided as a metabolic parameter measured through a standardized meal, i.e., postprandial monitoring such as a standardized postprandial thermogenic test.

[0061] As a non-limiting example, the FF may be quantified for different individuals and is often found to be in the range from 2 - 10 heartbeats per ingested kcal of food energy.

[0062] The base factor (BF) is an individualized metabolic parameter defining the proportional relationship between base heartbeats (BB) and basal energy expenditure (BEE). BF represents the number of heartbeats per unit of basal energy expenditure (e.g., beats per kilocalorie) under resting or baseline physiological conditions, including sleep and non-activity periods. In some embodiments, the base factor may be a function of time.

[0063] The activity factor (AF) is an individualized metabolic parameter defining the proportional relationship between activity heartbeats (AB) and activity-related energy expenditure (AEE). AF represents the number of heartbeats per unit of energy expenditure attributable to physical activity, movement, emotional arousal, or other non-resting physiological activation not associated with postprandial thermogenesis.

[0064] In embodiments where AF may be a function of heart rate it may comprise two or more transition phases, e.g., during low intensity (AFa), high intensity (AFb) and recovery (AFC). As a non-limiting example, the AF may comprise a transition phase during low-intensity activity, such as slow walking, where only slow type muscle fibres are recruited for the activity, in which the parameter rises in value with greater intensity of the activity, before reaching a fixed and stable value (AFb) during moderate intensity activity, such as running. Another comparable transition phase may also occur at higher exercise intensities in the initial phase of fast muscle fibre recruitment. The simultaneous recruitment of both fibre types during this later transition can create a small but measurable dip in the activity factor, reflecting the dynamic adjustment of cardiovascular and muscular systems. The activity factor may also have a recovery phase, AFC.P16006PC00 9

[0065] When the activity factor (AF) is a function, it may, herein, be referred to as either activity factor or activity-adjusted activity factor (herein may also be referred to as intensity-adjusted activity factor). The method may comprise a step of quantifying the base beats using the basal heart rate, e.g., by integrating the basal heart rate over an interval of time. In such embodiments, the basal heart rate may be provided as one of the at least one metabolic parameters or computed from the metabolic parameters.

[0066] The method may comprise a step of quantifying the activity beats using activity beats or activity heart rate.

[0067] The step of quantifying food heartbeats from the heart rate data may further comprise a step of subtracting the basal heart rate from the heart rate data, e.g., prior to quantifying the food beats and / or activity beats from the heart rate data.

[0068] The method may further comprise a step of dividing the heart rate data into consecutive first time segments and a step of identifying one or more points of minimum heart rate in each consecutive first time segment, and wherein the one or more food waves are fitted to the one or more points of minimum heart rate.

[0069] The method may further comprise a step of connecting / combining two or more food waves e.g., by removing possible overlap between the two or more food waves to obtain a heart rate response due to energy intake (also referred to as a combined food wave). Overlapping food waves may be combined through superposition, convolution, or other aggregation methods to generate a composite postprandial response.

[0070] A (thermogenic) food wave is a modelled time-dependent approximation of the postprandial heart rate response attributable to nutrient ingestion.

[0071] A food wave as defined and used herein is a mathematical function of time. A food wave preferably has a zero value at t=0, wherein t=0 represents the beginning of the interval on which the food wave is fitted. It is a unimodal or a unimodal-like function, preferably right skewed. In some embodiments, it may be substantially unimodal, i.e., include one dominant peak, and other less prominent peaks. In some embodiments, a food wave may be an exponential-type function, gamma distribution type function, or convolution-based function that has right-skewed unimodal form. Accordingly, in some embodiments, the food wave may be of the form:

[0072] F(t) = a ■ t ■ exp(—t / b~)

[0073] wherein the parameters a and b are determined by a fit to one or more points of minimum heart rate. The function may further have an additive constant c to be determined by the fit. In many embodiments, or applications, the parameter c will be set to zero. The parameter t may be expressed in units of time such as, but not limited to, in seconds, or in minutes, or in hours, or in days, or any combination thereof. Alternatively, a food wave may be expressed by a Gamma distribution with a(alpha) > 1, or a Weibull distribution with k > 1. The skilled person will understand that otherP16006PC00 10

[0074] mathematical functions than the above specified function, having similar characteristics, may work as well.

[0075] In some embodiments, the method may comprise a step of adjusting one or more heart rate values of the one or more points of minimum heart rate. Such a step is preferably carried out prior to fitting one or more food waves to the one or more points of minimum heart rate.

[0076] In some embodiments, the step of adjusting the values of the one or more points of heart rate data, and wherein the values of the heart rate data are adjusted according to a basal heart rate of the individual and / or adjusted according to an intensity-related elevation of resting heart rate and / or adjusted according to an activity heart rate, or any combination of adjustments thereof.

[0077] The one or more heart rate values may be adjusted according to the momentary base heart rate at the time of these one or more points of minimum value.

[0078] The one or more minimum heart rate values may be adjusted according to a predicted physical activity-related elevation of resting heart rate. In such embodiments, the method may comprise steps of: dividing the heart rate data into consecutive second time segments; estimating for each consecutive second time segment the mean intensity of activity; using the mean intensity of activity for each time segment to estimate EPOC (excess post-exercise oxygen consumption) related heartbeats and / or post exercise heart rate response; and, predicting the physical activity related elevation of resting heart rate. The predicted physical activity related elevation of the resting heart rate may then be used to adjust the heart rate value of the one or more points of minimum heart rate for activity related effects.

[0079] Additionally, or alternatively, the one or more minimum heart rate values may be adjusted using activity heart rate at the time of these one or more points of minimum heart rate value. In some embodiments, activity data may be used directly for adjusting the heart rate. In such embodiments, the method may further comprise a step of providing activity data to the processing unit.

[0080] The activity data and / or activity heart rate may be measured and provided by commercially available tracking devices and / or methods known in the field.

[0081] The adjustment of one or more points of minimum heart rate in the heart rate data may serve to lower the heart rate value of the one or more points of minimum heart rate, e.g., to, at least partially, exclude effects of activity or base heart rate in these points. Therefore, providing a more accurate heart rate response due to energy intake obtained from fitting and combining the one or more food waves to the adjusted one or more points of minimum heart rate and hence a more accurate estimate of the food beats may be obtained. However, the adjustment step is optional and the term "one or more points of minimum heart rate" should, therefore, be considered to encompass both adjusted and unadjusted one or more points of minimum heart rate.

[0082] In some embodiments, the activity beats may be obtained by integrating the activity heart rate over the interval of time spanned by the heart rate data. In some embodiments, the activity beats may be obtained for consecutive segments of the interval of time spanned by the heart rate data, i.e.,P16006PC00 11

[0083] partial activity heartbeats, such an embodiment is useful when an intensity adjusted activity factor is used.

[0084] The activity heart rate may be obtained by subtracting the basal heart rate and heart rate response due to energy intake from the heart rate data. Alternatively, the activity heart rate may be obtained from measured activity data such as by commercially available tracking devices, as mentioned above.

[0085] In some embodiments, the first time segments and the second time segments may be of different lengths / sizes. In one embodiment, the length of the first time segments may be 5 minutes or less, or 10 minutes, or 15 minutes, or 30 minutes, or 45 minutes, or 60 minutes, or any value therebetween. In one embodiment, the length of the second time segments, may be 1 minute or less, or 2 minutes, or 3 minutes, or 4 minutes, or 5 minutes, or 6 minutes, or 7 minutes, or 8 minutes, or 9 minutes, or 10 minutes, or any value therebetween.

[0086] In some embodiments, the interval of time used for an integration may be the range of time spanned by the heart rate data. In other embodiments, the interval of time used for an integration may be pre-defined such as, but not limited to, an hour or less, or two hours, or four hours, or eight hours, or sixteen hours, or twenty hours, or one day, or two days, or three days, or one week. However, the skilled person will understand that the interval of time may be limited by the interval spanned by the data. In some embodiments, the pre-defined interval of time may be inputted by a user. The step of determining the energy intake (El) comprises dividing the total number of food beats (FB) with the food factor (FF), i.e., by the equation:

[0087] El = FB / FF

[0088] The method may further comprise the step of determining one or more of the following energy expenditure parameters, such as (diet-induced) energy expenditure due to food intake (EEfood), energy expenditure due to physical activity (EEactivity), energy expenditure due to basal metabolism (EEbasal) and total energy expenditure (EEtotal).

[0089] The method may further comprise a step of determining energy balance (EEbalance).

[0090] In some embodiments, the method may further comprise a step of outputting the one or more energy expenditure parameters and / or energy balance.

[0091] The energy expenditure due to food intake (EEfood) may be determined by the equation:

[0092] EEfood= TEF · FB / FF

[0093] wherein TEF ("thermic effect of food") is typically a constant with a value, assumed to be the same for all the macronutrients, i.e. fats, proteins and carbohydrates. In some embodiments, the TEF constant may be selected to be in the range 0.05-0.2 such as 0.05, or as 0.06, or as 0.07, or as 0.08, or as 0.09, or as 0.10, or as 0.11, or as 0.12, or as 0.13, or as 0.14, or as 0.15, or as 0.16, or as 0.17, or as 0.18, or as 0.19, or as 0.20. In some embodiments, the TEF may be of different value for different macronutrients. In such embodiments, the user / individual may be required toP16006PC00 12

[0094] input type of food items or macronutrients, or optionally, the macronutrient division or composition of a mixed meal may be estimated.

[0095] The energy expenditure from basal metabolism may be determined by the equation:

[0096] EEbasal= BB / BF

[0097] The energy expenditure due to physical activity may be determined by the equation:

[0098] EEactivity= AB / AF

[0099] Or alternatively, by

[0100] EEactivity= ∑ABdt / AFdt

[0101]

[0102] where the activity factor is then an intensity-adjusted activity factor, i.e., a function of time.

[0103] The total energy expenditure may be determined by the following equation:

[0104] EEtotal= EEbasal+ EEactivity+ EEfood

[0105] The metabolic parameters BF, FF and AF are expressed in unit of heartbeats per energy (e.g., kcal), specific to the individual, but may also be subject to long-term changes in body composition, body weight and / or aerobic capacity of the individual. However, as described above, in some embodiments, the activity factor (AF) may further comprise a transition phase during low-intensity activity (such as slow walking) and only reach a stable value (AFb) during moderate intensity activity (such as slow running) The AF may further comprise another comparable transition phase at higher intensity in the early stages of fast fibre recruitment, during which the AF may decrease slightly below the stable value. The AF may further comprise a recovery phase.

[0106] In some embodiments, the activity-adjusted activity factor is heart-rate dependent.

[0107] In some embodiments, the method may further comprise a step of determining, by the processing unit, one or more energy expenditure parameters of the individual using one or more of the following: the food heartbeats and the food factor, the activity heartbeats and activity factor or an intensity adjusted activity factor, and the base heartbeats and the base factor and a step of outputting the one or more energy expenditure parameters to the individual.

[0108] Another advantage of the present invention is that it allows an individual to track both energy expenditure and energy intake. This will enable an individual to make more informed decisions about dietary choices such as portion sizes, nutrient balance and the required overall caloric intake to counterbalance the energy expenditure and achieve the individual's fitness and weight management goals, e.g., by using energy balance:

[0109] Ebalance= EI − EEtotal

[0110] wherein the condition Ebalance> 0 (a positive energy balance) means that the individual is consuming more energy than the individual spends over an interval of time. This may be beneficial when theP16006PC00 13

[0111] individual is trying to build up muscle mass. Similarly, a negative energy balance (Ebalance< 0) may be beneficial when the individual is trying to reduce bodyweight. When Ebalanceis approximately 0, the individual may be maintaining the physical state.

[0112] In some embodiments, an energy expenditure parameter due to activity is determined by: dividing the activity heart rate into time segments; estimating the partial activity heartbeats for each segment; dividing the partial activity heartbeats for each segment with the corresponding value of the intensity adjusted activity factor to obtain partial energy expenditure for each segment; and, summing up the partial energy expenditure for each segment over a range spanned by the activity heart rate.

[0113] In an aspect of the invention, a method for determining one or more energy parameters of an individual is provided.

[0114] The method includes steps of providing heart rate data of an individual as a function of time to a processing unit, providing one or more metabolic parameters of the individual to a processing unit, wherein the metabolic parameters comprise one or more of the following: basal heart rate, food factor, base factor and activity factor or an activity-adjusted activity factor, subtracting, by the processing unit, basal heart rate from the heart rate data, optionally estimating, by the processing unit, an physical activity related elevation of the resting heart rate, identifying, by the processing unit, one or more points of minimum heart rate in the heart rate data, optionally using, by the processing unit, the physical activity related elevation of the resting heart rate to adjust the heart rate values of the one or more points of minimum heart rate, fitting, by the processing unit, one or more food waves to the one or more points of minimum heart rate, connecting, by the processing unit, the one or more food waves (e.g., by removing overlap) for estimating a heart rate response due to energy intake, quantifying, by the processing unit, food heartbeats using heart rate response due to energy intake, quantifying, by the processing unit, base heartbeats using basal heart rate, subtracting, by the processing unit, the heart rate response due to energy intake from the heart rate data to obtain activity heart rate, quantifying, by the processing unit, activity heartbeats, or partial activity heartbeats, using the activity heart rate, determining, by the processing unit, one or more of the following energy parameters: energy intake using the food heartbeats and the food factor, energy expenditure due to energy intake using the food heartbeats and the food factor, energy expenditure due to activity using the activity heartbeats and activity factor or the partial activity heartbeats and the intensity adjusted activity factor, energy expenditure due to basal metabolism using the base heartbeats and base factor, and total energy expenditure, and, outputting, to the individual, one or more of the energy parameters.

[0115] In some embodiments, the partial activity heartbeats are determined for consecutive segments of the interval spanned by the heart rate data. In such embodiments, the energy expenditure due to activity is determined using the activity heartbeats determined for consecutive segments and the intensity adjusted activity factor.

[0116] In some embodiments, the method may comprise a step of measuring, by a wearable device, heart rate data of the individual as a function of time, wherein the wearable device is configured to provide the measured heart rate data to the processing unit.P16006PC00 14

[0117] In some embodiments, the method may comprise a step of measuring, by a wearable device, activity data of the individual as a function of time and a step of processing the activity data to activity heart rate.

[0118] In some embodiments, the wearable device is configured to provide the activity data or activity heart rate to the processing unit.

[0119] In some embodiments, the wearable device may not include a display. This will advantageously increase battery life of the wearable device. The cycle of the battery life of the wearable device is preferably at least 24 hours.

[0120] In some embodiments, the method may comprise a step of measuring the one or more metabolic parameters for the individual, through the standardized submaximal physical fitness test as described above.

[0121] In an aspect of the invention, a (non-transitory) computer-readable storage medium is provided, wherein the computer-readable storage medium comprises program instructions, which when executed by a processor, carry out the method(s) for determining the energy intake or energy parameters as described herein, or any embodiment thereof, or any combination of embodiments thereof.

[0122] In an aspect of the present invention, a system for determining energy intake of a human individual is provided.

[0123] The system comprises at least one processing unit configured to determine the energy intake of the individual according to any one of the foregoing aspects or embodiments, or any combination thereof, as described in the foregoing and below.

[0124] In an aspect of the present invention, a system for determining energy intake and / or expenditure of a human individual is provided.

[0125] The system comprises at least one processing unit configured to determine the energy intake and / or energy expenditure by carrying out the method(s) for determining the energy intake and / or energy parameters as described herein, or any embodiment thereof, or any combination of embodiments thereof.

[0126] The system further comprises an output means configured to output the energy intake and / or energy expenditure of the individual. In some embodiments, the output means may be a display configured to output the determined energy intake and / or energy expenditure to the individual. In some embodiments, the output means may be configured for transferring the determined energy intake and / or energy expenditure to a receiving electronic device, e.g., a mobile phone, a tablet, or a computer, on which the energy intake or energy parameters may be displayed.

[0127] The system may be configured to measure heart rate of the individual as a function of time. In such embodiments, the system may include a wearable electronic device configured to measure heart rate of the individual. Alternatively, the system may be configured to receive heart rate data from an external device.P16006PC00 15

[0128] The system may be configured to measure activity data such as movement-related accelerometery data of the individual as a function of time and obtain activity-related heart rate from the activity data. In such embodiments, the system may comprise a wearable electronic device configured to measure activity data of the individual. In some embodiments, the system may be configured to process the activity data to activity heart rate. Alternatively, the system may be configured to receive activity heart rate from an external device.

[0129] In some embodiments, the activity data may be measured from the same wearable electronic device as the heart rate data.

[0130] The system may further comprise a communication interface configured to receive heart rate data and / or activity data or activity heart rate and provide at least one processing unit with the received heart rate and / or activity data or activity heart rate.

[0131] The communication interface may be any medium known in the art that enables transfer of data to or from the system, and / or transfer of data between two or more components / devices of the system. The transfer of data may be carried out through a network such as, but not limited to, Wi-Fi, Bluetooth, Cellular network, or the Internet.

[0132] The communication interface may be configured to receive heart rate data from a wearable electronic device, wherein the wearable electronic device is configured to measure at least heart rate of the individual as a function of time.

[0133] The communication interface may be configured to receive activity data from a wearable electronic device, wherein the wearable electronic device is configured to measure activity data of the individual as a function of time.

[0134] In some embodiments, the communication interface may be configured to receive at least one metabolic parameter.

[0135] In some embodiments, the system may comprise an input means configured to input the at least one metabolic parameter and / or less preferably information about food items. The input means may for example comprise one or more interactive members such as, but not limited to buttons, for inputting the above-mentioned information.

[0136] In some embodiments, the system may comprise a means of receiving at least one metabolic parameter. In such embodiments, the means of receiving at least one metabolic parameter may be through direct input by an individual or transferred from an electronic device through the communication interface.

[0137] In some embodiments, the system may further comprise one or more non -transitory computer-readable memory such as, but not limited to, HDD or SDD. These may be used as one or more data storages for storing e.g., heart rate data and / or activity data and / or at least one metabolic parameter and / or analysis and / or results of data.

[0138] The at least one processing unit may further be configured to determine the energy expenditure, or parts thereof (i.e., any of EEfood, EEbasaland EEactivity) and / or the energy balance Ebalanceof theP16006PC00 16

[0139] individual using the computer-implemented method for determining the energy intake and / or energy expenditure according to any one of the embodiments, or any combination of embodiments, as described herein.

[0140] In some embodiments, the system may comprise an output means to output one or more of the determined energy parameters such as energy intake and energy expenditure, or parts thereof. In some embodiments, the output means may comprise a display unit and a graphical user interface for displaying the above-mentioned information.

[0141] The person skilled in the art will readily understand that embodiments, or combination of embodiments, of the method may be equally applicable to embodiments, or combination of embodiments, of the system and vice versa. The two types of aspects are closely related, sharing similar procedural steps and structural / functional features. Accordingly, features mentioned in embodiments of the method(s) apply to the system and vice versa and such embodiments with a combination of features taken from the particularly disclosed aspects of the method and system are encompassed by the invention and covered by this disclosure. The skilled person will be able to adapt embodiments from one to the another based on the disclosure, recognizing how the underlying technical features translate between procedural steps in the method and the structural or functional features of the systems. This flexibility ensures that the scope of the invention covers both aspects and all combinations of embodiments thereof.

[0142] BRIEF DESCRIPTION OF FIGURES

[0143] The foregoing and other aspects, features and advantages of the invention will be apparent from the following more particular description of particular embodiments of the invention, as illustrated in the accompanying drawings.

[0144] Fig. 1 shows a flowchart illustrating a method for determining one or more energy parameters such as energy intake and / or energy expenditure by quantifying one or more heartbeat components, in accordance with an embodiment of the present invention.

[0145] Fig. 2 shows a flowchart for a quantification of food beats, in accordance with an embodiment of the present invention.

[0146] Fig. 3 shows a flowchart for a quantification of activity beats, in accordance with an embodiment of the present invention.

[0147] Fig. 4 shows a flowchart a determination of one or more energy parameters, in accordance with an embodiment of the present invention.

[0148] Fig. 5(a) shows a schematic component diagram of an embodiment in accordance with the present invention.

[0149] Fig. 5(b) shows a schematic component diagram of an embodiment in accordance with the present invention, wherein the heart rate data is measured and provided by a wearable electronic device.P16006PC00 17

[0150] Fig. 6(a) shows a heart rate graph (bpm) illustrating an example heart rate response to a single-meal metabolic experiment comprising 1250 kcal, wherein the heart rate is fitted with an exponential growth and decay function (a food wave).

[0151] Fig. 6(b) shows heart rate graphs (bpm) for three differently sized meals. For clarity, only extra beats over basal heart rate are shown in the graph.

[0152] Fig. 7 schematically shows the concept of metabolic profiling through a submaximal exercise test, wherein the test comprises an exercise such as a walking or running with a warm-up and recovery phase.

[0153] Fig. 8 shows a diagram explaining the cardiorespiratory response to prolonged exercise at steady state intensity. For clarity, classification and sub-division of heartbeats is shown by insets.

[0154] Fig. 9(a) shows the relationship between heart rate and gait speed during both walking and running (an illustrative example).

[0155] Fig. 9(b) shows the relationship between heart rate response (trimmed heart rate) and gait speed during both walking and running (an illustrative example).

[0156] Fig. 10 shows the calculated recovery beats per km vs exercise intensity for a person weighing 75 kg and using 420 aerobic beats per km.

[0157] Fig. 11(a) shows an example of laboratory measurements of the activity factor (activity heartbeats per kcal of energy expenditure) during a submaximal treadmill exercise test with 1-kph speed increments (walking at 3-8 kph and running at 9-15 kph). The transition speed (Vt) is indicated on the x-axis.

[0158] Fig. 11(b) shows the mean value of the activity factor from the example in Fig. 11(a) plotted against the mean heart rate response (P) for each successive 1-kph speed increment from 3-13 kph on the treadmill. The AF-values during the walking phase are fitted with a logarithmic equation with details shown on the graph. The stable AF-value (AFb) and the transition response (Pt) are indicated on the axes.

[0159] Fig. 11(c) shows laboratory measured (black diamond markers) vs model-calculated (grey square markers) energy expenditure (EE) of a 77.7 kg male during an exercise test (5 min sitting rest, 40 min of walking / running at increasing speed, and 5 min of passive sitting recovery). The activity factor is indicated for different phases as AFa, AFb, AFC.

[0160] Fig. 12 shows a heart rate graph measured over 24 hours exemplifying the classification and quantification of heartbeats. For clarity, base beats are represented by the area in light grey, the activity beats by the area in dark grey and the remaining food beats by the area in white.

[0161] Fig. 13 shows an example of fitting of a plurality of food waves to a plurality of minimum heart rate points, wherein each food wave is initiated at a 5-min interval in Fig.P16006PC00 18

[0162] 13(a). Fig. 13(b) shows an example of how the multiple food waves may be connected and overlap removed to yield the food heart rate response due to energy intake. The food beats may then be quantified by integrating the area (heart rate * time) under the heart rate response.

[0163] Fig. 14(a) shows a screenshot from a GUI for a heart rate response to a 1200 kcal single meal ingested during 0 - 40 minutes of study time and 8 hours of heart rate measurements.

[0164] Fig. 14(b) shows another screenshot from the same GUI of the classification for the example presented in Fig. 14(a).

[0165] Fig. 15(a) shows a screenshot from a GUI for a quantification of heartbeats in an example of a full day validation study for participant ingesting regular meals throughout the day and performing an exercise session.

[0166] Fig. 15(b) shows a pie chart from a GUI detailing the division of energy expenditure from the example presented in Fig. 15(a).

[0167] Fig. 16 shows postprandial thermogenic heart rate responses (also referred to as heart rate due to energy intake) modelled using individual thermogenic food waves for eight study participants. Postprandial heartbeats were partitioned into food beats (dark grey shade) and activity beats (light grey shade). Basal beats are not shown. Fig. 17 shows agreement between measured and modelled energy intake (El) from FIG. 16.

[0168] Measured El (bars with dark shade) and metabolic tracking-derived El (bars with light shade) for each participant. Relative deviations are shown above paired bars.

[0169] DETAILED DESCRIPTION

[0170] In the below description, it will be appreciated that variations to the embodiments of the invention can be made while still falling within the scope of the invention. The exemplary embodiments are merely intended to better illustrate the invention and should not indicate a limitation on the scope of the present invention. Features disclosed in the specification, unless stated otherwise, can be replaced by alternative features serving the same, equivalent, or similar purpose.

[0171] Fig. 1 shows a flowchart illustrating a method for determining one or more energy parameters such as energy intake by quantifying one or more heartbeat components, in accordance with the present invention.

[0172] The method may be divided into several steps: providing biometric signals (100), providing or computing metabolic parameters (120), quantifying one or more heartbeat components (140), determining energy intake and / or energy expenditure (160) and outputting energy intake and / or energy expenditure (180).

[0173] The step of providing biometric signals (100) comprises providing heart rate data of an individual. In some embodiments, the biometric signals may further comprise activity data or activity (-related)P16006PC00 19

[0174] heart rate. These biometric signals may have been measured, as a function of time, over a previous period of time. The biometric signals, i.e., heart rate data and activity heart rate data, may be provided to a processing unit.

[0175] Accordingly, the present invention may optionally involve continuous monitoring of biometric signals (110) such as heart rate data with an external (or as a part of the system of the invention) electronic device such as, but not necessarily limited to, a wrist worn fitness tracker. Moreover, the present invention may also involve continuous monitoring of other biometric signals such as activity data by the electronic device, e.g., by measuring movement-related accelerometery data, from which activity heart rate data may be derived / obtained / provided.

[0176] In some embodiments, the present invention may comprise a wearable electronic device or a connection to such a device.

[0177] It is important to note that activity tracking is not essential to the present invention but may advantageously improve the accuracy in the determination of one or more energy parameters such as energy intake.

[0178] The measured and / or provided heart rate data may undergo a pre-processing step (not shown) including one or more of the following: an initial filtering of outliers and / or errors and / or spikes. The step of providing metabolic parameters (120) includes providing or computing one or more metabolic parameters for an individual. The metabolic parameters may comprise one or more of the following: food factor, activity factor and base factor. These parameters are specific to the individual and may in some embodiments be assumed to be independent of time over the interval of time comprised in the biometric signal measurement. The metabolic parameters are needed to convert heartbeats, of the different classes of heartbeats, to a quantity of energy for an individual.

[0179] The one or more metabolic parameters may be provided to a processing unit.

[0180] The metabolic parameter(s) may include one or more of the following: a food factor (FF), an activity factor (AF) and a base factor (BF). These factors describe the relationship between various types of heartbeats and quantity of energy (i.e., energy intake and energy expenditure). The metabolic parameter(s) may alternatively, or additionally, include other parameters such as, but not limited to, basal heart rate, basal metabolic rate, aerobic heart rate response, lactate threshold heart rate, lactate threshold speed or power and body-weight of the individual, some of which may be required to compute the food factor and / or the activity factor and / or the base factor.

[0181] The present invention can also determine many other key metabolic parameters either directly or indirectly, such as those listed herein below:

[0182] (i) Energy and Metabolic Rate Parameters: Basal Metabolic Rate (BMR), Resting Metabolic Rate (RMR), Total Energy Expenditure (TEE), Thermic Effect of Food (TEF), Exercise Energy Expenditure (EEE), Non-Exercise Activity Thermogenesis (NEAT), Metabolic Flexibility (the ability to switch between carbohydrate and fat as fuel sources).P16006PC00 20

[0183] (ii) Substrate Utilization Parameters: Respiratory Exchange Ratio (RER), Fat Oxidation Rate, Carbohydrate Oxidation Rate, Maximum Fat Oxidation (MFO), Glycogen Stores, Fat Stores, Muscle Glycogen Depletion Rate, Insulin Sensitivity, Glycaemic response (GR). (ill) Cardiovascular and Anaerobic Parameters: VO2 (Oxygen Uptake), VO₂max (Maximum Oxygen Uptake), VCO₂ (Carbon Dioxide Production), Ventilatory Thresholds (VT1 and VT2), Maximum Heart Rate (HRmax), Heart Rate Reserve (HRR), Cardiac Output (CO), Maximum Lactate Steady State (MLSS), Excess Post-Exercise Oxygen Consumption (EPOC).

[0184] Accordingly, the present invention may optionally involve measurement of the one or more metabolic parameters through metabolic profiling (130). Metabolic profiling may involve the analysis of the exercise-induced heart rate response and the calculation of personalized metabolic parameters, e.g., energy factors. The measurement of one or more metabolic parameters may be carried out through a standardized physical fitness test of an individual, referred to as metabolic profiling, such as, but not limited to, the standardized physical fitness test as disclosed in W02021166000.

[0185] The fitness test typically involves a sub-maximal session of exercise such as walking, running, biking or rowing, using standard exercise equipment such as treadmills, stationary bikes or rowers in standard gym settings. Alternatively, the fitness test can be performed outdoors, using a GPS-equipped fitness tracker. Indoor testing may preferably be performed at constant intensity or speed throughout the test, while speed variations are allowed in outdoor testing. The test typically involves a warm-up (3 minutes), an activity phase (5 - 20 minutes, depending on the chosen intensity), and a recovery phase (5 - 8 minutes). The warm-up and the recovery phase are optional but recommended for greater accuracy. In some embodiments, the test, or results thereof, may be uploaded to an electronic client such as, but not limited to, a smartphone wherein the smartphone may comprise a custom-made smartphone application and immediately analysed, using the analysis disclosed in W02021166000. In such embodiments, an output of the test may be provided to the user which includes all the performance parameters required for the calculation of individualised metabolic parameters.

[0186] Metabolic profiling can alternatively be performed through the analysis of heart rate data during daily activities over a period of several days (i.e., longitudinal test) or by a postprandial test Additionally, the method may include a step of storing at least one metabolic parameter in a data storage.

[0187] As discussed above, it is an assumption of the present invention that the heartbeats of an individual, and / or heart rate, may be sorted into three major classes of heartbeats and / or heart rate, respectively, i.e., base heartbeats (i.e., heart beats related to basal metabolism), activity heartbeats (i.e., heartbeats related to physical motion or emotional activity such as, but not limited to, walking, daily chores and exercising), and food heartbeats (i.e., additional heart beats related to energy intake). Therefore, with the heart rate data provided e.g., from measurements of biometric signals, one or more heartbeat components may be quantified from the heart rate data (140). The food heartbeats may be quantified from the heart rate data. Additionally, the base heartbeats and the activity heartbeats may be quantified from the heart rate data.P16006PC00 21

[0188] The base heartbeats may be quantified by integrating the basal heart rate (which may be provided as one of the metabolic parameters) over an interval of time.

[0189] Fig. 2 shows a flowchart for an embodiment of a method for the quantification of food beats from heart rate data (140a).

[0190] The method may include an optional step of subtracting basal heart rate from the heart rate data. In this step, the baseline heart rate for the individual (as e.g., determined from metabolic profiling) is subtracted from the heart rate data, thus removing the fraction of the heartbeats associated with baseline activity in the absence of motion activity. This still leaves all the activity- and food beats (activity and food-related heartbeats) to be separated according to the assumption of the present invention. The basal heart rate may be a constant as a function of time, i.e., a constant value subtracted from the heart rate data. Alternatively, the basal heart rate may be a function of time. In such embodiments, the basal heart rate as a function of time may have been previously measured as a function of time and provided as one of the parameters of the one or more metabolic parameters.

[0191] The heart rate data, measured over an interval of time, is divided into consecutive time segments (142). As a non-limiting example, if the interval of time is a day, the day may be divided into evenly spaced first time units. For example, the 24-hour day may be divided into 96 x 15-minute first time units. As the skilled person will recognize, other first time unit lengths may be equally applicable and are to be encompassed by the present invention. In some embodiments, the length of the first time units may be 1 minute or less, or 5 minutes, or 10 minutes, or 15 minutes, or 20 minutes, or 25 minutes, or 30 minutes, or even 1 hour. Furthermore, the skilled person will understand that the invention is not bound by the length of the interval of time. The interval of time may for example be 1 hour or less, 4 hours, 8 hours, 12 hours, 24 hours, 48 hours, 3 days, 7 days, 14 days, or a month, or any value therebetween.

[0192] With the heart rate data divided into consecutive time intervals, one or more points of mean minimum heart rate may be found / calculated for each first time unit (143) and optionally adjusted (147) (see further description below). The mean minimum heart rate in each first time unit may equal the average of a selected number of lowest heart rate values during a first time unit, e.g., 30-60 of the lowest heart rate values. The total number of values depends on the variation in the data. Each of these minimum values may optionally be adjusted (147) e.g., for the potential effects of excess post-exercise oxygen consumption (EPOC), as determined in the step of activity processing (148), e.g., by lowering each minimum heart rate value by the corresponding heart rate elevation. In a step of activity processing (148) the interval of time may be separated into consecutive second time units (herein, the term "time units" may also be referred to as "time segments"). As a nonlimiting example, a day may be by defined, by default, to start and end at 6:00 AM each day and then be divided into even second time units such as, but not limited to, 1440 x 1-minute units. As the skilled person will recognize, other start and end times may be chosen as well and that the length of a time unit need not be fixed to one minute. Indeed, other second time unit lengths may be equally applicable and are to be considered encompassed by the present invention. In some embodiments, the length of the second time unit may be 15 seconds or less, or 30 seconds, or 45P16006PC00 22

[0193] seconds, or 1 minute, or 1.5 minutes, or 2 minutes, or 5 minutes, or 10 minutes. Then, the intensity of the physical activity is calculated for each second time unit of the day. The mean intensity of the activity may then be calculated for each successive second time unit to estimate the associated EPOC and predict the physical activity-related elevation of RHR during exercise recovery. This physical activity-related elevation may then optionally be used to adjust the mean minimum heart rate values (147). In some embodiments, activity processing (148) may be carried out using the method disclosed in patent nr. W02021166000.

[0194] In some embodiments, where activity data (e.g., by direct activity tracking and classification) is available, the mean minimum heart rate values may also be adjusted for the possible effects of the activity heart rate during each first time unit.

[0195] In some embodiments, in particular in embodiments wherein the basal heart rate has not been subtracted from the heart rate data, the mean minimum heart rate values may further be adjusted by subtracting the corresponding basal heart rate.

[0196] These optional adjustments may serve to increase the accuracy in the determination of number of food heartbeats.

[0197] With the mean minimum heart rate values identified / calculated and optionally adjusted, one or more food waves are fitted to the (optionally adjusted) points of mean minimum heart rate (144), wherein the fit tries maximize the area and / or amplitude of the one or more food waves that may be fitted under the (optionally adjusted) minimum heart rate values, see e.g., Fig. 13(a). A food wave is a modelled time-dependent representation / approximation of the cardiovascular response attributable to nutrient ingestion (i.e., energy intake).

[0198] The thermogenic food wave may be represented by a right-skewed unimodal function such as in the form of an exponential function such as exponential decay, gamma-type distribution function, or convolution-based function, or other time-varying mathematical model capable of approximating the observed postprandial heart-rate trajectory. The functional form is not limited to any specific equation and may be determined through curve fitting, regression, optimization, machine learning, or other modelling techniques.

[0199] One advantage of the invention, is that by fitting one or more food waves, heart rate response due to energy intake associated with different types of macronutrients such as proteins, lipids and carbohydrates can be accounted for.

[0200] In some embodiments, the one or more food waves may be initiated at regular intervals, e.g., using a third time unit, over the interval of time spanning the heart rate data. The third time unit may have a length of 30 seconds or less, 1 minute, or 2 minutes, or 3 minutes, or 4 minutes, or 5 minutes, or 6 minutes, or 7 minutes, or 8 minutes, or 9 minutes, or 10 minutes, or even 15 minutes. Increasing the food wave frequency may be likely to increase the resolution of the method.

[0201] As a non-limiting example, if the third time unit is 5 minutes, a grid of 288 overlapping food waves is formed during every 24-hour day. The height and duration of each wave will be constrained by its specified dimensions and the minimum heart rate values during the next hours after initiation. Multiple thermogenic food waves may overlap in time to represent sequential or cumulative feeding events. The one or more food waves are then combined / connected (145) to obtain the heart rateP16006PC00 23

[0202] response due to energy intake (herein, also referred to as combined food wave). This step (145) may involve removing the overlapping areas between consecutive food waves to calculate the maximum allowable heart rate at each point in time that satisfies the constraints defined by the combined food wave. Overlapping waves may be combined through superposition, convolution, or other aggregation methods to generate a composite postprandial response.

[0203] According to the assumption of the invention, this step presumably removes all the activity beats, leaving only the food heart rate response related to the assimilation and digestion of ingested food, i.e., heart rate response due to energy intake. An example combined food wave may be visualized in Fig. 13(b) for the plurality of food waves in Fig. 13(a). Note that in this example, the combined food wave substantially retains the shape of a food wave. However, in many applications the shape of the heart rate response due to energy intake may be much more irregular, in particular, when dealing with heart rate data spanning a longer period of time.

[0204] The food beats may then be quantified (146) using the heart rate response due to energy intake, e.g., by integrating the area (heart rate x time) of the heart rate response due to energy intake. The integrated area under the food wave may be above the basal reference and corresponds to accumulated food heartbeats (FB), which may be used to quantify postprandial energy expenditure and / or energy intake.

[0205] Fig. 3 shows a flowchart for two embodiments of a method for the quantification of activity beats. In one embodiment the activity beats may be quantified (140b) by subtracting the basal heart rate (150) from the provided heart rate data. Obviously, if the basal heart rate has been previously subtracted from the heart rate data, e.g., in a step of quantifying food beats, the step should not need to be repeated. Then, the heart rate response due to energy intake may be subtracted from the heart rate data (151). According to the assumption of the invention, once the basal heart rate and the heart rate response due to energy intake have been removed, the remaining heart rate will correspond to activity heart rate. The activity heartbeats may then be computed (152) by integrating over the remainder heart rate which will correspond to the activity heart rate.

[0206] In some embodiments, the activity beats may be quantified (140b) by using commercially developed apparatuses / systems / methods configured to measure activity data and / or obtain / derive activity heart rate (153) and then from the activity heart rate compute the activity heartbeats (154).

[0207] Referring back to Fig. 1, with the one or more heartbeat components quantified after step (140), the energy intake may be determined (160). Additionally, one or more energy expenditure parameters, e.g., energy expenditure due to caloric / energy intake, energy expenditure due to basal metabolism, energy expenditure due to activity, or total energy expenditure, may be determined (160).

[0208] Once the heart rate data has been processed and the food heartbeats quantified, it is possible to determine the energy intake and energy expenditure due to caloric / food intake of an individual. If the activity heartbeats have also been quantified, the energy expenditure due to activity may be determined using the intensity-adjusted activity factor. If the basal heartbeats have also beenP16006PC00 24

[0209] quantified, the energy expenditure due to basal metabolism may be determined. If all of the above-mentioned energy parameters have been determined, it is possible to determine both the (total) energy expenditure and the energy balance of an individual.

[0210] As a non-limiting example of typical applications in quantification of heartbeats of the present invention, the basal metabolism (mostly organ function and static muscle function) may include about three quarters or more of the daily heartbeats and also a major part of the energy expenditure. The activity part includes routine activities, work and exercise and as such may be highly variable, ranging from about 5 to 25% of the beats and between about 10 to 50% of the energy expenditure. The food metabolism is usually the smallest part of the heartbeats and typically includes about 8-10% of the daily heartbeats and only about 4-5% of the daily energy expenditure.

[0211] Fig. 4 shows a flowchart for a determination of one or more energy parameters of an individual such as energy intake, energy expenditure and energy balance.

[0212] The energy intake (El) is based on the conversion of the food heartbeats (FB) using the food factor (FF) identified through metabolic profiling and may be determined (161) using the following equation: El = FB / FF.

[0213] The energy expenditure due to food intake may be determined (162b) using the following equation EEfood = TEF-FB / FF, wherein TEF ("thermic effect of food") is a constant value parameter. In some embodiments, the TEF constant may be selected to be in the range 0.05-0.2 such as 0.05, or as 0.06, or as 0.07, or as 0.08, or as 0.09, or as 0.10, or as 0.11, or as 0.12, or as 0.13, or as 0.14, or as 0.15, or as 0.16, or as 0.17, or as 0.18, or as 0.19, or as 0.20. Alternatively, the value of TEF may be a function of the macronutrients or type of food consumed.

[0214] The energy expenditure due to basal metabolism (162a) is based on the conversion of base heartbeats (BB) using the base factor (BF), identified through metabolic profiling, and may be determined (162) using the following equation: EEbasal= BB / BF.

[0215] The energy expenditure due to activity (162c) is based on the conversion of activity heartbeats (AB) using the activity factor (AF), identified through metabolic profiling, and may be determined (162) using the following equation: EEactivity= AB / AF.

[0216] In some embodiments, the energy expenditure due to activity (162c) may be obtained by using a constant valued activity factor. In such embodiments, it may be a weighted average activity factor, e.g., a weighted average of an intensity-adjusted activity factor.

[0217] In some embodiment, the energy expenditure due to activity (162c) may be obtained by conversion of activity heartbeats (AB) using the intensity-adjusted activity factor, wherein the momentary value of AF may be dependent on the momentary intensity of the activity or the heartrate (as discussed below). In such embodiments, the activity beats for a small time interval, dt, may be obtained, denoted by ABdt in Fig. 4, and divided by the value of the activity factor for the small time interval, denoted by AFdtin Fig. 4, to obtain the partial energy expenditure due to activity during the small time interval. This process may be repeated for a series of subsequent small time intervals, wherein the series may span the length of the heart rate data or a portion thereof. The partial energy expenditures may then be summed up (or integrated) to obtain energy expenditure due to activity.P16006PC00 25

[0218] The (total) energy expenditure (EE) is based on the conversion of all tracked heartbeats using the respective metabolic coefficients identified from the process of metabolic profiling. The total energy expenditure is the sum of the energy expenditure associated with basal metabolism, activity metabolism, and food metabolism. It may then be determined (162) using the following equation: EE = BB / BF + AB / AF + TEF*FB / FF = EEbasal+ EEactivity+ EEfood.

[0219] The calculation of the daily energy balance (EB) is based on the subtraction of the calculated energy expenditure (EE) from the calculated energy intake (El) and may be determined (163) with the following equation: EB = El - EE.

[0220] In some embodiments, the determination of energy expenditure parameters may be a live process (i.e., carried out in real time) estimating the momentary energy expenditure based on a prediction of an activity-related energy debt. Alternatively, or additionally, the determination of energy expenditure parameters may be a post-processing of heart rate data.

[0221] In some embodiments, the determination of energy intake (El), however, may be based on the postprocessing of heart rate data to obtain feeding-related heartbeats (i.e., food beats) and thus may lag actual energy intake by several hours. Referring to Fig. 1, with the energy parameters determined, i.e., energy intake and possibly other energy expenditure and energy balance, the one or more energy parameters may be outputted to an individual / user (180).

[0222] The step of outputting one or more energy parameters (180) may comprise displaying the one or more energy parameters using a display. Alternatively, the step of outputting one or more energy parameters (180) may comprise transferring the one or more energy parameters, or data thereof, to a second, possibly external device comprising a displaying unit such as to a mobile phone.

[0223] Fig. 5(a) shows a component diagram of an embodiment of a system in accordance with the present invention, wherein the system (1) is configured for determining energy parameters of an individual using heart rate data.

[0224] The system (1) includes at least one processing unit (2), a first data storage (3) and an output means (4).

[0225] The system (1) is configured to receive heart rate data of an individual. The system may further be configured to receive activity data or activity heart rate. In such embodiments, the system may comprise a communication interface (not shown) for communicating with one or more external wearable electronic devices configured to measure heart rate and / or activity data or activity heart rate, or both.

[0226] The at least one processing unit (2) configured to carry out the method(s) as described herein, e.g., as outlined in Fig. 1, or any embodiment thereof, or any combination of embodiments thereof. The system (1) may further comprise a memory allocation defined by: a second data store storing an executable asset and a working memory allocation, wherein the processing unit (2) is configured to load the executable asset from the data store into the working memory allocation to instantiate the determination of energy parameters such as energy intake, as described herein.P16006PC00 26

[0227] The first data storage (3) may include heart rate data (3a) received in real-time and / or historic heart rate data. The first data storage (3) further includes one or more metabolic parameters e.g., obtained from metabolic profiling. It may further include activity data or activity heart rate (3b) received in real-time and / or historic activity data or activity heart rate. The first data storage may further be used to store previous determinations (i.e., historic) of energy parameters such as energy intake (3d) and energy expenditure (3e).

[0228] In one embodiment, the output means (4) may comprise a display unit configured to display the one or more energy parameters to a user. In such embodiments, the system (1) preferably comprises a graphical user interface (GUI) displayed on the output means / display. A prototypical example of a GUI is shown in Figs. 13 and 14. In another embodiment, the output means may refer to transferring the determination of the energy parameters such as energy intake, or data thereof, to another external device, wherein the one or more energy parameters may be displayed to the user and / or processed by the second device.

[0229] Fig. 5(b) shows another embodiment of a system in accordance with the present invention.

[0230] In this embodiment, the system (1) may further comprise a wearable electronic device (5) configured to measure biometric signals such as heart rate data of an individual as a function of a time. In such embodiments, the wearable electronic device (5) may further be configured to measure biometric signals such as activity data and even process the activity data into activity heart rate. Alternatively, the activity data may be processed into activity heart rate by at least one processing unit (2). The wearable electronic device (5) being configured to transfer the biometric signals, i.e., the heart rate data and / or the activity data or activity heart rate, to the at least one processing unit through a network (7) such as Bluetooth, LAN or the internet. In some embodiments, the system (1) may comprise a communication interface (6) for receiving the biometric signals. The one or more energy parameters may then be determined and output as outlined above.

[0231] The wearable electronic device (5) of the present invention may comprise a battery (not shown). In some embodiments, the wearable electronic device (5) may be configured to measure heart rate. In such embodiments, it may comprise optical photoplethysmography (PPG) sensors to monitor heart rate. PPG is an optical technology that measures volumetric changes in blood by detecting light at the skin surface and utilizing photo detectors (PDs) to capture blood-related variations. The sensor emits light into the skin, and the PD measures the intensity of light that passes through or reflects back. Peaks in the pre-processed PPG waveform correspond to heartbeats and algorithms detect these peaks to calculate the heart rate.

[0232] In some embodiments, the wearable electronic device (5) may be configured to measure activity data. In such embodiments, the wearable electronic device may comprise one or more components configured to measure movement-related accelerometery data such as, but not limited to, accelerometers configured to measure motion in e.g., the three dimensions (X, Y, Z), gyroscopes configured to measure orientation and rotation, an altimeter configured to determine elevation changes, and GPS configured to measure location / position. The accelerometer may be configured to collect data on acceleration, frequency, duration, intensity, and movement patterns, and the rawP16006PC00 27

[0233] motion data are converted into meaningful metrics such as steps taken, activity levels, sleep quality and energy expenditure. In some embodiments, the present invention may be use and be combined with commercially available solutions for activity tracking.

[0234] In some embodiments, the wearable electronic device (5) may be configured to be worn on arm such as upper arm or forearm, or on wrist, or on finger, or on leg, or on torso of an individual. In some embodiments, the wearable electronic device (5) may not have a display unit, or at least a low-resolution display unit, to reduce battery consumption and increase the longevity of the battery cycle. In such embodiments, the wearable electronic device (5) will transfer the biometric measurements to at least one processing unit (2) of another device for processing of the measurements as described above.

[0235] EXAMPLES

[0236] Example 1

[0237] The effect of food consumption on a person's heart rate (the cardiorespiratory response to feeding) is illustrated in Fig. 6(a) and Fig. 6(b).

[0238] Fig. 6(a) shows the effect a single meal (in one metabolic experiment) can have on a person's resting heart rate for up to at least 10 hours after the food is consumed. The resting heart rate may increase by up to 10-15 beats per 1-2 hours but then decrease and return to the baseline 6-10 hours after eating.

[0239] Fig. 6(b) shows an example of the effect of three differently sized meals, wherein only extra beats over baseline are shown to emphasize the increase in heart rate response based on the magnitude of calorie consumption / caloric intake.

[0240] It is evident that the heart rate response to feeding is directly associated with TEF (i.e., the thermogenic / thermic effect of food digestion) and can be observed as a parabolic elevation of heart rate above the baseline level (resting heart rate) in the hours after consuming a meal. Following the ingestion of a meal, the heat production and the heart rate increase to a maximum before slowly tapering off back to baseline levels. The value and timing of the peak elevation are affected by the size and composition of the meal, as well as the fat-free mass and aerobic capacity of the individual. The parabolic elevation of heat production and heart rate can be simulated closely with an exponential decay function, as shown in Fig. 6(a), where the area under the curve corresponds to the increased heat production and heart rate above baseline levels.

[0241] The following general conclusions may be drawn from PhD-studies supervised by the Applicant: (I) the thermic effect of feeding is reflected in the resting heart rate post-ingestion, (ii) calorie intake is followed by a specific number of thermogenic heartbeats (food-beats), (ill) the relationship between calorie intake and food-beats is presented by a food-factor, (iv) the single-meal thermic effect increases linearly with the energy content of the meal, (v) the thermic response from a large single meal can last for up to approximately 8 hours post-ingestion, (vi) the food factor is strongly affectedP16006PC00 28

[0242] by gender, weight, max running speed, and endurance, and, (vii) females generally have a higher food factor than males.

[0243] The energy content of a meal may be estimated by measuring the extra beats above the basal heart rate, trimming away the activity beats leaving only the food beats, and translating the food beats into food energy, e.g., expressed in kcal.

[0244] It is possible to calculate the food beats from a single meal by integrating the area under the curve, i.e., from 0 to infinity. The exponential decay function has the following form:

[0245] F(x) = a · t · e(-t / b)

[0246] wherein, t is in units of time, e.g., minutes, and a and b are constants to be determined by the fit. The food beats may then be calculated as ab2(i.e., the integral over the exponential decay function). If the food factor (beats per ingested kcal of food energy) is known, e.g., has been determined through metabolic profiling, the calculated food beats may be translated into food energy intake using the food factor.

[0247] Example 2

[0248] The individual calibration of the proposed metabolic fitness tracker is performed through a process of "metabolic profiling". In such a process, one or more metabolic parameters are measured and stored. Herein, the terms "metabolic parameter" and "metabolic coefficient" may be used interchangeably.

[0249] Metabolic profiling may include the analysis of activity or heart rate data, or both, to calculate specific individual metabolic coefficients for the calculation of food energy intake and expenditure. Metabolic profiling typically requires an initial calibration test to be performed and the results of which may be used to obtain one or more metabolic parameters.

[0250] In some embodiments, the one or more metabolic parameters may be measured through a standardized test e.g., using the method disclosed in W02021166000. In other embodiments, postprandial calibration testing, longitudinal monitoring and exercise-based calibration, or combinations thereof may be used for one or more metabolic parameters.

[0251] In a calibration test, a user performs exercise (such as walking or running) at a known intensity (such as speed) for at least five minutes and subsequently engages in passive recovery (such as lying or sitting passively) for at least five minutes, as schematically illustrated in Fig. 7. This may be a submaximal test. The test, or results thereof, produce cardiorespiratory information about the user, such as aerobic endurance, resting heart rate (RHR), maximum heart rate (HRmax) and a range of metabolic thresholds, including the lactate threshold. Specifically, the food factor can be derived directly from the determined heart rate and intensity (such as speed or power) at the lactate threshold (also called maximum metabolic steady state (MMSS)) or lactate turn point (LTP) or ventilatory threshold 2 (VT2) in scientific literature).P16006PC00 29

[0252] The results may then be used to compute one or more metabolic coefficients (see e.g., list of equations and parameters) for energy / calorie tracking. In some embodiments, anthropometric information (gender, age, height, and weight) may also be needed for calculating e.g., the resting metabolic rate (RMR) of the user.

[0253] The one or more metabolic coefficients (parameters) for calorie tracking are based on the analysis of the cardiorespiratory response induced by the test. The diagram of Fig. 8 shows an illustrative heart rate response to an exercise session and the associated recovery heart rate response postexercise.

[0254] During exercise, food beats can optionally be excluded and the total heartbeats can be divided into three major classes i.e., base beats (BB), activity beats (AB) and recovery beats (RB). The recovery beats are further sub-divided into drift beats (DB) and EPOC beats (EPB). The heart rate response has a starting phase during which the heart rate rises quickly (phosphocreatine phase), until it reaches a steady state phase (glycogen phase) during which the heart rate rises much slower (cardiovascular drift). The method assumes activity beats to be associated with aerobic metabolism and recovery beats to be associated with anaerobic metabolism, lactate shuttling and glycogen replenishment. The drift beats are considered to be the first component of the ongoing exercise recovery. In the recovery phase, the activity factor may be referred to as AFC, assuming a slow exponential increase for about 5-15 minutes (see Fig. 10), depending on the exercise intensity and duration, until it equals the base factor, after which it remains stable during the rest of the recovery phase. To predict the total activity-related energy expenditure it is necessary to know the relationship between metabolism and exercise intensity.

[0255] The method analyses the heart rate response during exercise such as walking or running, as shown in Fig. 9. Fig. 9(a) illustrates the determination of performance factors (such as P2, T2 and Pzero) required for the computing of individualised metabolic parameters. Fig. 9(b) illustrates the relationship between heart rate response (trimmed heart rate) and gait speed during both walking and running for an aerobically fit individual.

[0256] The diagrams show that heart rate is lower during walking than running at slow to medium speeds, as walking is more economical. Heart rate increases exponentially with walking speed and linearly with running speed, intersecting at the "transition speed (Vt)" and "transition response (Pt)," where walking and running energy demands are equal. The linear slope of the running line (run factor (RF) = brun = 5.71) and the exponential slope of the walking line (walk factor (WF) = bwaik = 0.485) in Fig. 9(b), are specific to the individual and have the following general equations:

[0257] RF = brun = (P2 − Pzero) / T2 = Pt / Vt = e(Vt · bwalk) / Vt

[0258] WF = bwalk = ln(Pt) / Vt = ln(Vt · brun) / Vt

[0259] The relationship between WF and RF means that one slope can always be calculated from the other slope. This also means that the running heart rate response can be calculated from a submaximal testing of the walking heart rate response.P16006PC00 30

[0260] In some embodiments, basal heart rate (BHR) is the mean heart rate associated with the basal metabolic rate (BMR). Alternatively, basal heart rate may have diurnal variation over time and / or long-term variation over time, i.e., the basal heart rate may be a function of time.

[0261] The basal metabolic rate (BMR) is the amount of energy needed while resting in a temperate environment when the digestive system is inactive.

[0262] The method assumes the relative magnitude of the recovery heart rate to be entirely intensity dependent, as shown in Fig. 10. Fig. 10 shows how the number of recovery beats increases with exercise intensity for a person weighing 75 kg and using 420 aerobic beats per km of running. The derivation of metabolic parameters for an individual may be based on metabolic profiling to calculate maximum power output, e.g., by using the running model from Secret of Running (van Dijk & van Megen, 2016) [2].

[0263] In this model, power is described in watts, and sustainable output is determined by energy demands, calculated with the formula:

[0264] E = c ■ m - v

[0265] where m is body mass (e.g., in units of kg), v is speed (in units of length / time e.g., m / s), and c is a constant (0.98 kJ ■ kg1■ km1). The model assumes a fixed muscle efficiency of 25% and a running economy (RE) of 201 ml O2 ■ kg-1■ km-1which leads to the derivation of the constant c.

[0266] The total power (P) is the sum of running resistance (Pr), air resistance (P3), and climbing resistance (Pc). On level ground, the formula simplifies to P = Pr+ Pa= 0.98 ■ m ■ v + 0.1446 ■ v3. At zero incline, Pc= 0.

[0267] The method uses this formula to calculate power output from metabolic profiling. The calculation of individual metabolic parameters is summarized below (see also the List of equations and parameters).

[0268] The base factor (BF) describes the relationship between heart rate and basal metabolism. The BF can be calculated from the baseline heart rate (BHR) and the basal metabolic rate (BMR) through the following equation:

[0269] BF = BHR / BMR

[0270] Basal heart rate (BHR) may be the mean heart rate associated with the basal metabolic rate (BMR). The basal metabolic rate (BMR) is the amount of energy needed while resting in a temperate environment when the digestive system is inactive. BMR (kcal / min) can be calculated from the following equation:

[0271] BMR = 51.86 · (m · (h / 100)2.5)0.602

[0272] where m is body mass (kg) and h is height (cm).P16006PC00 31

[0273] BMR can alternatively be estimated as RMR (resting metabolic rate) with the equation published by Pavlidou et al. [1] and calculated from anthropometric factors using the following equations:

[0274] RMR males: (9.65 ■ weight in kg) + (573 ■ height in m) - (5.08 ■ age in years) + 260 (12) RMR females: (7.38 ■ weight in kg) + (607 ■ height in m) - (2.31 ■ age in years) + 43 (13) The base factor (BF) can also be determined from basal heart rate (BHR), basal metabolic rate (BMR) and the performance parameters (P2, T2 and RF), using the following general equation:

[0275] BF = (P2 - T2 ■ RF) / BMR

[0276] The method adjusts the BHR and the BMR according to anticipated body position, mainly sitting during waking hours and lying during sleeping hours.

[0277] The activity factor (AF) describes the relationship between heart rate and active energy expenditure. The AF can be calculated directly from the cardiorespiratory response (see Fig.8) using the following equation:

[0278] AF = ((HRa- BHR) / v) ■ 60 ■ ECF / m

[0279] where HRaequals the aerobic heart rate response (intercept with the y-axis on a HR vs speed graph), BHR equals the basal heart rate, v equals the constant running speed in km / hour, ECF equals the economy factor, i.e., ECF = 1 + 1.5 ■ (10E) / 100) and m equals body weight in kg

[0280] Calculating energy expenditure from heart rate is challenging because the activity factor (AF) varies with intensity during walking and running. At moderate to high intensities, such as running, AF stabilizes at a maximum value). However, during low-intensity activity like walking, AF is intensitydependent and variable due to a transition phase. Physiologically, this can be explained as follows: Initially, oxygen demand during walking is met primarily by muscle oxygen stores (myoglobin and haemoglobin), causing a slower heart rate increase and giving the false impression of high oxygen delivery per heartbeat. As walking speed increases, oxygen stores contribute less, and the heart must deliver more oxygen, reflected in a gradually rising AF. In the running phase, oxygen store contribution becomes negligible, stroke volume maximizes, and heart rate increases proportionally with oxygen demand. This is illustrated in Fig. 11(b) as the activity factor (AF = beats per kcal) rises gradually during the walking phase towards a stable value (AFb) at the transition speed (Vt, Fig. 11(a)) or transition response (Pt, Fig. 11(b)). Note that the contribution of AFcis not included in Fig. 11(a) and Fig. 11(b).

[0281] The method can also use Bernoulli's equation to model muscle fibre recruitment and the heart rate response [3]. The recruitment of slow-twitch fibres at low intensities causes an initial heart rate rise disproportionate to energy demand. This parallels Bernoulli's principle, where a smaller "tank height" (fewer recruited fibres) requires a higher "flow rate" (oxygen uptake per fibre). The flow rate, Q, is proportional to the square root of the tank's height and can be calculated as Q = √(2gh) c0n (d / 100)2 / 4, where g is gravity, h is liquid height, cO is the discharge coefficient, and d is the drain diameter. At higher intensities, fast oxidative fibres are also recruited and undergo a similar transition phase, initially causing a disproportionate heart rate rise before stabilizing. TheP16006PC00 32

[0282] simultaneous recruitment of both fibre types then creates a temporary small dip in the activity factor, reflecting dynamic cardiovascular and muscular adjustments.

[0283] To enable an accurate and realistic calculation of energy expenditure from heart rate it is thus essential to model the activity factor in the transition phase. Fig. 11(b) illustrates that AF has a logarithmic relationship with the heart rate response (P) during the transition phase (above Pzero) and has the following general equation:

[0284] AFa,b= max(b ■ ln(P), AFb)

[0285] where b equals the slope (b = AFb / ln(Pt)), P equals the heart rate response (above Pzero), AFbequals the max activity factor, and Pt equals the transition response. Using this equation, heart rate can be accurately translated into energy expenditure at any given intensity without specifying the mode or speed of the activity. This includes a calculation of the energy required for replenishment of muscle oxygen stores during post-activity resting, as explained in Fig. 10. Note that the contribution of AFcis omitted in the above equation.

[0286] In some embodiments, the AFcmay be computed with the following equation:

[0287] AFc= min(AFb+ AFb / 100 ■ t, BF)

[0288] wherein t is recovery time and BF is the base factor.

[0289] Alternatively, the AF in the transition phase can be calculated by first translating heart rate into walking speed (v) and then calculating AF from the walking speed with the following equation:

[0290] AF = ((0.0215 ■ FF) ■ (v ■ 7.8 / Vt)2+ (0.012 ■ FF) ■ (v ■ 7.8 / Vt) + FF) ■ (AFb / FF) / 2.4 where v equals speed, FF equals food factor, Vt equals the transition speed and AFb equals the stable activity factor.

[0291] The stable activity factor (AFb) has the following general equation:

[0292] AFb= RF ■ 60 ■ ECF / m

[0293] where RF equals the Runfactor, ECF equals the economy factor (ECF = 1 + 1.5 ■ (10E) / 100) and m equals body weight in kg. The AFbcan also be determined directly from a walking test since the RF can alternatively be derived from the transition speed, transition heart rate and Pzero.

[0294] The food factor (FF) describes the relationship between heart rate and food metabolism. The FF is specific to each individual and is determined by the individual's aerobic capacity. More specifically, the capacity for glucose uptake from the blood stream is directly related to the mitochondrial capacity, which again is determined by the number and efficiency of the mitochondria. Thus, with greater mitochondrial capacity, fewer heartbeats are required to absorb the glucose and other nutrients associated with meal digestion.

[0295] In some embodiments, the FF can be quantitatively determined through metabolic profiling (a standardized fitness test) from the relationship between cardiorespiratory parameters (P2 and T2) at lactate threshold intensity, and can be estimated through the following equation:P16006PC00 33

[0296] FF = P2 / (5 ■ T2)

[0297] where FF equals the food factor (beats per kcal), P2 equals the heart rate at lactate threshold intensity (beats per time unit), and T2 equals the velocity at lactate threshold intensity (distance per time unit), as explained in Fig. 9.

[0298] In some embodiments, FF can be measured directly through standardized meal intake.

[0299] In certain embodiments, the food factor (FF) may be derived, estimated, or refined using physiological variables associated with aerobic capacity and skeletal muscle composition. The Applicant has determined that FF may correlate with measures of aerobic capacity, including maximal oxygen uptake (VO2max), and with estimated aerobic muscle mass (AMM).

[0300] In certain embodiments, AMM may be determined as a function of total skeletal muscle mass (SMM) and an estimate of slow (aerobic) muscle fiber proportion: AMM = SMM-E where E represents an endurance or slow-fiber index. SMM may be estimated from performance-derived variables, including maximal speed (Vmax), endurance indices, or other exercise-derived parameters. For example, in one embodiment: SMM = f(Vmax, E). In another embodiment, SMM = Vmax ■ 0.015 + E / 10.

[0301] In alternative embodiments, SMM may be measured directly or indirectly using body composition technologies including DEXA, MRI, BIA, anthropometric assessment, or other tissue-composition measurement systems.

[0302] In certain embodiments, FF may be estimated directly from VO₂max without explicit calculation of SMM or AMM. For example, if Vmax and / or E are not known, FF can be estimated from V02max, either lab-derived or approximated. FF is strongly correlated with both AMM and V02max. In one embodiment, FF = 113.5 ■ (VO2max)-1. In some embodiments, FF = 70.47 ■ (AMM)-1.08.

[0303] BMR can be derived from body mass (m) and height (h). In some embodiments, BMR = 51.86 ■ (m ■ (h / 100)2.5)0.602.

[0304] In other embodiments, machine-learning or regression models may be used to determine FF from combinations of aerobic capacity, body composition, and heart-rate calibration parameters.

[0305] The observed relationship between FF and indices of aerobic capacity suggests that FF reflects cardiovascular response efficiency relative to postprandial energetic demand. However, the metabolic tracking method does not require direct measurement of muscle mass or VO₂max and may operate solely on heart-rate-derived calibration parameters.

[0306] The food factor may be directly related to the capacity for glucose uptake and may therefore also be estimated or predicted from the individual's glycemic efficiency. Accordingly, in some embodiments, the food factor may be estimated from the capacity of an individual for glucose uptake. In such embodiments, the glycemic response (GR) to meal ingestion can thus be estimated with the following equation: GR = FF / 6, where GR equals the relative glycemic response (ratio of peak-to-baseline glucose level) to a standardized 500 kcal carbohydrate meal and FF equals the food factor.P16006PC00 34

[0307] Example 3

[0308] As discussed above, the example in Fig. 6(a) shows the experimental measurements of resting heart rate after the consumption of one large meal. This may be done in specially organized studies but is rarely a viable option in people's daily lives. People are moving throughout the day and usually consume many small meals. It can therefore be very difficult and complicated to separate the food beats from the activity beats (beats related to motion activity). The greater the motion activity, the more difficult it is to distinguish the food beats exactly.

[0309] Accordingly, the Applicant realized that the daily total heartbeats need to be categorized into three classes, namely, activity beats, food beats and base beats, and quantified as schematically illustrated in Fig. 12. In the example of Fig. 12, a total of over 124,000 beats were measured over a 24-hour period from a person who did some strenuous exercise in the middle of the day (see large heart rate peak of Fig. 12). After analysing and sorting the data (in this example, base beats and activity beats were identified first), approximately 8,000 beats remained that could not be explained by either activity- or basal metabolism. It may be assumed that these beats are due to food consumption (i.e., food beats) and may therefore be used to estimate the daily food intake for this person over the 24-hour period.

[0310] Comparative example 1

[0311] The example in Fig. 14(a) shows the heart rate of a study participant after ingesting a 1200 kcal meal ingested during a 0 - 40 minutes of study time. The diagram shows that the heart rate remained elevated during the meal ingestion but then assumed a wave-like growth and decay pattern during the rest of the experiment (eight hours). Heart rate minimums were reached during passive resting phases approximately every half hour. The heart rate data are systematically processed as described herein and the results are shown in Fig. 14(b).

[0312] The diagram in Fig. 14(b) shows the final result after processing the heart rate data from the example in Fig. 14(a). The heart rate data is adjusted to the base heart rate and the activity heart rate and a largest possible food wave fitted to the data. The accumulated heartbeat count and the heartbeat classes are shown in the diagram.

[0313] In this example, the metabolic coefficients have been identified for the participant as BF = 39.36 (base factor), AF = 6.56 (activity factor) and FF = 2.46 (food factor), as previously explained in example 2. For clarity, the AF equals AFbin this example and has not been intensity- or recovery adjusted, which would result in different energy expenditure values.

[0314] The number of food beats (FB) in this example is calculated as 2949 beats (by integrating the „food beats area" in the graph), which means that the food factor (FF) of this study participant can be calculated as 2949 beats / 1200 kcal = 2.46 beats / kcal. The active energy expenditure can be calculated as AE = AB / AF = 2988 / 6.56 = 455 kcal. The base energy expenditure can be calculated as BE = BB / BF = 24502 / 39.36 = 623 kcal.

[0315] It may be assumed that feeding energy expenditure (FE) equals 10% of the food energy or FE = 120 kcal (TEF = 10%). The total energy expenditure during the experiment can thus be calculatedP16006PC00 35

[0316] as EE = BE + AE + FE = 623 + 455 + 120 = 1198 kcal. The energy balance (EB) is therefore calculated as EB = El - EE = 1200 - 1198 = 2 kcal (positive energy balance).

[0317] Comparative Example 2

[0318] The method disclosed in this application has been validated by the Applicant through both single meal and multiple meal testing. Single meal testing without exercise is the easiest scenario, while whole-day testing with multiple meals, daily activities and exercise has proven more challenging. Illustrative results from whole day tracking from a validation study can be seen in Fig. 15. The diagram in Fig. 15(a) shows the final result after processing the heart rate response of a study subject ingesting regular meals throughout the day and performing a long and strenous exercise session in the late afternoon. The study subject (23 year old female) kept an accurate diet-log throughout the day (and many more days) so that the actual energy intake for the whole day could be calculated accurately. This was a test day with relatively high energy intake (2960 kcal) and relatively vigorous exercise.

[0319] The heart rate was recorded continously throughout the 24-hour day (with a Polar chest strap monitor) and the recording uploaded to the Applicant's metabolic model. The heart rate data was processed into base- and activity heart rate and the largest possible food waves fitted to the activity-adjusted data as explained in detail in this application.

[0320] In this example, the resting heart rate (RHR) was determined from metabolic profiling as 47.0 bpm. The numbers of base beats (BB), activity beats (AB) and food beats (FB) were determined as 67,633, 28,987, and 8,377, respectively. The metabolic coefficients were determined as BF = 48.8, AF = 13.2 and FF = 2.6. Again for clarity, the AF equals AFbin this example and has not been intensity-or recovery adjusted, which would result in a slightly different EE values. The total energy expenditure during the whole day could thus be calculated as EE = BE + AE + FE = 1386 + 2194 + 320 = 3900 kcal. It was assumed that feeding energy expenditure (FE) equals 10% of the food energy. The energy expenditure division is shown as a pie chart in Fig. 15(b). The pie chart shows that about 60% of the energy expenditure (kcal) was associated with physical activity.

[0321] The energy intake (El) could be estimated as El = FB / FF = 8242 / 2.6 = 3170 kcal / day. The actual recorded energy intake was 2960 kcal, which means that the energy intake was overestimated in this case by approximately 7%. This accuracy can be considered satisfactory for whole day tracking with strenous exercise. The calculated energy balance (EB) for the whole day can therefore be calculated as EB = El - EE = 3900 - 3170 = -703 kcal (negative energy balance). This is an extreme case with very high active energy expenditure due to a two hour long and strenous exercise session. Comparative example 3

[0322] The method disclosed in this application was further evaluated in a controlled feeding study involving eight adult participants. Each participant completed a standardized Postprandial Thermogenic Test (PTT) under controlled laboratory conditions without structured exercise during the postprandial observation window. Heart rate was recorded continuously throughout the test using wearable heart rate monitors and uploaded to the Applicant's metabolic tracking model.P16006PC00 36

[0323] For each participant, the recorded heart rate time series was processed to determine basal heart rate (BHR) and to partition total heartbeats into base beats (BB), activity beats (AB), and food beats (FB), as described in detail in this application. Briefly, a basal reference was established from minimum heart rate values observed during the test window, optionally adjusted for movement artifacts using activity data. A thermogenic food wave was then fitted to the postprandial heart rate response. The food wave was modelled as a right-skewed unimodal function approximating the timedependent thermogenic response to the standardized meal. The integrated area under the food wave above the basal reference defined the accumulated food heartbeats (FB). Heartbeats exceeding the combined basal and food components were classified as activity heartbeats (AB).

[0324] Individualized metabolic coefficients were determined for each participant through prior metabolic calibration, including base factor (BF), activity factor (AF), and food factor (FF). Using these coefficients, total energy expenditure (EE) during the PTT was partitioned into basal energy expenditure (BEE), activity-related energy expenditure (AEE), and food-related energy expenditure (FEE). Energy intake (El) was inferred from accumulated food heartbeats using the relationship:

[0325] El = FB / FF

[0326] Fig. 16 shows representative trimmed heart rate signals for all eight participants with corresponding fitted thermogenic food waves and heartbeat partitioning into food and activity components. Despite inter-individual variability in heart rate dynamics, meal-induced thermogenic amplitude, and activity-related fluctuations, the method consistently identified postprandial food waves and quantified food heartbeats for all subjects.

[0327] Across the eight participants, substantial individual variation was observed in thermogenic wave amplitude, temporal profile, and accumulated food heartbeats. Variation was also observed in the individualized food factor (FF), reflecting differences in physiological response to postprandial energetic demand. In certain embodiments, such variation may correspond to differences in aerobic capacity, skeletal muscle mass, and oxidative metabolism, although the operation of the method does not require direct measurement of such variables.

[0328] The results, summarized in Fig. 17, show agreement between measured and modelled energy intake (El). Measured El (bars with a darker shade) and metabolic tracking-derived El (bars with a lighter shade) for each participant. Relative deviations are shown above paired bars.

[0329] The method demonstrated consistent partitioning of heartbeats and reproducible estimation of postprandial food-related energy across all participants. The results confirm that the metabolic tracking framework can be applied to multiple individuals under controlled feeding conditions and is robust to inter-individual variability in heart rate dynamics.P16006PC00 37

[0330] LIST OF FEATURES

[0331] Feature Description

[0332] Fig. 1

[0333] (100) Providing biometric signals

[0334] (HO) Measuring biometric signals

[0335] (120) Providing and / or computing metabolic parameters

[0336] (130) Metabolic profiling

[0337] (140) Quantifying one or more heartbeat components

[0338] (160) Determining energy intake and / or energy expenditure

[0339] (180) Outputting energy intake and / or energy expenditure

[0340] Fig. 2

[0341] (140a) Quantifying food beats

[0342] (141) Optional: subtracting basal heart rate from heart rate

[0343] (142) Dividing heart rate into consecutive time segments

[0344] (143) Finding one or more points of mean minimum heart rate in each time segment (144) Fitting one or more food waves to one or more (adjusted) points of minimum heart rate

[0345] (145) Connecting the one or more food waves to form a heart rate response due to El. (146) Computing food beats

[0346] (147) Adjusting one or more points of minimum heart rate.

[0347] (148) Activity processing

[0348] Fig. 3

[0349] (140b) Quantifying activity beats

[0350] (150) Subtracting basal heart rate

[0351] (151) Subtracting heart rate response due to energy intake

[0352] (152) Computing activity beats as remainder

[0353] (153) Using commercially developed methods

[0354] to measure / obtain activity heart rate

[0355] (154) Computing activity beats

[0356] Fig. 4

[0357] (161) Determining energy intake

[0358] (162) Determining total energy expenditure

[0359] (162a) Determining energy expenditure due to basal metabolism

[0360] (162b) Determining energy expenditure due to energy intake

[0361] (162c) Determining energy expenditure due to activity

[0362] (163) Determining energy balance

[0363]

[0364] P16006PC00 38

[0365] LIST OF USEFUL EQUATIONS AND PARAMETERS

[0366] Body mass (kg) = m

[0367] Body height (cm) = h

[0368] Endurance (%) = E (determined from Metabolic profiling)

[0369] Lactate threshold heart rate (beats per minute, bpm) = P2 (determined from Metabolic profiling) Threshold speed = T2 (determined from Metabolic profiling)

[0370] Running speed (kph) = v (determined from Metabolic profiling)

[0371] Maximum running speed (kph) = Vmax(determined from Metabolic profiling)

[0372] Max power (watts) = Pmax= 0.98 ■ m ■ Vmax+ 0.1446 ■ (Vmax)3

[0373] Pt = walk / run transition HR (bpm)

[0374] Vt = walk / run transition speed (kph)

[0375] Aerobic heart rate response (bpm) = HRa(determined from Metabolic profiling)

[0376] Maximum heart rate (bpm) = HRmax(determined from Metabolic profiling)

[0377] Basal heart rate (bpm) = BHR (determined from Metabolic profiling)

[0378] BHR percentage = BHRp = BHR / HRmax= 0.4 - (0.004 ■ (Vmax■ (1 + 0.00016 ■ (Vmax)2))) Resting heart rate (bpm) = RHR (= minimum momentary heart rate)

[0379] Standing heart rate at zero speed = Pzero

[0380] Run factor = RF = (P2 - Pzero) / T2

[0381] Basal metabolic rate (kcal / min) = BMR = 51.86 ■ (m ■ (h / 100)2.5)0.602

[0382] Economy factor = ECF = 1 + 1.5 ■ (10E) / 100

[0383] Activity factor = AF = (AB+RB) / (AE+RE) = ((HRa- BHR) / v) ■ 60 / m

[0384] AFa = bwalk* ln(HRa)

[0385] bwalk= AFb / ln(Pt)

[0386] AFt = RF ■ 60 ■ ECF / m

[0387] Base factor = BF = BHR / BMR = (P2 - T2 ■ RF) / BMR

[0388] Food factor = FF = P2 / (5 ■ T2)

[0389] FF = 113.5 ■ (VO2max)-1

[0390] FF = 70.47 ■ (AMM)’1 08

[0391] Total beats = TB = BB + AB + FBP16006PC00 39

[0392] Base beats = BB = TB - AB - FB

[0393] Activity beats = AB = TB - BB - FB

[0394] Food beats = FB = TB - BB - AB

[0395] Energy expenditure = EE = BB / BF + AB / AF + FB / FF ■ TEF

[0396] Energy intake = El = FB / FF

[0397] Energy balance = Ebalance= EB = EI - EE = EI - EEtotal

[0398] EEfood = FE = TEF ■ FB / FF = TEF ■ El

[0399] EEbasal= BE = BB / BF

[0400] EEactivity= AE = AB / AF

[0401] EEtotal= EE = EEbasal+ EEactivity+ EEfood

[0402] Exercise intensity = T (on a scale from 0 - 5)

[0403] RMR males = (9.65 ■ weight in kg) + (573 ■ height in m) - (5.08 ■ age in years) + 260

[0404] RMR females = (7.38 ■ weight in kg) + (607 ■ height in m) - (2.31 ■ age in years) + 43

[0405] AFa,b= max(bln(P), AFb)

[0406] AFC= min(AFb+ (AFb / 100) * t, BF)

[0407] AFb= 42.01 ■ HRa / MaxP / (1.315 - 0.0007 ■ MaxP)

[0408] AFa,b= max(bwaik ■ In(HRa), AFb)

[0409] AFa,b= max(((0.0215-FF) ■ (v-7.8 / Vt)A2 + (0.012-FF) ■ (v-7.8 / Vt) + FF) ■ (AFb / FF) / 2.4, AFb) Flow rate (tank system analogy) = Q = V(2gh) cOn (d / 100)A2 / 4

[0410] RF = brun = (P2 - Pzero) / T2 = Pt / Vt = e(Vt*bwalk) / Vt

[0411] WF = bwalk= ln(Pt) / Vt = ln(Vt ■ brun) / Vt

[0412] References:

[0413] [1] Pavlidou, E., Papadopoulou, S. K., Seroglou, K., 8t Giaginis, C. (2023). Revised Harris-Benedict Equation: New Human Resting Metabolic Rate Equation. Metabolites, 13(2), 189.

[0414] [2] van Dijk, H. 8t van Megen, R. (2016). The Secret of Running: Maximum Performance Gains Through Effective Power Metering and Training Analysis (1sted.). Meyer 8t Meyer Sport (May 1, 2017).

[0415] [3] Young, H. D., Freedman, R. A., 8t Ford, A. L. (2015). University Physics with Modern Physics (14th ed.). Pearson.P16006PC00 40

[0416] Clauses describing representative non-limiting embodiments:

[0417] 1. A method for determining one or more energy parameters of an individual, comprising: a. providing heart rate data of an individual as a function of time to a processing unit; b. providing one or more metabolic parameters of the individual to a processing unit, wherein the metabolic parameters comprise one or more of the following: basal heart rate, food factor, base factor and activity factor or an intensity-adjusted activity factor;

[0418] c. subtracting, by the processing unit, basal heart rate from the heart rate data; d. optionally estimating, by the processing unit, an physical activity related elevation of the resting heart rate;

[0419] e. identifying, by the processing unit, one or more points of minimum heart rate in the heart rate data;

[0420] f. optionally using, by the processing unit, the physical activity related elevation of the resting heart rate to adjust the heart rate values of the one or more points of minimum heart rate;

[0421] g. fitting, by the processing unit, one or more food waves to the one or more points of minimum heart rate;

[0422] h. connecting, by the processing unit, the one or more food waves for estimating a heart rate response due to energy intake;

[0423] i. quantifying, by the processing unit, food heartbeats using heart rate response due to energy intake;

[0424] j. quantifying, by the processing unit, base heartbeats using basal heart rate; k. subtracting, by the processing unit, the heart rate response due to energy intake from the heart rate data to obtain activity heart rate;

[0425] l. quantifying, by the processing unit, activity heartbeats, or partial activity heartbeats, using the activity heart rate;

[0426] m. determining, by the processing unit, one or more of the following energy parameters: energy intake using the food heartbeats and the food factor, energy expenditure due to energy intake using the food heartbeats and the food factor, energy expenditure due to activity using the activity heartbeats and activity factor or the partial activity heartbeats and the intensity adjusted activity factor, energy expenditure due to basal metabolism using the base heartbeats and base factor, and total energy expenditure; and,

[0427] n. outputting, to the individual, one or more of the energy parameters.P16006PC00 41

[0428] 2. The method according to clause 1, wherein the partial activity heartbeats are determined for consecutive segments of a time period spanned by the heart rate data.

[0429] 3. The method according to clause 2, wherein the energy expenditure due to activity is determined using the activity heartbeats determined for consecutive segments and the intensity adjusted activity factor.

[0430] 4. The method according to any of clauses 1 to 3, further comprising the step of measuring, by a wearable electronic device, heart rate data of the individual as a function of time and optionally activity data or activity heart rate of the individual as a function of time.

[0431] 5. The method according to clauses 1 or 4, further comprising the step of measuring and / or computing the one or more metabolic parameters for the individual.

Claims

P16006PC00 42CLAIMS1. A computer-implemented method for determining an energy intake of an individual, the method comprising:a. providing, to a processing unit, data comprising heart rate of an individual as a function of time;b. providing to, or computing by, the processing unit at least one metabolic parameter of the individual, wherein the at least one metabolic parameter comprises a food factor (FF), the food factor describing how many additional heart beats are associated with each unit of energy consumed by the individual;c. quantifying, by the processing unit, food heartbeats (FB) from the heart rate data, wherein the quantification is determined by fitting one or more food waves, each food wave fitted to one or more points in the heart rate data, to obtain a heart rate response attributable to energy intake, and wherein each food wave is a mathematical function of time, and integrating over the heart rate response due to energy intake to obtain the accumulated food heartbeats;d. determining, by the processing unit, the energy intake (El) of the individual using the food heartbeats (FB) and the food factor (FF) by the following expression El = FB / FF; and,e. outputting the energy intake to the individual.

2. The method according to claim 1, wherein the one or more food waves are each expressed by a right-skewed unimodal function.

3. The method according to either claim 1 or claim 2, wherein the one or more food waves are expressed by an exponential-type function, gamma distribution type function, or convolution-based function.

4. The method according to the preceding claim, wherein the one or more food waves are expressed by the following mathematical expression: F(t) = a ■ t■ e(~), or gamma distribution with alpha > 1, or a Weibull distribution with k > 1.

5. The method according to any of the preceding claims, wherein the method further comprises the step of measuring the food factor and optionally measuring and / or determining other metabolic parameter such as a base factor (BF), and an activity factor (AF) or an intensity adjusted activity factor (AF).

6. The method according to the preceding claim, wherein the step of measuring and / or determining the at least one metabolic parameter uses the individual's heart rate response to exercise.

7. The method according to any of the preceding claims, wherein the food factor (FF) is computed using the heart rate (P2) of an individual at a maximum metabolic steady stateP16006PC00 43(lactate threshold) and the velocity (T2) of an individual at the maximum metabolic steady state, or wherein the food factor is computed using the maximal oxygen uptake (VO2max) and / or estimated aerobic muscle mass.

8. The method according to any of the preceding claims, wherein the method further comprises steps of:a. dividing the heart rate data into consecutive first time segments; and, b. identifying one or more points of minimum heart rate in each consecutive first time segment and wherein the one or more food waves are fitted to the one or more points of minimum heart rate.

9. The method according to any of the preceding claims, wherein the method further comprises a step of adjusting the values of the one or more points of heart rate data, and wherein the values of the heart rate data are adjusted according to a basal heart rate of the individual and / or adjusted according to an physical activity-related elevation of resting heart rate and / or adjusted according to an activity heart rate.

10. The method according to the preceding claim, wherein the intensity-related elevation of resting heart rate is estimated by dividing the heart rate data into consecutive second time segments, estimating for each consecutive second time segment the mean intensity of activity using the intensity of the activity for each time segment to estimate excess postexercise oxygen consumption (EPOC) and / or post exercise heart rate response, and predicting the activity related elevation of post exercise resting heart rate.

11. The method according to any of the preceding claims, wherein the one or more fitted food waves are combined to obtain heart rate response due to energy intake.

12. The method according to any of the preceding claims, wherein the method further comprises steps of:c. determining, by the processing unit, one or more energy expenditure parameters of the individual such asi. energy expenditure due to energy intake using the food heartbeats and the food factor,ii. energy expenditure due to basal metabolism using base heartbeats and the base factor, andiii. energy expenditure due to activity using activity heartbeats and an activity factor or a physical activity adjusted activity factor; and, d. outputting the one or more energy expenditure parameters to the individual.

13. The method according to any of the preceding claims, wherein the method further comprises a step of quantifying base heartbeats (BB) using a basal heart rate of the individual.P16006PC00 4414. The method according to either claim 12 or 13, wherein the method further comprises a step of quantifying activity heartbeats (AB) using an activity heart rate of the individual.

15. The method according to the preceding claim, wherein the activity heart rate is obtained by subtracting the basal heart rate and the heart rate response due to energy intake from the heart rate data and integrating over the subtracted heart rate data, or by subtracting the base heartbeats and food heartbeats from the total heartbeats of the individual, preferably over a predetermined interval of time.

16. The method according to claim 14, wherein the activity heart rate of the individual is provided from a wearable device configured to measure activity data or activity heart rate.

17. The method according to any of claims 12 to 16, wherein the energy expenditure due to activity is determined by:e. dividing the activity heart rate into time segments;f. estimating the partial activity heartbeats for each segment;g. dividing the partial activity heartbeats for each segment with a corresponding value of a physical activity adjusted activity factor to obtain partial energy expenditure for each segment; and,h. summing up the partial energy expenditure for each segment over a range spanned by the activity heart rate.

18. The method according to any of the preceding claims, wherein the method further comprises a step of measuring the heart rate.

19. The method according to any of the preceding claims, wherein the heart rate data is measured by, and provided from, a wearable measurement device configured to measure the heart rate.

20. A system for determining energy intake of an individual, the system comprising:a. at least one data storage, configured to store one or more metabolic parameters of the individual;b. at least one processing unit configured to:I. receive heart rate data;ii. determine an energy intake of the individual from the heart rate data and one or more metabolic parameters using the computer-implemented method according to any one of claims 1-19; and,c. an output means configured to output the energy intake of the individual.

21. The system according to the preceding claim, wherein the at least one processing unit is further configured to determine one or more energy expenditure parameters and the outputP16006PC00 45means is configured to output the one or more energy expenditure parameters to an individual.

22. The system according to either claim 20 or 21, wherein the system further comprises a wearable electronic device configured to measure heart rate of the individual as a function of time and optionally configured to measure activity data or activity heart rate of the individual as a function of time.

23. The system according to the preceding claim, wherein the wearable electronic device is configured to be worn on forearm, or on wrist, or on torso, or on finger of the individual.

24. The system according to any of claims 20 to 23, wherein the system further comprises a communication interface configured to receive heart rate data and optionally physical activity data from one or more external wearable electronic devices configured to measure such data.

Images