DESC:TECHNICAL FIELD OF THE INVENTION
[0001] The present disclosure relates generally to computing systems. More particularly, the present disclosure relates to weight management systems based on daily energy expenditure.
BACKGROUND
[0002] Typically, users are struggling to control their body weight, whether for losing or gaining weight, or simply to maintain the weight they have. For this, users generally use conventional systems for logging daily routine details, food intake, calories intake, body weight logs, exercise, etc. The conventional systems maintain the user logging details by computing total daily energy expenditure using a Harris-Benedict equation to calculate Basal Metabolic Rate (BMR) and then factoring in daily activity and exercise of a user. However, these conventional systems perform theoretical estimation of the total daily energy expenditure and can only be used as an initial estimate while recommending calorie consumption requirements for the user to maintain his current body weight.
[0003] FIG. 1 illustrates a flow chart (100) depicting computing initial values for average total daily energy expenditure. In this, the conventional systems take inputs from the user, such as body weight (102a), height (102b), weight (102c), age (102d), and gender (102e) and compute total daily energy expenditure using the Harris-Benedict equation (104) to calculate the Basal Metabolic Rate (BMR)/ Base value of resting metabolic rate (106). Further, the conventional systems factor in the daily activity, for example, activity levels each day (108a); and exercise of the user, for example, exercise intensity of the user (108b) to calculate resting metabolic rate (110) to compute the initial values for theoretical total daily energy expenditure (112).
[0004] The conventional systems estimate total daily energy expenditure, which is purely theoretical and does not take into account the actual food intake and corresponding body-weight change of the individual. The approach of calculating total daily energy expenditure uses the formula:
‘Total Daily Energy Expenditure = Calorie Intake - Energy corresponding to change in body weight’.
However, it does not take into account daily fluctuations in scale weight due to water retention, etc., which will create a big error in the calculations.
[0005] Further, the Harris-Benedict Equation does not take into consideration the calorie consumption of the user and the corresponding change in body weight. It only provides a theoretical starting point to recommend the calorie requirement of a user. Adjustments on calorie requirement based on how the user’s body is responding to calorie consumption cannot be made with this equation.
[0006] Therefore, there is a need for a system and method which solves the above-defined problems and estimate an average total daily energy expenditure week over week for the user based on their calorie intake and change in body weight.
SUMMARY
[0007] An aspect of the present invention is to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below.
[0008] Accordingly, in one aspect of the present disclosure, a method for managing calorie intake is disclosed. The method performed by a system includes receiving a plurality of inputs from a user device associated with a user. The plurality of inputs includes fitness data and personal data of the user. The fitness data is logged on a regular basis. The method includes storing the plurality of inputs including the fitness data and the personal data of the user in a lookup table in a database associated with the system. Further, the method includes determining calorie intake trend and trend weight for a predefined time based at least on the fitness data and personal data of the user. The method includes determining basal metabolic rate (BMR) of the user. The method further includes computing a total daily energy expenditure (TDEE) based at least on the calorie intake trend, trend weight and the basal metabolic rate. The method includes computing an expenditure value based at least on the total daily energy expenditure (TDEE). The method includes providing dynamic calorie intake recommendations to the user for a specified time frame based on the expenditure value, for fitness maintenance of the user.
[0009] Accordingly, in one aspect of the present disclosure, a system for managing calorie intake is disclosed. The system includes a communication interface, a memory comprising executable instructions, and a processor communicably coupled to the communication interface and the memory. The processor is configured to execute the instructions to cause the system to at least receive a plurality of inputs from a user device associated with a user. The plurality of inputs includes fitness data and personal data of the user. The fitness data is logged on a regular basis. The system is caused to store the plurality of inputs comprising the fitness data and the personal data of the user in a database associated with the system. Further, the system is caused to determine calorie intake trend and trend weight for a predefined time based at least on the fitness data and personal data of the user. The system is caused to determine basal metabolic rate (BMR) of the user. The system is further caused to compute a total daily energy expenditure (TDEE) based at least on the calorie intake trend, trend weight and the basal metabolic rate. The system is caused to compute an expenditure value based at least on the total daily energy expenditure (TDEE). Further, the system is caused to provide dynamic calorie intake recommendations to the user for a specified time frame based on the expenditure value, for fitness maintenance of the user.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
[0010] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[0011] FIG. 1 illustrates a prior art depicting a flow chart for computing initial values for average total daily energy expenditure;
[0012] FIG. 2 illustrates a simplified block diagram representation of an environment, in accordance with an embodiment of the present disclosure;
[0013] FIG. 3 is a simplified block diagram representation of a system for managing calorie intake of a user, in accordance with an embodiment of the present disclosure;
[0014] FIG. 4 illustrates a flow chart depicting dynamically adjusting average total daily energy expenditure based on food intake logs and body weight logs of a user, according to an implementation of the present disclosure; and
[0015] FIG. 5 is a flow diagram depicting a method for managing calorie intake of the user, in accordance with an embodiment of the present disclosure.
[0016] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative methods embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0017] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
[0018] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
[0019] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces. References in the specification to “one embodiment” or “an embodiment” mean that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
[0020] By the term “substantially” it is meant that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic was intended to provide.
[0021] Figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged communications system. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element.
[0022] In the following description, for purpose of explanation, specific details are set forth in order to provide an understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these details. One skilled in the art will recognize that embodiments of the present disclosure, some of which are described below, may be incorporated into a number of systems. However, the systems and methods are not limited to the specific embodiments described herein. Further, structures and devices shown in the figures are illustrative of exemplary embodiments of the presently disclosure and are meant to avoid obscuring of the presently disclosure.
[0023] In an embodiment, the present disclosure discloses a weight management system and method thereof, which compute total daily energy expenditure and dynamically adjust weekly changes in a caloric requirement for men and women to maintain their body weight, based on their daily food logs and body-weight logs. This will help recommend calorie requirements for individuals to lose weight or gain weight following a trend of eating over a stipulated period of time.
[0024] In another embodiment, the present disclosure discloses a weight management system and method thereof, which provides a solution available to accurately recommend personalized numbers of calorie requirements of users chasing body-weight goals, dynamically week over week. The existing solutions used by conventional systems are simply based on the Harris-Benedict equation or less accurate formulas and not on real-time user inputs. In another embodiment, the present disclosure discloses a weight management system and method thereof, which irons out the fluctuations in bodyweight people go through on a regular basis, relying on ‘Trend Weight’, rather than scale weight.
[0025] In another embodiment, the present disclosure discloses a weight management system and method thereof, which irons out dramatic changes in recommendations and errors that will happen due to daily fluctuations in body weight of users due to various reasons.
[0026] Various embodiments of the present disclosure are further described with reference to FIG. 2 and FIG. 5.
[0027] FIG. 2 illustrates a simplified block diagram representation of an environment (200), in accordance with an embodiment of the present disclosure. Although the environment (200) is depicted to include one or a few components, modules, or devices arranged in a particular arrangement in the present disclosure, it should not be taken to limit the scope of the present disclosure. The environment (200) includes a user (202) associated with a user device (204) (exemplary depicted to be “mobile phone”). Further, the environment (200) includes a system (208) and a database (210) associated with the system (208).
[0028] Various entities of the environment (200) are communicably coupled to each other via a network (214). In some embodiments, the network (214) may include wired or wireless communication protocols. In an embodiment, the network (214) may include, but not limited to a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a mobile network capable of supporting communication among two or more entities illustrated in FIG. 2, or any combination thereof.
[0029] The system (208) may include hardware/structural features for performing one or more operations described herein. Typically, the system (208) is configured to provide dynamic calorie intake recommendations to the user (202) for fitness maintenance of the user (202). In particular, the system (208) is communicably coupled to the user device (204). The system (208) may receive a plurality of inputs from the user device (204) of the user (202). In an embodiment, the user device (204) may be equipped with an application (such as, an application (206). The application (206) may be hosted and managed by the system (208). In one scenario, the system (208) may provide an instance of the application (206) to the user device (204) upon receiving a request from the user device (204). In another scenario, the application (206) may be factory installed in the user device (204) or may be a web-based application managed by the application server (208). In another scenario, the user (202) may download the application (206) from online application stores such as, but not limited to, Google Play store, App Store, and the like.
[0030] The system (208) corresponds to a weight management system configured to estimate average total daily energy expenditure (TDEE) for a specified time period (e.g., week over week) for the user (202) based on the plurality of inputs their calorie intake and change in body weight. The system (208) performs calculation of trend weight to help in predicting the body weight of the user (202) in a stipulated period of time if he/she follows a particular trend of calorie intake which will be explained further in detail. The database (214) may be configured to store details related to food logs, user inputs, calorie intake logs, etc. In an embodiment, the database (210) includes a lookup table (212) configured to store details of each user (e.g., the user (202). Further, the system (208) provides accurate recommendations on daily calorie requirement of the user (202) regardless of their body weight or eating habits. The recommendations on daily calorie requirement allows the user (202) to maintain fitness (gain/loss/maintain body weight).
[0031] As explained above, the system (208) provides the recommendations based on accuracy of inputs (e.g., food log and weight logs) of the user (202). If the user (202) logs incorrect information, the recommendations will be inaccurate. Further, the system (208) does not take into consideration people who are on medications that may have a direct impact on metabolism like thyroid medication, other hormonal aids, Testosterone (TRT), and the like.
[0032] In addition, the system (208) filters out the errors in estimation that may occur due to daily fluctuations in body weight due to water-retention for the users due to onset of the menstrual cycle, excess consumption of sodium, excess consumption of carbohydrates etc., constipation or digestion issues, and the like. These quick ups and downs in body weight due to changes in water weight will not be an indicator of real weight change.
[0033] FIG. 3 is a simplified block diagram representation of a system (300) for managing calorie intake of the user (202), in accordance with an embodiment of the present disclosure. The system (300) is an example of the system (208) of FIG. 2. The system (300) includes at least one processor (302), a memory (304), a communication interface (306) and a database (308). The one or more components of the system (300) may communicate via a centralized circuitry or a communication bus.
[0034] The processor (302) includes suitable logic, circuitry, and/or interfaces to execute computer readable instructions for managing calorie intake of the user (202). Examples of the processor (302) include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), and the like.
[0035] The memory (304) may be configured to store pre-determined rules related to computation of values and dynamic adjustment of calorie requirements for users to maintain fitness (i.e., body weight). The memory (304) includes suitable logic, circuitry, and/or interfaces to store a set of computer readable instructions for performing operations. Examples of the memory (304) include a random-access memory (RAM), a read-only memory (ROM), a removable storage drive, a hard disk drive (HDD), and the like. It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to realizing the memory (304) in the system (300), as described herein. In some embodiments, the memory (304) may be realized in the form of a database server or a cloud storage working in conjunction with the system (300), without deviating from the scope of the present disclosure.
[0036] The processor (302) is operatively coupled to the communication interface (306) such that the processor (302) is capable of communicating with a remote device (310) such as, the user device (204), or with any entity connected to the network (214) as shown in FIG. 2.
[0037] In an embodiment, the database (308) is integrated within the system (300). In another embodiment, the database (308) may be embodied as a separate entity (as shown in FIG. 2). The system (300) may include one or more hard drives as the database (308).
[0038] It is noted that the system (300) as illustrated and hereinafter described is merely illustrative of an apparatus that could benefit from embodiments of the present disclosure and, therefore, should not be taken to limit the scope of the present disclosure. It is noted that the system (300) may include fewer or more components than those depicted in FIG. 2.
[0039] In an embodiment, the processor (302) includes a data collection module (312), a determination module (314), a computation module (314), and a recommendation module (318). The one or more components of the processor (302) as described above are communicably coupled with the user device (204) (or the application (206) and manage the calorie intake of the user (202).
[0040] The data collection module (312) includes a suitable logic and/or interfaces for receiving the plurality of inputs from the user device (204) associated with the user (202). The plurality of inputs includes fitness data and personal data of the user. The fitness data is logged on a regular basis (e.g., daily basis). In an embodiment, the user (202) may include wearable device, nutrient tracker or any other device communicably coupled to the user device (204) and is/are capable of tracking the fitness data of the user (202). In one scenario, the application (206) may access the information tracked from other devices mentioned above and communicate to the system (300). The fitness data may include at least a daily weight log, daily food log, calorie intake log, and the like. The personal data may include height, age, gender, and the like. The inputs are stored in the database (308) for further processing. In particular, the system (300) creates a lookup table (e.g., the lookup table (212)) upon receiving the inputs (i.e., fitness data and personal data) and further stores the lookup table in the database (308).
[0041] The determination module (314) includes a suitable logic and/or interfaces determining calorie intake trend and trend weight for a predefined time based at least on the fitness data and personal data of the user (202). Thereafter, the determination module (314) determines basal metabolic rate (BMR) of the user (202). In an example scenario, the inputs may contain data log of weight, calorie, and food for past 30 days. Hence, the inputs are referred to as historical data of the user (202). Further, the system (300) is configured to determine one or more parameters such as the calorie intake trend and the trend weight for the predefined time. The predefined time may be set as past 20 days. As a result, the determination module (314) determines the trend weight and the calorie intake trend based on considering the inputs of past 20 days.
[0042] In an embodiment, the trend weight is determined by computing a weight average of an input (e.g., weight) from the fitness data for the predefined time (20 days). The weight average of past 20 days for determining the trend weight is computed using the following formula:
Trend weight = [Sum of weights of past 20 days]/20. ----- (1)
[0043] The determined trend weight includes weight logged in current time frame. In one embodiment, the determination module (314) determines the calorie intake trend by computing a weighted average of another input (e.g., calorie intake) from the fitness data for the predefined time (20 days). The weighted average of past 20 days for determining the calorie input trend is computed using following formula:
Calorie Input Trend = [Sum of Calories Logged by the user for the past 20 days]/20
----- (2)
[0044] In an embodiment, the calorie input trend will not include at current time frame the calorie intake, (unlike weight-trend, the determination module (314) looks at the past 20 days, excluding current time frame).
[0045] The computation module (316) receives the determined BMR, trend weight, and calorie input trend. Thereafter, the computation module (316) may be configured to compute total daily energy expenditure (TDEE) based on the determined BMR, trend weight, and calorie input trend. In an embodiment, the total daily energy expenditure can also be called Expenditure. In one embodiment, the computation module (316) computes trend weight delta for a specific time frame (for example, 7 days) based on the trend weight by using following formula:
Trend Weight Delta for 7 days = Trend Weight - Trend Weight 7 days ago
----- (3)
[0046] In another embodiment, the computation module (316) computes expenditure (i.e., average calorie input trend) for the specific time frame (weekly or for 7 days) based on the calorie input trend. The expenditure is computed using the following formula:
Expenditure = Calorie Input Trend - [Trend-weight delta for past 20 days X 2.2 X 3500X 1120] ----- (4)
where, 3500 calories correspond to libs of body weight lost.
[0047] In an embodiment, the computation module (316) computes a high accuracy expenditure value by using the adjusted TDEE. In an exemplary embodiment, when the user has consistent food logs and weight logs for 2 weeks, the computation module (316) computes a high accuracy expenditure value for the user by using following formula:
High Accuracy Expenditure Value = Average of [Average Expenditures of 2 weeks where consistent food and weight logs happened] ----- (5)
[0048] The recommendation module (318) receives the computed TDEE and the expenditure value. The recommendation module (318) may be further configured to provide dynamic calorie intake recommendations to the user (202) for the specified time frame (7 days) based on the expenditure value. The user (202) is required to follow the dynamic calorie intake recommendations for fitness maintenance of the user (202). In other words, the recommendation module (318) generates dynamic calorie recommendations based on the computed TDEE for weight loss, weight gain, and weight maintenance. In an embodiment, the recommendation module (318) may be configured to perform dynamic weekly calorie intake recommendations for weight loss, weight gain, and weight maintenance. In one embodiment, the recommendation module (318) provides the dynamic calorie intake recommendations based on one or more artificial intelligence (AI) models. The AI models may be trained with training data (e.g., weight trend, calorie intake trend, calorie recommendations, and the like) and stored in the database (308). In an embodiment, the recommendation module (318) may be configured to perform dynamic weekly calorie intake recommendations for weight loss, weight gain, and weight maintenance by using following formulas:
Recommended Calories = Average Expenditure of past 7 days + [ Trend Weight Delta X 2.2 X 500] When food logs of all 7 days are available. ----- (6)
New Calorie Delta = [Trend Weight Delta X 2.2 X 500] ----- (7)
Recommended Calories [Weekly Adjustment for Weight Gain] = New TDEE + New Calorie Delta ----- (8)
Recommended Calories [Weekly Adjustment for Weight Loss] = New TDEE - New Calorie Delta ----- (9)
[0049] FIG. 4 illustrates a flow chart depicting dynamically adjusting average total daily energy expenditure based on food intake logs body weight logs of the user, according to an implementation of the present disclosure.
[0050] In an embodiment, the data collection module (312) may be configured to receive daily food logs entered by the user, as shown at step (402). At step (404), the determination module (314) determines calorie input trend by calculating weighted moving average of past 20 days. It filters out fluctuations in calorie intake due to fasting, binge eating on certain days, etc. Further, the data collection module (312) may be configured to also receive daily weight log (at step 406) from the user (202). The computation module (316) performs adjustments of values for accuracy by using an algorithm to fill in missed logs on a casually ordered time series, as shown at step (408). At step (410), the determination module (314) determines trend weight by calculating weighted moving average of past 20 days. The determination module (314) filters out fluctuations in body weight due to water retention, constipation, bloating, etc. At step (412), the computation module (316) computes average calorie input trend for a specific time frame based on the calorie input trend. At step (414), the computation module (316) computes trend weight delta for a specific time frame based on the trend weight. At step (416), the computation module (316) computes an adjusted total daily energy expenditure (TDEE) by using following formula:
TDEE=Calorie Input Trend – Energy corresponding to Trend Weight Delta
----- (10)
At step (418), the computation module (316) computes a high accuracy expenditure value by using the adjusted TDEE. In an exemplary embodiment, when the user has consistent food logs and weight logs for 2 weeks, the computation module (316) computes a high accuracy expenditure value for the user (202). At step (420), the recommendation module (318) may be configured to perform dynamic weekly calorie intake recommendations for weight loss, weight gain, and weight maintenance.
[0051] Trend Weight:
In an embodiment, the system (300) may be configured to determine sudden fluctuations in body weight. In other words, the system (300) identifies one or more conditions pertaining to drastic changes (or fluctuations) in the fitness data of the user (202) over the predefined time (20 days). The one or more conditions include at least fluctuations in body weight due to water-retention for the user, onset of the menstrual cycle, fasting, bloating, excess consumption of sodium, excess consumption of carbohydrates, and digestion issues. For instance, the fluctuations may be as high as 2kgs or more in a day with such changes. This is largely water-weight fluctuations and not really tissue weight change. So, if 2kg fluctuation in body weight in a day is identified and hence changes in a week. In this scenario, the system (300) by default will assume that the user is on a 2000 Calorie deficit and user's metabolism (expenditure) is very high. This is a major edge case. This can be solved by putting limits on adjustments, but this is not scalable. The solution is looking at 'Trend Weight'. For example, the user logs weight only on day 1 and day 10. He gained 2kgs in the process. First, the system (300) develops a pattern weight for all the missing data points (missed entry) as shown in below Table 1.

Day Logged Weight Missed data filled Adjusted Weight
1 69.5 Original Delta for 9 days 2.5
2 Missed Logging 69.778 Number of days 9
3 Missed Logging 70.056 Per day Delta 0.278
4 Missed Logging 70.333
5 Missed Logging 70.611
6 Missed Logging 70.889
7 Missed Logging 71.167
8 Missed Logging 71.444
9 Missed Logging 71.722
10 72
Table 1
As shown, the system (300) identifies missed entry (or missing data points) of the input corresponding to weight of the user (202) for a time period (e.g., day 2 to day 9) within the predefined time. Thereafter, the system (300) determines adjusted weight parameter for the time period based at least on the lookup table. As explained above, the lookup table (212) includes the inputs received from the user (202). In this scenario, the system (200) accesses the input (weight) of day 1 and day 10 from the lookup table. The system (300) determines the adjusted weight parameter for the time period (day 9 to day 10). This way, the system (300) ensures that there is data associated with body weight, every single day. In an embodiment, to calculate the trend weight, the system (300) makes the following assumption in the beginning:
The user (202) has been in the body weight he/she entered at the beginning of the program for the past 20 days.
This means the user (202) is also having a standard expenditure of his initial calculated TDEE for the past 20 days. The system (300) uses a Harris-Benedict formula to calculate the initial TDEE. In the above scenario, the trend weight for day 1 is 69.5kg.
For Day 2, the system (200) uses the equation (1) to calculate trend weight, i.e.,
Trend weight = [ Sum of weights of past 20 days]/20
Trend weight = [(69.5X19) + 69.778]/20
That trend weight is the average body weight of the past 14 days, taking into account adjusted body weight.
For Day 3: Trend weight = [(69.778X19) + 70.036]/20
In Table 2, the columns in the logged weight section show the actual logged scale weight. In between columns are adjusted weights. The right-most column is the trend weight.
Day Logged Weight Trend Weight
1 69.5 69.5
2 69.77777778 69.51388889
3 70.05555556 69.54097222
4 70.33333333 69.58059028
5 70.61111111 69.63211632
6 70.88888889 69.69495495
7 71.16666667 69.76854053
8 71.44444444 69.85233573
9 71.72222222 69.94583005
10 72 70.04853855
Table 2
Now, this is a scenario, where the system (300) starts weight and end weight and adjusted the weight for in-between days. Until a new log of body weight is available from the user, the latest trend weight will remain unchanged. Logged weight is of little significance now. In an embodiment, the weight delta may include 'trend weight delta' which is calculated by using the equation (2). The trend weight delta can either be positive or negative.
Trend Weight Delta for 7 days = Latest Trend Weight - Trend Weight 7 days ago
In an embodiment, when the system (200) computes the trend delta, only trend weight on day 1 and trend weight on day 10 have been computed, not the actual weight. For this, the system (200) performs following tasks:
1. Fill the weights on missing days and keep a track in the backend.
2. Calculate the trend weight for every single day.
3. On the front end, plot trend weight along with the actual scale weight being logged.
This solves for any sudden fluctuations in weight, which could be due to water retention, menstrual cycles, or even constipations/no bowel movements.
[0052] Calorie Input Trend:
In an exemplary embodiment, suppose that the initial calculated theoretical TDEE for a user is 2500 Calories. There is an assumption that the user has been in a stable state of being at a stable body weight of 69.5kg (this case) consuming 2500 Calories a day for the past 20 days.
Calorie Input Trend = [ Sum of Calories Logged by the user for the past 20 days1/20
This will not include today (unlike weight-trend, the system (200) looks at the past 20 days, excluding today). The calorie input trend is shown in Table 3.
On Day 2, the Calorie input trend = [(2500X20]/20
On Day 3 the Calorie input trend = [(2500X19)+2200]/20
On Day 4 the Calorie input trend = [(2485X19)+2600]/20

Day Calorie Input Calorie Input Trend
1 2500 2500
2 2200 2500
3 2600 2485
4 1900 2490.75
5 1800 2461.2125
6 1750 2428.151875
7 2800 2394.244281
8 2100 2414.532067
9 2300 2398.805464
10 2200 2393.865191
Table 3
[0053] Expenditure:
The expenditure is calculated by using the equation (4), i.e.,
Calories in - Calories out = Energy corresponding to change in weight. Calories out = Calories in - Energy corresponding to change in weight
Calories out are 'Expenditure'
Expenditure = Calorie Input Trend - [Trend-weight delta for past 20 days X 2.2 X 3500X 1/20] [3500 Calories corresponds to 1 lbs of bodyweight lost]
Here, 3500 calories correspond to 1lbs of body weight lost. Table 4 shows the values of computing expenditure. It is to be noted that, Table 4 corresponds to the lookup table (212), where the inputs from the user (202) and computed values for the user (202) based on the inputs are listed.
Day Logged Weight Trend Weight Trend Weight Delta Calorie Input Calorie Input Trend Expenditure
1 69.50 69.50 0.00 2,500 2,500.00 2,500.00
2 69.78 69.51 0.01 2,200 2,500.00 2,494.65
3 70.06 69.54 0.04 2,600 2,485.00 2,469.23
4 70.33 69.58 0.08 1,900 2,490.75 2,459.72
5 70.61 69.63 0.13 1,800 2,461.21 2,410.35
6 70.89 69.69 0.19 1,750 2,428.15 2,353.09
7 71.17 69.77 0.27 2,800 2,394.24 2,290.86
8 71.44 69.85 0.35 2,100 2,414.53 2,278.88
9 71.72 69.95 0.45 2,300 2,398.81 2,227.16
10 72.00 70.05 0.55 2,200 2,393.87 2,182.68
Table 4
In an embodiment, the system (300) may be configured to compute expenditure on all days and store it in the database (308). In one embodiment, the expenditure is largely dependent on the calorie input trend and not calorie input. The calorie input trend is dependent on the food logs for the past 20 days. So, if the user (202) had missed entry of food logs, the system (300) omits the next day's calorie input trend value from calculating the calorie input trend for future days. The system (300) also does not calculate expenditure for that day.
Referring to below Table 5, where, Day 3, 5, and 9, there is no food log. Here, the expenditure will not be calculated for Day 4, 6 and 10. The Day 4 expenditure is calculated on Day 5, based on calorie input trend of Day 4 and trend weight of Day 4. If the user does not log food on Day 3, the calorie input trend for Day 4 goes for a toss. So, Day 4 expenditure will not be calculated. For Day 4, 6 and 10, the calorie input trend will be ignored for future calculations, and Day 4, Day 6 and Day 10 expenditure values will not be taken for future expenditure average calculations. The user has no access to the calorie input trends.
Day Logged Weight Trend Weight Trend Weight Delta Calorie Input Calorie Input Trend Expenditure
1 69.50 69.50 0.00 2,500 2,500.00 2,500.00
2 69.78 69.51 0.01 2,200 2,500.00 2,494.65
3 70.06 69.54 0.04 0 2,485.00 2,469.23
4 70.33 69.58 0.08 1,900 2,360.75 2,329.72
5 70.61 69.63 0.13 0 2,337.71 2,286.85
6 70.89 69.69 0.19 1,750 2,220.83 2,145.77
7 71.17 69.77 0.27 2,800 2,197.29 2,093.90
8 71.44 69.85 0.35 2,100 2,227.42 2,091.77
9 71.72 69.95 0.45 0 2,221.05 2,049.41
10 72.00 70.05 0.55 2,200 2,110.00 1898.81
Table 5
[0054] High Accuracy Expenditure Value:
In an exemplary embodiment, If the system (200) has 2 weeks of consistent food log and weight-log records, the system (200) computes a High Accuracy Expenditure Value by using the equation (5).
Meaning of consistent food log: Food log available on all 7 days of the week
Meaning of consistent weight log: At least 2 weight logs are available in a week, 2 days apart [ Mon-Thurs, Tues-Friday, Wednesday-Saturday, Thursday-Sunday]
Average Expenditure of Week 1 will be the first Low Accuracy Expenditure Value Average Expenditure of Week 2 will be the second Low Accuracy Expenditure Value
Average of average expenditures of Week 1, Week 2 will be the 'High Accuracy Expenditure Value’. Week 1 and Week 2 need not be back-to-back weeks.
[0055] Weekly Adjustments
In an exemplary embodiment, current weekly adjustments take into account only body-weight changes. This is theoretically a perfect approach, however, the metabolism of each user is different. Even though the TDEE formula takes into account all factors, it is still cannot take into account week-to-week change in a user’s activity levels, hormonal changes, etc., which will impact the energy expenditure of the person. In a scenario of a person logging all 7 days of food and having a starting and an end bodyweight logged for a week, the system (300) will be able to compute an accurate measure of what his calorie intake should be to continue on the goal. This is being taken into account in the Table 6. The current calculations remain as it is. The new calculations will be used only in a scenario of the user having done input for calorie intake all 7 day a week and clicked finish day and the system (300) has at least one weight log toward the end of the week [last 3 days].

Goal Condition Current Calculation in the system (200) modified.

No change. This requires only weight logs.

This continues if there is no availability of 7 days food log for the user. But we have weight logs New Calculation

The system (200) uses the computation, if 1. There is 7 days finish day Data available.

If weight log is not available, the system (200) uses the latest trend weight, regardless.
Weight Gain Look for any High-Accuracy Expenditure values in the past (within this program).

If the system (200) has a High-confidence expenditure value available.

New TDEE = Last High-Accuracy Expenditure Value

Else

Calculate New TDEE based on Trend Weight on the 7th day.
Calculate New Calorie Delta based on Trend Weight delta for past 7 days.

Recommended Calories = New TDEE + New Calorie Delta

N.B.- Trend Weight Delta = (Trend weight on Day 7 - Trend weight on Day 1)

New Calorie Delta = [Trend Weight Delta X 2.2 X 500] TDEE = Average Expenditure for past 7 days.

Recommended Calories
Average Expenditure of past 7 days + [ Trend Weight Delta X 2.2 X 500]

[New Calorie Delta = Trend Weight on day 7 X 2.2 X Rate of change factor X500]

N.B - Trend weight delta can be positive or negative.
Maintenance Trend weight delta for past 7 days>0 Look for any High-Accuracy Expenditure values in the past (within this program).

If the system (200) has a High-Accuracy expenditure value available.

New TDEE = Last High-Accuracy Expenditure Value

Else

Calculate New TDEE based on Trend Weight on the 7th day.

Calorie Delta=Trend weight on day 7 in kgs X Rate of Change factor X 2.2 X 500

Adjusted Daily Calories Intake for next week = New TDEE-Calorie Delta
New TDEE = Average Expenditure in past 7 days (within this program).

Adjusted Calorie Intake =New TDEE-[Trend Weight on Day 7 X Rate of change factor X 2.2 X 500]
Trend weight delta for past 7 days<0 Look for any High-Accuracy Expenditure values in the past.

If the system (200) has a High-Accuracy expenditure value available.

New TDEE = Last High Accuracy Expenditure value

Else

Calculate New TDEE based on Trend Weight on the 7th day.

Calorie Delta = Trend weight on day 7X 0.005 X 2.2 X 500

Adjusted Daily Calories Intake for next week = New TDEE + Calorie Delta New TDEE = Average Expenditure in past 7 days

Adjusted Calorie Intake =New TDEE-[Trend Weight on Day 7 X Rate of change factor X 2.2 X 500]
Input Body-Weight delta=0 Adjusted Daily Calories Intake for next week = Last week’s calorie intake Adjusted Calorie Intake = Average expenditure for the past week
Weight Loss Target Body weight delta> Input body-weight delta Look for any High-Accuracy Expenditure values in the past.

If the system (200) has a High-Accuracy expenditure value available.

New TDEE = Last High Accuracy Expenditure value

else
Calculate New TDEE based on Trend Weight on the 7th day.

Calculate New Calorie Delta based Trend Weight on Day 7.

Calculate New Target Calorie Intake = New TDEE - New Calorie Delta New TDEE = Average Expenditure of past 7 days

Adjusted Calorie Intake = New TDEE - [Trend weight on day 7X Rate of change factor X 2.2 X 500]

[0056] FIG. 5 is a flow diagram depicting a method (500) for managing calorie intake of the user (202), in accordance with an embodiment of the present disclosure. The various steps and/or operations of the flow diagram, and combinations of steps/operations in the flow diagram, may be implemented by, for example, hardware, firmware, a processor, circuitry and/or by an apparatus such as the system (300) or the system (208). The method (500) starts at (502).
[0057] At operation (502), the method (500) includes receiving, by the system (300), a plurality of inputs from the user device (204) associated with the user (202). The plurality of inputs includes fitness data and personal data of the user (202). wherein the fitness data is logged on a regular basis.
[0058] At operation (504), the method (500) includes storing, by the system (300), the plurality of inputs including the fitness data and the personal data of the user in the database (308, 210) associated with the system (300).
[0059] At operation (506), the method (500) includes determining, by the system (300), calorie intake trend and trend weight for the predefined time based at least on the fitness data and personal data of the user (202).
[0060] At operation (508), the method (500) includes determining, by the system (300), basal metabolic rate (BMR) of the user (202).
[0061] At operation (510), the method (500) includes computing, by the system (300), a total daily energy expenditure (TDEE) based at least on the calorie intake trend, trend weight and the basal metabolic rate.
[0062] At operation (512), the method (500) includes computing, by the system (300), an expenditure value based at least on the total daily energy expenditure (TDEE).
[0063] At operation (514), the method (500) includes providing, by the system, dynamic calorie intake recommendations to the user (202) for a specified time frame based on the expenditure value, for fitness maintenance of the user (202). Further, one or more operations performed by the system (300) are explained with references to FIGS. 3 and 4, therefore they are not reiterated for the sake of brevity.
ADVANTAGES
[0064] In an advantageous aspect, the present disclosure provides a system for accurate calculations of average daily energy expenditure of a user based on historical data of the user related to meal logs and body-weight logs.
[0065] In an advantageous aspect of the present disclosure, the system provides accurate recommendations on daily calorie requirement of the subject to gain/loss/maintain body weight.
[0066] In an advantageous aspect of the present disclosure, filtering of drastic changes in recommendations and errors that will happen due to daily fluctuations in body weight of users due to various reasons.
[0067] The various embodiments described above are specific examples of a single broader invention. Any modifications, alterations or the equivalents of the above-mentioned embodiments pertain to the same invention as long as they are not falling beyond the scope of the invention as defined by the appended claims. It will be apparent to a skilled person that the embodiments described above are specific examples of a single broader invention which may have greater scope than any of the singular descriptions taught. There may be many alterations made in the invention without departing from the scope of the invention.
[0068] Figures are merely representational and are not drawn to scale. Certain portions thereof may be exaggerated, while others may be minimized. Figures illustrate various embodiments of the invention that can be understood and appropriately carried out by those of ordinary skill in the art.
[0069] In the foregoing detailed description of embodiments of the invention, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description of embodiments of the invention, with each claim standing on its own as a separate embodiment.
,CLAIMS:
1. A method (500) for managing calorie intake, comprising:
receiving (502), by a system (300), a plurality of inputs from a user device (204) associated with a user (202), the plurality of inputs comprising fitness data and personal data of the user (202), wherein the fitness data is logged on a regular basis;
storing (504), by the system (300), the plurality of inputs comprising the fitness data and the personal data of the user (202) in a database (308) associated with the system (300);
determining (506), by the system (300), calorie intake trend and trend weight for a predefined time based at least on the fitness data and personal data of the user (202);
determining (508), by the system (300), basal metabolic rate (BMR) of the user (202);
computing (510), by the system (300), a total daily energy expenditure (TDEE) based at least on the calorie intake trend, trend weight and the basal metabolic rate;
computing (512), by the system (300), an expenditure value based at least on the total daily energy expenditure (TDEE); and
providing (514), by the system (300), dynamic calorie intake recommendations to the user (202) for a specified time frame based on the expenditure value, for fitness maintenance of the user (202).
2. The method (500) as claimed in claim 1, wherein determining the calorie intake trend and trend weight for the predefined time comprises:
determining, by the system (300), the trend weight by computing a weight average of an input from the fitness data for the predefined time, wherein the input corresponds to weight of the user (202) over the predefined time; and
determining, by the system (300), the calorie intake trend by computing a weighted average of another input from the fitness data for the predefined time, wherein the input corresponds to calorie intake of the user (202) over the predefined time.
3. The method (500) as claimed in claim 2, further comprising:
identifying, by the system (300), missed entry of the input corresponding to weight of the user (202) for a time period within the predefined time; and
upon identifying the missed entry, determining, by the system (300), adjusted weight parameter for the time period based at least on a lookup table (212).
4. The method (500) as claimed in claim 1, further comprising:
identifying, by the system (300), one or more conditions pertaining to drastic changes in the fitness data of the user (202) over the predefined time, the one or more conditions comprising at least fluctuations in body weight due to water-retention for the user (202), onset of the menstrual cycle, fasting, bloating, excess consumption of sodium, excess consumption of carbohydrates, and digestion issues,
wherein the one or more conditions are filtered from the plurality of inputs of the user (202) prior to providing dynamic calorie intake recommendations to the user (202) for a specified time frame, and
wherein the dynamic calorie intake recommendations are provided based, at least in part, on one or more artificial intelligence (AI) models.
5. The method (500) as claimed in claim 1, wherein determining the total daily energy expenditure (TDEE) further comprises:
determining, by the system (300), trend weight delta for a specified time frame based at least on the trend weight; and
determining, by the system (300), the total daily energy expenditure (TDEE) based on the trend weight delta and the calorie input trend.
6. A system (300) for managing calorie intake, comprising:
a communication interface (306);
a memory (304) comprising executable instructions; and
a processor (302) communicably coupled to the communication interface (306) and the memory (304), the processor (302) configured to execute the instructions to cause the system (300) to at least:
receive (502) a plurality of inputs from a user device (204) associated with a user (202), the plurality of inputs comprising fitness data and personal data of the user (202), wherein the fitness data is logged on a regular basis,
store (504) the plurality of inputs comprising the fitness data and the personal data of the user (202) in a database (308) associated with the system (300),
determine (506) calorie intake trend and trend weight for a predefined time based at least on the fitness data and personal data of the user (202),
determine basal metabolic rate (BMR) of the user (202),
compute (510) a total daily energy expenditure (TDEE) based at least on the calorie intake trend, trend weight and the basal metabolic rate,
compute (512) an expenditure value based at least on the total daily energy expenditure (TDEE), and
provide (514) dynamic calorie intake recommendations to the user (202) for a specified time frame based on the expenditure value, for fitness maintenance of the user (202).
7. The system (300) as claimed in claim 6, wherein the system is further caused to at least:
determine the trend weight by computing a weight average of an input from the fitness data for the predefined time, wherein the input corresponds to weight of the user (202) over the predefined time; and
determine the calorie intake trend by computing a weighted average of another input from the fitness data for the predefined time, wherein the input corresponds to calorie intake of the user (202) over the predefined time.

8. The system (300) as claimed in claim 7, wherein the system is further caused to at least:
identify missed entry of the input corresponding to weight of the user (202) for a time period within the predefined time; and
upon identifying the missed entry, determine adjusted weight parameter for the time period based at least on a lookup table (212).
9. The system (300) as claimed in claim 6, wherein the system is further caused to at least:
identify one or more conditions pertaining to drastic changes in the fitness data of the user (202) over the predefined time, the one or more conditions comprising at least fluctuations in body weight due to water-retention for the user (202), onset of the menstrual cycle, fasting, bloating, excess consumption of sodium, excess consumption of carbohydrates, and digestion issues,
wherein the one or more conditions are filtered from the plurality of inputs of the user (202) prior to providing dynamic calorie intake recommendations to the user (202) for a specified time frame, and
wherein the dynamic calorie intake recommendations are provided based, at least in part, on one or more artificial intelligence (AI) models.
10. The system (300) as claimed in claim 6, wherein the system is further caused to at least:
determine trend weight delta for a specified time frame based at least on the trend weight; and
determine the total daily energy expenditure (TDEE) based on the trend weight delta and the calorie input trend.