Diary-free calorimeter
First Claim
1. A processor-executable method for determining nutritional caloric intake, comprising:
- detecting, from a portable sensor adapted to be worn by a user, a parameter associated with physical exercise over a measurement period;
comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period;
accessing a model for caloric intake stored in memory, the model for caloric intake being trained by regression analysis based upon caloric intake and weight changes monitored for a population of a plurality of individuals; and
estimating, by one or more processors, total caloric intake consumed by the user based upon the detected parameter, the prior weight, the current weight, and the caloric intake model.
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Accused Products
Abstract
An indirect calorimeter estimates nutritional caloric intake by periodically monitoring weight and sensing physical exercise (i.e., physiological data and/or motion data related to physical exertion), which can then be used in a calorimetry model derived from regression analysis of a population (e.g., linear regression, feed-forward neural network, Gaussian process, boosted regression tree, etc.). A strap-on user device for tracking exercise can detect one or more of heart rate, body temperature, skin resistance, motion/acceleration sensing (e.g., pedometer, accelerometer), velocity sensing (e.g., global positioning system (GPS)), and an intelligent, integrated exercise machine (e.g., treadmill, exercise bike, etc.). To gain further fidelity, the user can fine-tune the estimate by undergoing a journal-based routine for a relatively short period of time or clinical calorimetry measurement (e.g., respiratory calorimeter), thereby providing a baseline for resting or exercising metabolic rate.
28 Citations
20 Claims
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1. A processor-executable method for determining nutritional caloric intake, comprising:
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detecting, from a portable sensor adapted to be worn by a user, a parameter associated with physical exercise over a measurement period; comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period; accessing a model for caloric intake stored in memory, the model for caloric intake being trained by regression analysis based upon caloric intake and weight changes monitored for a population of a plurality of individuals; and estimating, by one or more processors, total caloric intake consumed by the user based upon the detected parameter, the prior weight, the current weight, and the caloric intake model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14)
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12. A processor-executable method for determining nutritional caloric intake, comprising:
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detecting, from a first portable sensor adapted to be worn by a user, a physiological parameter associated with physical exercise over a measurement period; detecting, from a second portable sensor adapted to be worn by the user, a motion parameter associated with physical exercise over the measurement period; comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period; accessing a model for caloric intake trained by performing regression analysis on caloric intake, weight changes, physiological sensing data, and exercise monitor data for a control population of a plurality of individuals, the model for caloric intake stored in memory; and estimating, by one or more processors, total caloric intake consumed by the user based upon the detected physiological and motion parameters and the caloric intake model. - View Dependent Claims (15, 16)
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17. A system comprising:
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a processor; and a memory communicatively coupled to the processor that stores computer-readable instructions that, when executed by the processor, perform operations comprising; detecting a parameter associated with physical exercise over a measurement period, the parameters including physiological and motion parameters; comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period; accessing a model for caloric intake stored in memory, the model for caloric intake being trained by regression analysis based upon caloric intake and weight changes monitored for a control population of a plurality of individuals; and estimating total caloric intake consumed by the user based upon the detected physiological and motion parameters, the prior weight, the current weight, and the caloric intake model. - View Dependent Claims (18, 19, 20)
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Specification