Method for measuring weight values ​​in sleep state by eccentricity confidence

Abstract

The invention discloses a method for measuring body weight in a sleep state through eccentricity confidence. A plurality of pressure sensors are arranged under the bed, and the duration of a measurement period is determined; a long time window is defined, and a steady state is judged through the long time window ; When the steady state of the bed state ends, determine the steady state body weight of the user during the steady state, and calculate the eccentricity confidence degree as the steady state confidence degree or the reference value of the steady state confidence degree; after the end of the measurement period, according to The steady-state confidence selects one or more steady-state body weights to calculate the user's weight during the measurement period. In the present invention, by dividing the long-time window and judging whether the long-time window is a steady state method, the steady-state duration and the steady-state duration confidence can be calculated, and the steady-state body weight can be obtained; each steady-state weight can be evaluated by the eccentricity confidence The reliability of the weight can further obtain the weight value that can represent the actual weight of the user, and provide accurate weight data for basic health monitoring.

Description

technical field [0001] The invention relates to the field of basic health monitoring, in particular to a method for measuring body weight in a sleeping state through eccentricity confidence. Background technique [0002] The accuracy of traditional electronic weighing scales is generally 0.1kg. Under this accuracy, the reading is relatively easy to stabilize. Once stable, the electronic weighing scale can output it to the display screen, and it seems that only one value is read. In fact, with any electronic weighing device, the readings obtained are a sequence, not a single value. Since basic health monitoring needs to obtain high-precision weight data of the user in order to analyze and monitor the health status, a high-precision pressure sensor below 10g is generally used, and the measurement of the pressure sensor is a dynamic process. As long as a certain accuracy is achieved, a slight disturbance will Different readings are generated. Assuming that a measurement is per...

AI Technical Summary

Problems solved by technology

Since basic health monitoring needs to obtain high-precision weight data of the user in order to analyze and monitor the health status, a high-precision pressure sensor below 10g is generally used, and the measurement of the pressure sensor is a dynamic process. As long as a certain accuracy is achieved, a slight disturbance will Different readings are generated. Assuming that a measurement is performed every second, different readings will be generated every second, so that the weight data is difficult to stabilize. For example, the user stays in bed for 8 hours, that is, 8×60×60=28800 seconds Will produce 28,800 weighing readings; making it difficult to judge which weighing data more accurately reflects the user's actual weight

[0003] In addition, the actions of a person on the bed, such as: going to bed, getting out of bed, turning over, etc., will have an impact on the readings of the pressure sensor, resulting in large instantaneous fluctuations; if these effects are not eliminated, the measured weight value will produce large distortion

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