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案件記録

Protable device and method for non-invasive measurement of the level of physiological values sPortable device and method for non-invasive estimation of the level of

発明審査中
2閲覧数
5請求項 · 1 独立
§ Ⅰ

案件概要

IPC分類

A61B 5/A61B 5/205A61B 5/24A61B 5/531A61B 5/537A61B 5/145A61B 5/1486

CPC分類

A61B5/7267A61B5/17A61B5/2055A61B5/2416A61B5/2438A61B5/531A61B5/537A61B5/14532A61B5/1486A61B5/681A61B5/6897A61B5/7435A61B2562/238

A portable device and method for non-invasive estimation of the level of physiological values such as blood glucose and blood cholesterol, comprising a central processing unit () which includes connections to: a signal emitter which emits signals via electrodes in contact with the skin, which, when processed by a bioimpedance microcontroller and said central processing unit (), provide values such as hydration, body mass index, bone index; a digital optical sensor () which allows calculation of the blood oxygen value, heart rate and temperature; and enzymatic sensors () which determine the quantity of glucose or lactate; said central processing unit () calculating the physiological values on the basis of an automatic learning algorithm that has been trained with a set of clinical history data from a group of patients for whom at least values of bioimpedance, temperature, oxygen and heart rate are available.

原文(中国語)

A portable device and method for non-invasive estimation of the level of physiological values such as blood glucose and blood cholesterol, comprising a central processing unit () which includes connections to: a signal emitter which emits signals via electrodes in contact with the skin, which, when processed by a bioimpedance microcontroller and said central processing unit (), provide values such as hydration, body mass index, bone index; a digital optical sensor () which allows calculation of the blood oxygen value, heart rate and temperature; and enzymatic sensors () which determine the quantity of glucose or lactate; said central processing unit () calculating the physiological values on the basis of an automatic learning algorithm that has been trained with a set of clinical history data from a group of patients for whom at least values of bioimpedance, temperature, oxygen and heart rate are available.

外部リソース