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公开(公告)号：US12089952B2
公开(公告)日：2024-09-17
申请号：US17387387
申请日：2021-07-28
CPC分类号： A61B5/6802 , A43B3/34 , A43B3/44 , A43B3/48 , A43B17/00 , A61B5/0002 , A61B5/6807 , A61B5/743 , G01L1/146 , A61B2562/0247 , A61B2562/0271 , A61B2562/164 , A61B2562/227
摘要： The enclosed describes a sensor pad for wearing on a human body. The sensor pad is configured to be in contact with a substrate having a contoured surface, such as a surface of the body. The sensor pad comprises at least a sensor layer and a stiffener layer. The sensor layer comprises a surface area defining a sensing area configured to measure value at a plurality of locations of the sensing area. The stiffener layer is couples to the surface area of the sensor layer. The stiffener layer has a micro-cut pattern to reduce mechanical resistance of the stiffener layer. The micro-cut pattern facilitates the stiffener layer in stretching or compressing in one or more predefined directions, enabling the stiffener lay to conform to the contoured surface of the substrate.
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公开(公告)号：US10973344B2
公开(公告)日：2021-04-13
申请号：US15679123
申请日：2017-08-16
IPC分类号： A47C31/12 , G06N3/04 , G06T7/73 , A47C27/08 , A47C21/04 , A47C21/00 , A47G9/10 , A47C31/00 , A47C27/10 , G06N3/08
摘要： A bedding system uses a convolutional neural network (CNN)-based machine vision to makes adjustments for comfort and/or support. The machine vision process identities a body position by using a trained CNN that receives a pressure image and identifies a body position. The body position may be determined by classifying the pressure image into a predetermined body position classification. The machine vision process includes at least one trained CNN that determines joint locations. The machine vision tracks pressure accumulated at joints over time.
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公开(公告)号：US20210196055A1
公开(公告)日：2021-07-01
申请号：US17202652
申请日：2021-03-16
IPC分类号： A47C31/12 , G06N3/04 , G06T7/73 , A47C27/08 , A47C21/04 , A47C21/00 , A47G9/10 , A47C31/00 , A47C27/10 , G06N3/08
摘要： A bedding system uses a convolutional neural network (CNN)-based machine vision to makes adjustments for comfort and/or support. The machine vision process identifies a body position by using a trained CNN that receives a pressure image and identifies a body position. The body position may be determined by classifying the pressure image into a predetermined body position classification. The machine vision process includes at least one trained CNN that determines joint locations. The machine vision tracks pressure accumulated at joints over time.
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公开(公告)号：US20220031241A1
公开(公告)日：2022-02-03
申请号：US17387387
申请日：2021-07-28
摘要： The enclosed describes a sensor pad for wearing on a human body. The sensor pad is configured to be in contact with a substrate having a contoured surface, such as a surface of the body. The sensor pad comprises at least a sensor layer and a stiffener layer. The sensor layer comprises a surface area defining a sensing area configured to measure value at a plurality of locations of the sensing area. The stiffener layer is couples to the surface area of the sensor layer. The stiffener layer has a micro-cut pattern to reduce mechanical resistance of the stiffener layer. The micro-cut pattern facilitates the stiffener layer in stretching or compressing in one or more predefined directions, enabling the stiffener lay to conform to the contoured surface of the substrate.
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公开(公告)号：US20180027988A1
公开(公告)日：2018-02-01
申请号：US15679123
申请日：2017-08-16
IPC分类号： A47C31/12 , G06N3/04 , G06T7/73 , A47C27/10 , A47C21/04 , A47C21/00 , A47G9/10 , A47C31/00 , G06N3/08 , A47C27/08
CPC分类号： A47C31/123 , A47C21/003 , A47C21/044 , A47C21/048 , A47C27/083 , A47C27/10 , A47C31/008 , A47G9/1027 , A47G9/1036 , G06N3/04 , G06N3/08 , G06T7/73 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30196
摘要： A bedding system uses a convolutional neural network (CNN)-based machine vision to makes adjustments for comfort and/or support. The machine vision process identities a body position by using a trained CNN that receives a pressure image and identifies a body position. The body position may be determined by classifying the pressure image into a predetermined body position classification. The machine vision process includes at least one trained CNN that determines joint locations. The machine vision tracks pressure accumulated at joints over time.
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