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US 11,538,581 B2Granted
Monitoring system, device and computer-implemented method for monitoring pressure ulcers
AssigneesTaipei Medical University, Trihealth Enterprise Inc., Limited, Vast Technologies Inc.
InventorsYu-Chuan Jack Li, Jack Hsiao, Wei Chuan Liao
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Abstract
The present disclosure provides monitoring system, device and computed-implemented method for monitoring pressure ulcers. The monitoring system and the device are configured to: capture at least one image corresponding to a user; retrieve the anamnesis data of the user; and determine a pressure ulcers condition result corresponding to the at least one image and the anamnesis data according a pressure ulcers prediction model.
Claims14
A computer-implemented method for monitoring pressure ulcers, comprising:
- capturing, by an image capturing unit of a monitoring system, a plurality of images corresponding to a user;
- determining, by a processor of the monitoring system, a movement frequency according to the plurality of images corresponding to the user;
- retrieving, by the processor, anamnesis data of the user, wherein the anamnesis data is stored in a storing unit of the monitoring system;
- and
- determining, by the processor, a pressure ulcers condition result corresponding to the plurality of images and the anamnesis data according to a pressure ulcers prediction model stored in the storing unit,
- wherein the pressure ulcers prediction model is a machine learning model including a first phase and a second phase, the processor inputs the plurality of images to the first phase of the pressure ulcers prediction model for deriving a movement type from the first phase of the pressure ulcers prediction model, and the processor inputs the movement type, the movement frequency and the anamnesis data to the second phase of the pressure ulcers prediction model for deriving the pressure ulcers condition result from the second phase of the pressure ulcers prediction model.
3 more elements
A monitoring system for monitoring pressure ulcers, comprising:
- an image capturing unit for capturing a plurality of images corresponding to a user;
- a processor;
- and
- a storing unit for storing anamnesis data of the user, a pressure ulcers prediction model and a program that, when being executed, causes the processor to:
- retrieve the plurality of images from the image capturing unit;
- determine a movement frequency according to the plurality of images corresponding to the user;
- retrieve the anamnesis data from the storing unit;
- and
- determine a pressure ulcers condition result corresponding to the plurality of images and the anamnesis data according to the pressure ulcers prediction model, wherein the pressure ulcers prediction model is a machine learning model including a first phase and a second phase, the processor inputs the plurality of images to the first phase of the pressure ulcers prediction model for deriving a movement type from the first phase of the pressure ulcers prediction model, and the processor inputs the movement type, the movement frequency and the anamnesis data to the second phase of the pressure ulcers prediction model for deriving the pressure ulcers condition result from the second phase of the pressure ulcers prediction model.
6 more elements
A device, comprising:
- a network interface for connecting with a network database;
- an image capturing unit for capturing a plurality of images corresponding to a user;
- a processor;
- and
- a storing unit for storing a pressure ulcers prediction model and a program that, when being executed, causes the processor to:
- retrieve the plurality of images from the image capturing unit;
- determine a movement frequency according to the plurality of images corresponding to the user;
- retrieve anamnesis data from the network database via the network interface;
- and
- determine a pressure ulcers condition result corresponding to the plurality of images and the anamnesis data according to the pressure ulcers prediction model, wherein the pressure ulcers prediction model is a machine learning model including a first phase and a second phase, the processor inputs the plurality of images to the first phase of the pressure ulcers prediction model for deriving a movement type from the first phase of the pressure ulcers prediction model, and the processor inputs the movement type, the movement frequency and the anamnesis data to the second phase of the pressure ulcers prediction model for deriving the pressure ulcers condition result from the second phase of the pressure ulcers prediction model.
7 more elements
Dependencies are inferred from the claim text.