Client-side patient room real-time monitoring and alerting system
A video stream is received from a monitoring device located in a patient room. Images from the video stream are analyzed using a machine learning model to detect a first state associated with the patient room. A second is detected state from subsequent images of the video stream using the machine learning model. The first state and the second state are compared to identify a change indicating an active state. A notification is presented at the user device based on the active state.
1 . A method implemented by a user device, comprising:
receiving a video stream from a monitoring device located in a patient room;
analyzing images from the video stream using a machine learning model to detect a first state associated with the patient room;
detecting a second state from subsequent images of the video stream using the machine learning model;
comparing the first state and the second state to identify a change indicating an active state;
presenting, at the user device, a notification based on the active state; and
displaying individual images of the video stream while no active state is detected.
2 . The method of claim 1 , further comprising:
storing, at the user device, the first state and a corresponding timestamp.
3 . The method of claim 1 , further comprising:
transmitting a request to the monitoring device to modify a quality or a compression rate of the video stream based on the active state.
4 . The method of claim 1 , wherein the machine learning model is a multi-label image classification model configured to detect multiple states simultaneously.
5 . The method of claim 1 , wherein the notification includes a description of the active state.
6 . The method of claim 1 , further comprising:
transmitting another notification directly to a device associated with a healthcare provider.
7 . The method of claim 1 , further comprising:
displaying a real-time view of the patient room based on the video stream when the active state is detected.
8 . The method of claim 1 , the individual images being key frames.
9 . A system for monitoring patient environments, comprising:
a monitoring device configured to:
obtain a video stream of at least a part of a patient room; and
transmit the video stream to a user device; and
the user device configured to:
receive the video stream from the monitoring device;
analyze images from the video stream using a machine learning model to detect a first state associated with the patient room;
detect a second state from subsequent images of the video stream using the machine learning model;
compare the first state and the second state to identify a change indicating an active state;
present a notification based on the active state; and
display individual images of the video stream while no active state is detected.
10 . The system of claim 9 , wherein the user device is further configured to:
transmit a request to the monitoring device to modify a quality or a compression rate of the video stream based on the active state.
11 . The system of claim 9 , wherein the machine learning model is a multi-label image classification model configured to detect multiple states simultaneously.
12 . The system of claim 9 , wherein the notification includes a description of the active state.
13 . The system of claim 9 , wherein the user device is further configured to:
display a real-time view of the patient room based on the video stream when the active state is detected.
14 . The system of claim 9 , the individual images being key frames of the video stream.
15 . A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors of a user device, cause the one or more processors to perform operations comprising:
receiving a video stream from a monitoring device located in a patient room;
analyzing images from the video stream using a machine learning model to detect a first state associated with the patient room;
detecting a second state from subsequent images of the video stream using the machine learning model;
comparing the first state and the second state to identify a change indicating an active state;
presenting, at the user device, a notification based on the active state; and
displaying individual images of the video stream while no active state is detected.
16 . The non-transitory computer-readable medium of claim 15 , the operations further comprising:
storing, at the user device, the first state and a corresponding timestamp.
17 . The non-transitory computer-readable medium of claim 15 , further comprising:
transmitting a request to the monitoring device to modify a quality or a compression rate of the video stream based on the active state.
18 . The non-transitory computer-readable medium of claim 15 , wherein the machine learning model is a multi-label image classification model configured to detect multiple states simultaneously.
19 . The non-transitory computer-readable medium of claim 15 , wherein the notification includes a description of the active state.
20 . The non-transitory computer-readable medium of claim 15 , the operations further comprising:
transmitting another notification directly to a device associated with a healthcare provider.