Increasing capabilities of wearable devices using big data and video feed analysis

Patent metadata

Field	Value
Publication number	US-11967149-B2
Application number	US-202117342962-A
Country	US
Kind code	B2
Filing date	Jun 9, 2021
Priority date	Jun 9, 2021
Publication date	Apr 23, 2024
Grant date	Apr 23, 2024

Abstract

According to one embodiment, a method, computer system, and computer program product for wearable device activity analysis is provided. A computer receives a video of an activity. The computer identifies the activity based on analyzing the video. The computer identifies body movements from the video. The computer correlates the activity and the body movements to a wearable device. The computer identifies additional inputs for the activity and updates the wearable device based on the identified additional inputs.

First claim

What is claimed is:

1. A processor-implemented method for wearable device activity analysis, the method comprising:
receiving a video of an activity;
identifying the activity based on analyzing body movements in the video;
identifying the body movements from the video;
correlating the activity and the body movements to a wearable device;
identifying additional inputs for the activity based on the correlated activity and the body movements; and
updating the wearable device based on the identified additional inputs, wherein the updating includes,
identifying a software program associated with available sensors on the wearable device, and the correlated activity and the body movements, and
installing, on the wearable devices, the software program.

2. The method of claim 1 further comprising:
identifying unused inputs that are absent from the wearable device, wherein the unused inputs correlated to the activity and the body movements from the video; and
recommending adding one or more sensors to the wearable device based on the unused inputs.

3. The method of claim 1 , wherein analyzing the video utilizes a convolutional neural network.

4. The method of claim 1 , wherein identifying the body movements from the video further comprises:
identifying the body movements using image processing; and
clustering the identified body movements using K-means clustering algorithm.

5. The method of claim 1 , wherein correlating the activity and the body movements to a wearable device further comprises:
identifying one or more inputs for the activity using the activity and the body movements; and
determining that sensors of the wearable device have the one or more inputs.

6. The method of claim 5 , wherein identifying additional inputs for the activity further comprises:
based on determining that sensors of the wearable device lack the one or more inputs identifying the additional inputs as inputs that have no corresponding sensors in the wearable device.

7. A computer system for wearable device activity analysis, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
receiving a video of an activity;
identifying the activity based on analyzing body movements in the video;
identifying the body movements from the video;
correlating the activity and the body movements to a wearable device;
identifying additional inputs for the activity based on the correlated activity and the body movements; and
updating the wearable device based on the identified additional inputs, wherein the updating includes,
identifying a software program associated with available sensors on the wearable device, and the correlated activity and the body movements, and
installing, on the wearable devices, the software program.

8. The computer system of claim 7 further comprising:
identifying unused inputs that are absent from the wearable device, wherein the unused inputs correlated to the activity and the body movements from the video; and
recommending adding one or more sensors to the wearable device based on the unused inputs.

9. The computer system of claim 7 , wherein analyzing the video utilizes a convolutional neural network.

10. The computer system of claim 7 , wherein identifying the body movements from the video further comprises:
identifying the body movements using image processing; and
clustering the identified body movements using K-means clustering algorithm.

11. The computer system of claim 7 , wherein correlating the activity and the body movements to a wearable device further comprises:
identifying one or more inputs for the activity using the activity and the body movements; and
determining that sensors of the wearable device have the one or more inputs.

12. The computer system of claim 11 , wherein identifying additional inputs for the activity further comprises:
based on determining that sensors of the wearable device lack the one or more inputs identifying the additional inputs as inputs that have no corresponding sensors in the wearable device.

13. A computer program product for wearable device activity analysis, the computer program product comprising:
one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor, the program instructions comprising:
program instructions to receive a video of an activity;
program instructions to identify the activity based on analyzing body movements in the video;
program instructions to identify the body movements from the video;
program instructions to correlate the activity and the body movements to a wearable device;
program instructions to identify additional inputs for the activity based on the correlated activity and the body movements; and
program instructions to update the wearable device based on the identified additional inputs, wherein the program instructions to update includes,
program instructions to identify a software program associated with available sensors on the wearable device, and the correlated activity and the body movements, and
program instructions to install, on the wearable devices, the software program.

14. The computer program product of claim 13 further comprising:
program instructions to identify unused inputs that are absent from the wearable device, wherein the unused inputs correlated to the activity and the body movements from the video; and
program instructions to recommend adding one or more sensors to the wearable device based on the unused inputs.

15. The computer program product of claim 13 , wherein program instructions to analyze the video utilizes a convolutional neural network.

16. The computer program product of claim 13 , wherein program instructions to identify the body movements from the video further comprises:
program instructions to identify the body movements using image processing; and
program instructions to cluster the identified body movements using K-means clustering algorithm.

17. The computer program product of claim 13 , wherein program instructions to correlate the activity and the body movements to a wearable device further comprises:
program instructions to identify one or more inputs for the activity using the activity and the body movements; and
program instructions to determine that sensors of the wearable device have the one or more inputs.

18. The computer program product of claim 17 , wherein program instructions to identify additional inputs for the activity further comprises:
based on determining that sensors of the wearable device lack the one or more inputs program instructions to identify the additional inputs as inputs that have no corresponding sensors in the wearable device.

Assignees

• IBM

Inventors

• Decrop Clement
• Agrawal Tushar
• Fox Jeremy R
• Rakshit Sarbajit K

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06V20/40 Primary

  in video content (extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44) · CPC title

• G06F1/163

  Wearable computers, e.g. on a belt · CPC title

• G06F8/61

  Installation · CPC title

• G06F16/783

  using metadata automatically derived from the content · CPC title