Method for operating an appliance using hand gestures
A method of using a remote server to operate a first appliance of a plurality of appliances connected to an external network includes receiving one or more images from a second appliance of the plurality of appliances, analyzing the one or more images to detect a hand gesture, identifying a responsive action associated with the hand gesture, and instructing the first appliance to implement the responsive action.
1. A method of using a remote server to operate a first appliance of a plurality of appliances, the method comprising:
receiving one or more images from a second appliance of the plurality of appliances;
analyzing the one or more images to detect a hand gesture, wherein the hand gesture is an ok symbol;
identifying a responsive action associated with the hand gesture, wherein the responsive action is leaving a clean indicator on; and
instructing the first appliance to implement the responsive action.
2. The method of claim 1 , wherein each of the plurality of appliances are connected to the remote server through an external network.
3. The method of claim 1 , wherein the one or more images are provided from at least two appliances of the plurality of appliances.
4. The method of claim 1 , wherein the first appliance is a dishwasher appliance.
5. The method of claim 1 , further comprising:
detecting user interaction with the first appliance; and
obtaining the one or more images within a predetermined time after detecting the user interaction.
6. The method of claim 5 , wherein the predetermined time is less than one minute.
7. The method of claim 5 , wherein the first appliance comprises a door and detecting the user interaction with the first appliance comprises determining that the door has been opened or closed.
8. The method of claim 1 , wherein analyzing the one or more images to detect the hand gesture comprises using a machine learning image recognition process comprising at least one of a convolution neural network (“CNN”), a region-based convolution neural network (“R-CNN”), a deep belief network (“DBN”), or a deep neural network (“DNN”) image recognition process.
9. The method of claim 1 , wherein the responsive action comprises:
adjusting a cycle type, a cycle setting, or other operating parameters.
10. The method of claim 1 , wherein the hand gesture is a thumbs up and the responsive action is commencing an operating cycle.
11. The method of claim 1 , wherein the hand gesture is an ok symbol and the responsive action is leaving a clean indicator on.
12. A method for operating an appliance, the method comprising:
obtaining one or more images using a camera assembly;
analyzing the one or more images to detect a hand gesture, wherein the hand gesture is an ok symbol;
identifying a responsive action associated with the hand gesture, wherein the responsive action is leaving a clean indicator on; and
implementing the responsive action using the appliance.
13. The method of claim 12 , wherein the camera assembly is mounted on an outside surface of a cabinet of the appliance for obtaining the one of more images.
14. The method of claim 12 , wherein the camera assembly comprises a fixed focus range of less than 2 feet.
15. The method of claim 14 , wherein the fixed focus range is between about 4 inches and 8 inches.
16. The method of claim 12 , wherein the appliance is a first appliance of a plurality of appliances connected to a local network, and wherein the camera assembly is mounted on a second appliance of the plurality of appliances for obtaining the one of more images.
17. The method of claim 16 , wherein the one or more images are provided from at least two appliances of the plurality of appliances.
18. The method of claim 12 , wherein the appliance is a dishwasher appliance.
19. The method of claim 12 , further comprising:
detecting user interaction with the appliance; and
obtaining the one or more images within a predetermined time after detecting the user interaction.
20. The method of claim 12 , wherein analyzing the one or more images to detect the hand gesture comprises using a machine learning image recognition process comprising at least one of a convolution neural network (“CNN”), a region-based convolution neural network (“R-CNN”), a deep belief network (“DBN”), or a deep neural network (“DNN”) image recognition process.