Apparatus and method for operating a dental appliance
A method for operating a dental appliance by providing a dental appliance including at least one motion sensor that provides motion data, the motion sensor is one of an orientation sensor, an acceleration sensor, and an inertial sensor. Further, providing at least one camera associated with the dental appliance that provides image data, and segmenting the image data received from the camera to segment a surface condition using a first machine learning neural network to generate a surface condition segmentation. Then classifying the motion data received from the motion sensor and the image data received from the camera to classify motion and position of the dental appliance with respect to a surface of a user's anatomy, using a second machine learning neural network classifier to generate a surface position comprising at least one of a relational motion classification or a relational position classification. Finally, generating user treatment progress condition and position based on the surface condition and surface position; and communicating the treatment progress condition and position to the user.
1 . A method for operating a dental appliance, comprising:
providing a dental appliance comprising at least one motion sensor that provides motion data, the motion sensor comprises an orientation sensor, an acceleration sensor, and/or an inertial sensor;
providing an intra-oral camera associated with the dental appliance that provides image data;
segmenting the image data received from the intra-oral camera to segment a surface condition using a first machine learning neural network to generate a surface condition segmentation;
classifying the motion data received from the motion sensor and the image data received from the intra-oral camera to classify motion and position of the dental appliance with respect to a surface of a user's anatomy, wherein the surface of the user's anatomy is the user's oral cavity, and wherein the oral cavity is divided into at least 16 treatment zones, using a second machine learning neural network classifier to generate a surface position comprising at least one of a relational motion classification or a relational position classification; wherein the classifying data step further comprises: receiving, at the second machine learning neural network, the data from the motion sensor; and mapping, with the second machine learning neural network, the data from the motion sensor to one of at least 16 different motion patterns; wherein each one of the at least 16 different motion patterns is associated with a respective one of the at least 16 different treatment zones; wherein the mapping step comprises outputting, from the second machine learning neural network, a probability value that the data from the motion sensor maps to each of the at least 16 different motion patterns such that the probability values for each of the at least 16 different motion patterns indicates a probability that the dental appliance is in one of the 16 different treatment zones that corresponds to each respective motion pattern; and,
generating user treatment progress condition and position based on the surface condition and surface position;
and communicating the treatment progress condition and position to the user; and
automatically modifying operation of the dental appliance based on the surface condition and/or the surface position.
2 . The method of claim 1 , wherein first and second machine learning neural networks are a convolution neural network or a transformer neural network.
3 . The method of claim 1 , wherein the treatment progress condition and position comprises feedback for the amount of operation time of the dental appliance on each treatment zone.
4 . The method of claim 1 , wherein the treatment progress condition and position comprises feedback on where the dental appliance is located within each treatment zone.
5 . The method of claim 1 , wherein the treatment progress condition and position comprises a feedback and recommendation based upon the surface condition only.
6 . The method of claim 1 , wherein:
the dental appliance further comprises a computer network interface transmitting and receiving data over a computer network; and
an additional camera located on a computerized device that comprises a computer network interface at least transmitting image data over the computer network.
7 . The method of claim 1 , wherein the step of communicating the treatment progress condition and position comprises a gamification aspect.
8 . The method of claim 1 , wherein the surface condition is the presence of plaque on a user's teeth.
9 . The method of claim 1 , wherein the step of modifying operation of the dental appliance comprises adjusting a pressure sensitivity of the dental appliance.
10 . A method for operating a dental appliance, comprising:
providing a dental appliance comprising at least one motion sensor that provides motion data, the motion sensor comprises an orientation sensor, an acceleration sensor, and/or an inertial sensor;
providing a camera associated with the dental appliance that provides image data;
segmenting the image data received from the camera to segment a surface condition using a first machine learning neural network to generate a surface condition segmentation;
classifying the motion data received from the motion sensor and the image data received from the camera to classify motion and position of the dental appliance with respect to a surface of a user's anatomy wherein the surface of the user's anatomy is the user's oral cavity, and wherein the oral cavity is divided into at least 16 treatment zones, using a second machine learning neural network classifier to generate a surface position comprising at least one of a relational motion classification or a relational position classification; wherein the classifying data step further comprises: receiving, at the second machine learning neural network, the data from the motion sensor; and mapping, with the second machine learning neural network, the data from the motion sensor to one of at least 16 different motion patterns; wherein each one of the at least 16 different motion patterns is associated with a respective one of the at least 16 different treatment zones; wherein the mapping step comprises outputting, from the second machine learning neural network, a probability value that the data from the motion sensor maps to each of the at least 16 different motion patterns such that the probability values for each of the at least 16 different motion patterns indicates a probability that the dental appliance is in one of the 16 different treatment zones that corresponds to each respective motion pattern; and,
generating user treatment progress condition and position based on the surface condition and surface position;
communicating the treatment progress condition and position to the user; and
automatically modifying a brush speed setting of the dental appliance based on the surface position.
11 . The method of claim 10 , wherein the step of communicating the treatment progress condition and position to the user comprises a visual element, a haptical element, and/or a graphical user interface.
12 . The method of claim 10 , wherein the first machine learning neural network and the second machine learning network are a convolutional neural network and/or a recurrent neural network.
13 . The method of claim 10 , wherein the camera is an intra-oral camera.