System and method for tracking a human hand in an augmented reality environment
A system configured for tracking a human hand in an augmented reality environment may comprise a distancing device, one or more physical processors, and/or other components. The distancing device may be configured to generate output signals conveying position information. The position information may include positions of surfaces of real-world objects, including surfaces of a human hand. Feature positions of one or more hand features of the hand may be determined through iterative operations of determining estimated feature positions of individual hand features from estimated feature position of other ones of the hand features.
1. A system configured to track a human hand in an interactive environment, the system comprising:
one or more physical processors configured by machine-readable instructions to:
obtain position information conveying positions of surfaces of a human hand, the hand having a first hand feature and other hand features;
determine, based on the position information, an estimated feature position of the first hand feature; and
perform an iteration of:
determining estimated feature positions of the other hand features based on the estimated feature position of the first hand feature;
determining an other estimated feature position of the first hand feature based on the estimated feature positions of the other hand features; and
determining a difference between the estimated feature position of the first hand feature and the other estimated feature position of the first hand feature; and
wherein:
based on the difference being below a threshold distance, specify:
a first feature position of the first hand feature as the other estimated feature position of the first hand feature;
feature positions of the other hand feature as the estimated feature positions of the other hand features; and.
based on the difference being above the threshold distance, perform another iteration using the other estimated feature position of the first hand feature as the estimated feature position of the first hand feature.
2. The system of claim 1 , wherein:
the first hand feature comprises a wrist; and
the other hand features include a metacarpophalangeal joint of a thumb and a set of metacarpophalangeal joints of a set of fingers.
3. The system of claim 1 , wherein the one or more physical processors are further configured by machine-readable instructions to:
determine, from the position information, clusters of surfaces;
determine cluster groups, the individual cluster groups including one or more clusters of surfaces that make up individual contiguous objects;
associate the individual clusters in the individual cluster groups with individual hand features;
determine, for the individual clusters in the individual cluster groups, individual confidence levels indicating likelihood that the individual clusters represent the associated individual hand features;
determine, for the individual cluster groups, individual aggregate confidence levels indicating likelihood that the individual cluster groups represent the hand, the individual aggregate confidence levels being an aggregate of the individual confidence levels of the individual clusters in the individual cluster groups; and
identify, based on the individual aggregate confidence levels, an individual cluster group as representing the hand.
4. The system of claim 3 , wherein identifying, based on the individual aggregate confidence levels, the individual cluster group as representing the hand comprises:
comparing the individual aggregate confidence levels to a threshold confidence level; and
identifying the individual cluster group as representing the hand based on the individual aggregated confidence level of the individual cluster group being above the threshold confidence level.
5. The system of claim 3 , wherein an individual hand feature includes one or more of a wrist, a thumb, a metacarpophalangeal joint of a thumb, a palm, a finger, a metacarpophalangeal joint of a finger, a fingertip, a set of fingers, a set of metacarpophalangeal ,joints of a set of fingers, or a set of fingertips.
6. The system of claim i, wherein the position information comprises point cloud information, the point cloud information including positions of points that lie on the surfaces of the real-world objects, such that the positions of the points are the positions of the surfaces.
7. The system of claim 1 , further comprising:
a light source configured to emit light; and
an optical element, the optical element being configured to provide the light emitted from the light source to an eye of the user.
8. The system of claim 7 , wherein the one or more physical processors are further configured by machine-readable instructions to:
generate views of virtual content to be perceived within the field-of-view of the user; and
facilitate user interaction with the virtual content based on the first feature position of the first hand feature and the feature positions of the other hand features.
9. The system of claim 8 , wherein the virtual content includes one or more virtual objects.
10. The system of claim 1 , wherein position information is obtained based on a sampling rate of a distancing device.
11. A method to track a human hand in an interactive environment, the method being implemented in a computer system comprising one or more physical processors and non-transitory storage media storing machine-readable instructions, the method comprising:
obtaining position information conveying positions of surfaces of a human hand, the hand having a first hand feature and other hand features;
determining, based on the position information, an estimated feature position of the first hand feature; and
performing an iteration of:
determining estimated feature positions of the other hand features based on the estimated feature position of the first hand feature;
determining an other estimated feature position of the first hand feature based on the estimated feature positions of the other hand features; and
determining a difference between the estimated feature position of the first hand feature and the other estimated feature position of the first hand feature; and wherein:
based on the difference being below a threshold distance, specify:
a first feature position of the first hand feature as the other estimated feature position of the first hand feature;
feature positions of the other hand feature as the estimated feature positions of the other hand features; and
based on the difference being above the threshold distance, perform another iteration using the other estimated feature position of the first hand feature as the estimated feature position of the first hand feature.
12. The method of claim 11 , wherein:
the first hand feature comprises a wrist; and
the other hand features include a metacarpophalangeal joint of a thumb and a set of metacarpophalangeal joints of a set of fingers.
13. The method of claim 11 , further comprising:
determining, from the position information, clusters of surfaces;
determining cluster groups, the individual cluster groups including one or more clusters of surfaces that make up individual contiguous objects;
associating the individual clusters in the individual cluster groups with individual hand features;
determining, for the individual clusters in the individual cluster groups, individual confidence levels indicating likelihood that the individual clusters represent the associated individual hand features;
determining, for the individual cluster groups, individual aggregate confidence levels indicating likelihood that the individual cluster groups represent the hand, the individual aggregate confidence levels being an aggregate of the individual confidence levels of the individual clusters in the individual. cluster groups; and
identifying, based on the individual aggregate confidence levels, an individual cluster group as representing the hand.
14. The method of claim 13 , wherein identifying, based on the individual aggregate confidence levels, the individual cluster group as representing the hand comprises:
comparing the individual aggregate confidence levels to a threshold confidence level; and
identifying the individual cluster group as representing the hand based on the individual aggregated confidence level of the individual cluster group being above the threshold confidence level.
15. The method of claim 13 , wherein an individual hand feature includes one or more of a wrist, a thumb, a metacarpophalangeal joint of a thumb, a palm, a finger, a metacarpophalangeal joint of a finger, a fingertip, a set of fingers, a set of metacarpophalangeal joints of a set of fingers, or a set of fingertips.
16. The method of claim 11 , wherein the position information comprises point cloud information, the point cloud information including positions of points that lie on the surfaces of the real-world objects, such that the positions of the points are the positions of the surfaces.
17. The method of claim 11 , further being implemented using a light source configured to emit light, and an optical element, the optical element being configured to provide light emitted from the light source to an eye of the user.
18. The method of claim 17 , further comprising:
generating views of virtual content to be perceived within the field-of-view of the user; and
facilitating user interaction with the virtual content based on the first feature position of the first hand feature and the feature positions of the other hand features.
19. The method of claim 18 , Wherein the virtual. content includes one or more virtual objects.
20. The method of claim 11 , wherein the position information is obtained based on a sampling rate of a distancing device.