Privacy-preserving motion analysis
A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.
1. A method for privacy-preserving motion analysis, the method comprising:
collecting data corresponding to a user with one or more sensors;
identifying one or more joints of the user based on the data;
generating one or more 3D representations of the one or more joints of the user;
anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling;
classifying one or more actions of the user based on analysing the one or more 3D representations, wherein the classifying outputs a predicted action score;
determining whether an identity of the user can be reverse engineered from the one or more 3D representations, wherein the determining outputs a predicted privacy-preservation score; and
based on determining that the identity of the user cannot be reverse engineered from the one or more 3D representations, exporting at least one of the data and the one or more actions.
2. The method of claim 1 , wherein the predicted action score is increased by decreasing the predicted privacy-preservation score, and wherein the predicted privacy-preservation score may be increased by decreasing the predicted action score.
3. The method of claim 1 , wherein the joint-centering disconnects a spatial connectivity but preserves a temporal connectivity between the one or more joints within the one or more 3D representations.
4. The method of claim 1 , wherein the random shuffling shuffles spatial locations of the one or more joints within the one or more 3D representations.
5. The method of claim 1 , wherein classifying the one or more actions of the user is based on applying a model to the data, and wherein the one or more actions are predicted in the future.
6. The method of claim 1 , wherein the random shuffling is irreversible.
7. The method of claim 1 , wherein the one or more actions are selected from a group comprising general motion, posture, gait, a fall, a seizure, and anomalies.
8. A computer program product for privacy-preserving motion analysis, the computer program product comprising:
one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions including a method, the method comprising:
collecting data corresponding to a user with one or more sensors;
identifying one or more joints of the user based on the data;
generating one or more 3D representations of the one or more joints of the user;
anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling;
classifying one or more actions of the user based on analysing the one or more 3D representations, wherein the classifying outputs a predicted action score;
determining whether an identity of the user can be reverse engineered from the one or more 3D representations, wherein the determining outputs a predicted privacy-preservation score; and
based on determining that the identity of the user cannot be reverse engineered from the one or more 3D representations, exporting at least one of the data and the one or more actions.
9. The computer program product of claim 8 , wherein the predicted action score is increased by decreasing the predicted privacy-preservation score, and wherein the predicted privacy-preservation score may be increased by decreasing the predicted action score.
10. The computer program product of claim 8 , wherein the joint-centering disconnects a spatial connectivity but preserves a temporal connectivity between the one or more joints within the one or more 3D representations.
11. The computer program product of claim 8 , wherein the random shuffling shuffles spatial locations of the one or more joints within the one or more 3D representations.
12. The computer program product of claim 8 , wherein classifying the one or more actions of the user is based on applying a model to the data, and wherein the one or more actions are predicted in the future.
13. The computer program product of claim 8 , wherein the random shuffling is irreversible.
14. The computer program product of claim 8 , wherein the one or more actions are selected from a group comprising general motion, posture, gait, a fall, a seizure, and anomalies.
15. A computer system for privacy-preserving motion analysis, the computer system comprising:
one or more computer processors, one or more computer-readable storage media, and program instructions stored on one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions including a method, the method comprising:
collecting data corresponding to a user with one or more sensors;
identifying one or more joints of the user based on the data;
generating one or more 3D representations of the one or more joints of the user;
anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling;
classifying one or more actions of the user based on analysing the one or more 3D representations, wherein the classifying outputs a predicted action score;
determining whether an identity of the user can be reverse engineered from the one or more 3D representations, wherein the determining outputs a predicted privacy-preservation score; and
based on determining that the identity of the user cannot be reverse engineered from the one or more 3D representations, exporting at least one of the data and the one or more actions.
16. The computer system of claim 15 , wherein the predicted action score is increased by decreasing the predicted privacy-preservation score, and wherein the predicted privacy-preservation score may be increased by decreasing the predicted action score.
17. The computer system of claim 15 , wherein the joint-centering disconnects a spatial connectivity but preserves a temporal connectivity between the one or more joints within the one or more 3D representations.
18. The computer system of claim 15 , wherein the random shuffling shuffles spatial locations of the one or more joints within the one or more 3D representations.
19. The computer system of claim 15 , wherein classifying the one or more actions of the user is based on applying a model to the data, and wherein the one or more actions are predicted in the future.
20. The computer system of claim 15 , wherein the random shuffling is irreversible.