Methods and devices for identifying users based on tremor
First Claim
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1. A method for identifying a user of a handheld device comprising the steps of:
- detecting, using a motion sensor and a processor, a hand tremor associated with a user holding said handheld device; and
identifying said user based on said detected hand tremor,wherein said step of identifying further comprises;
(a) obtaining tremor data sets associated with a plurality of users;
(b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users,(c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users,(d) identifying clusters associated with said reduced feature sets, and(e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters,wherein said step (b) of extracting features further comprises;
calculating a plurality of point Fast Fourier Transforms (FFTs) averaged with an overlap for a number of points in said movement data within a predetermined frequency range.
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Abstract
Systems and methods according to the present invention address these needs and others by providing a handheld device, e.g., a 3D pointing device, which uses hand tremor as an input. One or more sensors within the handheld device detect a user'"'"'s hand tremor and identify the user based on the detected tremor.
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Citations
62 Claims
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1. A method for identifying a user of a handheld device comprising the steps of:
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detecting, using a motion sensor and a processor, a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said step of identifying further comprises; (a) obtaining tremor data sets associated with a plurality of users; (b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users, (c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users, (d) identifying clusters associated with said reduced feature sets, and (e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters, wherein said step (b) of extracting features further comprises; calculating a plurality of point Fast Fourier Transforms (FFTs) averaged with an overlap for a number of points in said movement data within a predetermined frequency range. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)
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11. A non-transitory computer-readable medium, capable of storing program instructions which, when executed perform the steps of:
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detecting a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said step of identifying further comprises; (a) obtaining tremor data sets associated with a plurality of users; (b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users, (c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users, (d) identifying clusters associated with said reduced feature sets, and (e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters, wherein said step (b) of extracting features further comprises; calculating a plurality of point Fast Fourier Transforms (FFTs) averaged with an overlap for a number of points in said movement data within a predetermined frequency range. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56)
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21. A system including a handheld device, the system comprising:
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at least one motion sensor capable of generating data associated with movement of the handheld device; and a processor for generating hand tremor data based on said movement data and for identifying a user of said handheld device based on said hand tremor data by determining within which of a plurality of clusters at least one feature vector associated with said hand tremor data lies. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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57. A method for identifying a user of a handheld device comprising the steps of:
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detecting, using a motion sensor and a processor, a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said step of identifying further comprises; (a) obtaining tremor data sets associated with a plurality of users; (b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users, (c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users, (d) identifying clusters associated with said reduced feature sets, and (e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters, wherein said step (c) of removing features further comprises; applying a Principal Component Analysis (PCA) algorithm to determine a set of basis vectors.
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58. A method for identifying a user of a handheld device comprising the steps of:
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detecting, using a motion sensor and a processor, a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said step of identifying further comprises; (a) obtaining tremor data sets associated with a plurality of users; (b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users, (c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users, (d) identifying clusters associated with said reduced feature sets, and (e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters, wherein said step (d) of identifying clusters further comprises; applying a discriminant to accentuate at least one discriminating feature associated with each cluster, wherein said discriminant is an Enhanced Fisher Linear Discriminant (EFM-1).
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59. A non-transitory computer-readable medium, capable of storing program instructions which, when executed perform the steps of:
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detecting a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said step of identifying further comprises; (a) obtaining tremor data sets associated with a plurality of users; (b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users, (c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users, (d) identifying clusters associated with said reduced feature sets, and (e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters, wherein said step (c) of removing features further comprises; applying a Principal Component Analysis (PCA) algorithm to determine a set of basis vectors.
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60. A non-transitory computer-readable medium, capable of storing program instructions which, when executed perform the steps of:
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detecting a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said step of identifying further comprises; (a) obtaining tremor data sets associated with a plurality of users; (b) extracting features from each of said tremor data sets to generate an extracted feature set for each of said plurality of users, (c) removing features from each of said extracted feature sets to generate a reduced feature set for each of said plurality of users, (d) identifying clusters associated with said reduced feature sets, and (e) identifying said user by generating new feature vectors based on said hand tremor data and determining whether said new features lie within said clusters, wherein said step (d) of identifying clusters further comprises; applying a discriminant to accentuate at least one discriminating feature associated with each cluster, wherein said discriminant is an Enhanced Fisher Linear Discriminant (EFM-1).
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61. A method for identifying a user of a handheld device comprising the steps of:
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detecting, using a motion sensor and a processor, a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said motion sensor is capable of generating data associated with movement of the handheld device, and said processor generates hand tremor data based on said movement data for identifying said user of said handheld device based on said hand tremor data by determining within which a plurality of clusters at least one feature vector associated with said hand tremor data lies.
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62. A non-transitory computer readable medium, capable of storing program instructions which, when executed perform the steps of:
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detecting a hand tremor associated with a user holding said handheld device; and identifying said user based on said detected hand tremor, wherein said detecting step is performed by at least one motion sensor capable of generating data associated with movement of the handheld device, and wherein said identifying step is performed by at least one processor for generating hand tremor data based on said movement data and for identifying a user of said handheld device based on said hand tremor data by determining within which of a plurality of clusters at least one feature vector associated with said hand tremor data lies.
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Specification