1. A gesture recognition apparatus for recognizing a gesture of a predetermined operating body, comprising:
a storage unit, having stored therein, correspondence relationships between a plurality of coordinate ranges in relation to the operating body and to a plurality of operation target apparatuses, wherein each of the plurality of coordinate ranges corresponds to one operation target apparatus of the plurality of operation target apparatuses;
an image capturing unit configured to capture one or more images of the operating body;
a coordinate detecting unit configured to detect coordinates of the operating body based on the one or more captured images;
an operation target apparatus specifying unit configured to select an operation target apparatus when the detected coordinates of the operating body come within the coordinate range associated with the operation target apparatus in the stored correspondence relationships between the plurality of coordinate ranges and the plurality of operation target apparatuses; and
a gesture recognition processing unit configured to recognize a gesture associated with the operating body based on the one or more captured images and corresponding to the selected operation target apparatus.
2. The gesture recognition apparatus according to claim 1, wherein,
in the storage unit, a correspondence relationship is stored for each of a plurality of operation target functional units of each operation target apparatus;
the operation target apparatus specifying unit configured to specify an operation target functional unit concurrently with the specification of an operation target apparatus; and
the gesture recognition processing unit configured to perform operation processing for the operation target functional unit specified by the operation target apparatus specifying unit.
3. The gesture recognition apparatus according to claim 2, wherein
each of the coordinate ranges is set to be a range in proximity to an operation target functional unit corresponding to the respective coordinate range.
4. The gesture recognition apparatus according to claim 1, wherein
each of the coordinate ranges is set to be a range in proximity to an operation target apparatus corresponding to the respective coordinate range.
5. The gesture recognition apparatus according to claim 4, wherein
the coordinate range is a three-dimensional coordinate range.
6. The gesture recognition apparatus according to claim 5, wherein
the operating body is a hand of a user.
7. The gesture recognition apparatus according to claim 6, the gesture recognition apparatus is mounted in a vehicle.
8. The gesture recognition apparatus according to claim 7, wherein
the operation target apparatuses include an in-vehicle apparatus and an air conditioner.
9. The gesture recognition apparatus according to claim 7, wherein
the operation target functional units include a sound volume control unit and a station selection unit of an in-vehicle apparatus, and a temperature regulation unit and an air volume control unit of an air conditioner.
10. The gesture recognition apparatus according to claim 7, wherein
the operation target apparatuses include a meter panel and a head-up display.
11. The gesture recognition apparatus according to claim 1, further comprising:
a notification device configured to notify a user that the selection by the operation target apparatus specifying unit has been performed.
12. A gesture recognition method for recognizing a gesture of a predetermined operating body, the method carried out by a processor performing steps comprising:
a first step in which correspondence relationships between a plurality of coordinate ranges of the operating body and to a plurality of operation target apparatuses are stored in a storage unit, wherein each of the plurality of coordinate ranges corresponds to one operation target apparatus of the plurality of operation target apparatuses;
a second step in which one or more images of the operating body is captured;
a third step in which coordinates of the operating body are detected based on the one more captured images;
a fourth step in which an operation target apparatus is selected when the detected coordinates of the operating body come within the coordinate range associated with the operation target apparatus in the stored correspondence relationships between the plurality of coordinate ranges and the plurality of operation target apparatuses; and
a fifth step in which a gesture associated with the detected operating body is recognized based on the one or more captured images corresponding to the selected operation target apparatus.
13. The gesture recognition method according to claim 12, wherein
a correspondence relationship is set for each of a plurality of operation target functional units of each operation target apparatus;
an operation target functional unit is specified concurrently with the selection of an operation target apparatus; and
operation processing is performed for the specified operation target functional unit.
14. The gesture recognition method according to claim 13, wherein
each of the coordinate ranges is set to be proximate to a corresponding operation target functional unit.
15. The gesture recognition method according to claim 12, wherein
each of the coordinate ranges is set to be proximate to a corresponding operation target apparatus.
16. The gesture recognition method according to claim 15, wherein
the coordinate ranges are set in a multi-dimensional coordinate space.
17. A non-transitory recording medium in which a gesture recognition program for recognizing a gesture of a predetermined operating body is recorded, the program causing a computer to function as:
a coordinate detecting unit configured to detect coordinates of the operating body based on one or more images captured by an image capturing unit;
an operation target apparatus specifying unit configured to select an operation target apparatus when the detected coordinates of the operating body come within the coordinate range associated with the operation target apparatus in correspondence relationships between a plurality of coordinate ranges of the operating body and a plurality of operation target apparatuses, wherein each of the plurality of coordinate ranges corresponds to one of the plurality of operation target apparatuses, the correspondence relationships being stored in a storage unit; and
an operation processing unit configured to recognize a gesture associated with the operating body based on the one or more captured images and corresponding to the selected operation target apparatus.
18. The recording medium in which a gesture recognition program is recorded according to claim 17, wherein, the program causing the computer to function as:
the storage unit, in which a correspondence relationship is stored for each of a plurality of operation target functional units of each of the operation target apparatuses,
the operation target apparatus specifying unit, which specifies an operation target functional unit concurrently with the specification of an operation target apparatus, and
the operation processing unit, which performs the operation processing for the operation target functional unit specified by the operation target apparatus specifying unit.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for automated analysis of data from an encoded bead multiplex assay for detection of chromosomal aneuploidies andor microdeletions, the method comprising the steps of:
(a) providing or receiving a set of background-subtracted data corresponding to an encoded bead multiplex assay for a plurality of patient samples run in parallel, wherein the data represents signals detected from beads corresponding to each of a plurality of chromosomal targets for each of a first through nth patient sample, wherein the chromosomal targets are selected for the detection of chromosomal aneuploidies andor microdeletions;
(b) following step (a), normalizing, by a processor of a computing device, the background-subtracted data from step (a) for each of the first through nth patient samples using a median of signals detected from beads for the corresponding first through nth patient sample, thereby producing normalized data;
(c) following step (b), for the normalized data corresponding to each chromosomal target, determining, by the processor, a principal component, and
for each principal component, determining, by the processor, a corresponding parallel component and an orthogonal component using the normalized data from step (b);
(d) following step (c), for each of the first through nth patient sample and for each chromosomal target, identifying a deviation from a threshold value indicative of a signal from a normal sample using the corresponding parallel components determined in step (c); and
(e) following step (d), for each of the first through nth patient sample and for each chromosomal target, identifying at least one quality parameter indicative of sample preparation quality using the corresponding orthogonal components determined in step (c).
2. The method of claim 1, further comprising the step of:
(f) determining one or more chromosomal aneuploidies andor microdeletions for any one or more of the first through nth patient samples on the basis of the deviations determined in step (d) and the quality parameters determined in step (e).
3. The method of claim 1, wherein the background-subtracted data in step (a) represents signals detected from 2 to 10 encoded bead types corresponding to each of the chromosomal targets.
4. (canceled)
5. The method of claim 1, wherein the background-subtracted data in step (a) represents signals detected from encoded beads corresponding to each of at least 3 chromosomal targets for the detection of chromosomal aneuploidies andor microdeletions.
6.-7. (canceled)
8. The method of claim 1, wherein the background-subtracted data in step (a) represents signals detected from beads for each of from at least 5 patient samples.
9.-10. (canceled)
11. The method of claim 1, wherein the plurality of samples run in parallel are run on a single microplate for signal detection.
12. The method of claim 1, wherein the chromosomal targets are selected for detection of one or more chromosomal aneuploidies, wherein the one or more chromosomal aneuploidies comprise at least one trisomy.
13. The method of claim 1, wherein the chromosomal targets are selected for detection of one or more microdelections each having length in the range of from 20 to 300 kilobases.
14. The method of claim 1, wherein step (b) comprises normalizing the background-subtracted data from step (a) for each of the first through nth patient samples using a median of signals detected from beads for the corresponding first through nth patient sample and using a median of medians of signals from the plurality of patient samples run in parallel, thereby producing the normalized data.
15. The method of claim 1, wherein step (b) comprises normalizing the data for a first through mth bead type of the first through nth patient sample using a median of signals detected from the corresponding first through mth bead type of the plurality of patient samples run in parallel.
16. The method of claim 1, wherein step (b) comprises normalizing the background-subtracted data from step (a) for each of the first through nth patient samples using a normalization factor that eliminates bead-to-bead variation, thereby producing double-distilled normalized data.
17. The method of claim 1, wherein step (c) comprises determining the corresponding parallel component and the orthogonal component using the normalized data for the corresponding chromosomal target for the plurality of patient samples.
18.-19. (canceled)
20. The method of claim 1, wherein the at least one quality parameter identified in step (e) indicates whether a deviation identified in step (d) is suspicious (false positive).
21. The method of claim 1, wherein the at least one quality parameter for a given patient sample and a given chromosomal target is identified in step (e) using deviations identified in step (d) for other chromosomal targets for the given patient sample, such that multiple anomalies are identified as indicative of poor sample preparation.
22. The method of claim 1, wherein the chromosomal targets are selected for the detection of chromosomal aneuploidies andor microdeletions comprising at least one member selected from the group consisting of Williams-Beuren Syndrome, Smith-Magenis Syndrome, Angleman Syndrome, Down Syndrome (Trisomy 21), Edwards Syndrome (Trisomy 18 & X), Patau Syndrome, DiGeorge Syndrome (Velocardio Facial Syndrome), Mille-Dieker Syndrome, Solf-Hirschorn Syndrome, Langer-Giedion Syndrome, Cri-du-chat Syndrome, Prader-Willi Syndrome, 47 XYY Syndrome, and DiGeorge II Syndrome (10p14 microdeletion).
23. The method of claim 1, further comprising determining a gender for each of the first through nth patient samples by determining a principal component and corresponding parallel component for a Y chromosome target and identifying a deviation from a threshold value indicative of a signal from a male or female sample using the corresponding parallel component.
24. An apparatus for automated analysis of data from an encoded bead multiplex assay for detection of chromosomal aneuploidies andor microdeletions, the apparatus comprising:
a memory for storing a code defining a set of instructions; and
a processor for executing the set of instructions, wherein the instructions, when executed, cause the processor to:
(a) provide a set of background-subtracted data corresponding to an encoded bead multiplex assay for a plurality of patient samples run in parallel, wherein the data represents signals detected from beads corresponding to each of a plurality of chromosomal targets for each of a first through nth patient sample, wherein the chromosomal targets are selected for the detection of chromosomal aneuploidies andor microdeletions;
(b) following step (a), normalize the background-subtracted data from step (a) for each of the first through nth patient samples using a median of signals detected from beads for the corresponding first through nth patient sample, thereby producing normalized data;
(c) following step (b), for the normalized data corresponding to each chromosomal target, determine a principal component and for each principal component, determine a corresponding parallel component and an orthogonal component using the normalized data from step (b);
(d) following step (c), for each of the first through nth patient sample and for each chromosomal target, identify a deviation from a threshold value indicative of a signal from a normal sample using the corresponding parallel components determined in step (c); and
(e) following step (d), for each of the first through nth patient sample and for each chromosomal target, identify at least one quality parameter indicative of sample preparation quality using the corresponding orthogonal components determined in step (c).
25. A method comprising:
accessing, by a processor of a computing device, a set of background-subtracted data corresponding to an encoded bead multiplex assay, wherein
the set of background-subtracted data comprises data related to a plurality of patient samples,
the background-subtracted data represents signals detected from beads corresponding to each chromosomal target of a plurality of chromosomal targets for each patient sample of the plurality of patient samples, and
each chromosomal target of the plurality of chromosomal targets is identified for the detection of at least one of chromosomal aneuploidies and microdeletions;
for each patient sample of the plurality of patient samples,
normalizing, by the processor, the background-subtracted data of the respective patient sample to determine normalized data, wherein normalizing comprises determining a median of signals detected from beads of the respective patient sample,
for each chromosomal target of the plurality of chromosomal targets,
determining, by the processor, a respective principal component of the respective normalized data, and
determining, by the processor, a parallel component of the respective principal component; and
for at least a first chromosomal target of the plurality of chromosomal targets, and for at least a first patient sample of the plurality of patient samples, using the respective parallel component, identifying, by the processor, one or more signal values within the respective normalized data deviating by at least a threshold value from a normal sample value, wherein the one or more signal values represent potential genetic abnormality.
26. The method of claim 25, further comprising, for each chromosomal target of the plurality of chromosomal targets, for each patient sample of the plurality of patient samples:
determining an orthogonal component of the respective principal component; and
identifying, based at least in part upon the orthogonal component, one or more quality parameters indicative of sample preparation quality.
27. The method of claim 26, further comprising, for at least the first chromosomal target of the plurality of chromosomal targets, and for at least the first patient sample of the plurality of patient samples, identifying a suspected bad sample, wherein the suspected bad sample is identified based in part upon at least one of the one or more quality parameters indicative of sample preparation quality.
28. (canceled)
29. The method of claim 26, further comprising, for at least the first chromosomal target of the plurality of chromosomal targets, and for at least the first patient sample of the plurality of patient samples, confirming genetic abnormality in relation to the one or more signal values within the respective normalized data deviating by at least the threshold value from the normal sample value, wherein confirming genetic abnormality comprises confirming the one or more quality parameters are indicative of good sample preparation quality.
30. The method of claim 25, further comprising, after normalizing the background-subtracted data, renormalizing the background-subtracted data, wherein renormalizing the background-subtracted data comprises determining a median of a first normalized bead signal a for all patients of the plurality of patients, and, for each patient of the plurality of patients, normalizing the respective normalized data using the median of the first normalized bead signal \u03b1.
31. The method of claim 25, further comprising, for each patient sample of the plurality of patients samples, determining a gender of the respective patient, wherein determining the gender of the respective patient comprises identifying, using the respective parallel component, a deviation from a threshold value indicative of a signal from one of a male sample and a female sample.
32. The method of claim 25, further comprising determining the threshold value, wherein the threshold value is based upon a mean absolute deviation within the normalized data.
33.-34. (canceled)