Optimizing performance of event detection by sensor data analytics
First Claim
1. A computer-implemented method comprising:
- obtaining data measured by one or more sensors;
segmenting the data into a plurality of sliding windows;
extracting one or more features from each of the plurality of sliding windows;
analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and
determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>
1.
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Accused Products
Abstract
A computer-implemented method, computerized apparatus and computer program product, the method comprising: obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>1.
20 Citations
19 Claims
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1. A computer-implemented method comprising:
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obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>
1. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method comprising:
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obtaining data measured by one or more sensors; receiving a time-based constraint on false positive rate of activity detections; and determining at least one parameter maximizing true positive rate of activity detections subject to the time-based constraint, said determining comprises the steps of; (a) selecting a value for the at least one parameter; (b) segmenting the data into a plurality of sliding windows; (c) extracting one or more features from each of the plurality of sliding windows; (d) analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection result in the sliding window, whereby obtaining a plurality of activity detection results; (e) calculating false positive rate per time unit for the plurality of activity detection results based on the time-based constraint; (f) calculating true positive rate of the plurality of activity detection results; and (g) repeating steps (b) to (f) with one or more different values; wherein said segmenting, extracting, and analyzing are performed in accordance with the value.
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12. A computerized apparatus having a processor, the processor being adapted to perform the steps of:
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obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>
1. - View Dependent Claims (13, 14, 15, 16)
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17. A computerized apparatus having a processor, the processor being adapted to perform the steps of:
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obtaining data measured by one or more sensors; receiving a time-based constraint on false positive rate of activity detections; and determining at least one parameter maximizing true positive rate of activity detections subject to the time-based constraint, said determining comprises the steps of; (a) selecting a value for the at least one parameter; (b) segmenting the data into a plurality of sliding windows; (c) extracting one or more features from each of the plurality of sliding windows; (d) analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection result in the sliding window, whereby obtaining a plurality of activity detection results; (e) calculating false positive rate per time unit for the plurality of activity detection results based on the time-based constraint; (f) calculating true positive rate of the plurality of activity detection results; and (g) repeating steps (b) to (f) with one or more different values; wherein said segmenting, extracting, and analyzing are performed in accordance with the value.
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18. A computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising:
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obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>
1.
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19. A computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising:
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obtaining data measured by one or more sensors; receiving a time-based constraint on false positive rate of activity detections; and determining at least one parameter maximizing true positive rate of activity detections subject to the time-based constraint, said determining comprises the steps of; (a) selecting a value for the at least one parameter; (b) segmenting the data into a plurality of sliding windows; (c) extracting one or more features from each of the plurality of sliding windows; (d) analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection result in the sliding window, whereby obtaining a plurality of activity detection results; (e) calculating false positive rate per time unit for the plurality of activity detection results based on the time-based constraint; (f) calculating true positive rate of the plurality of activity detection results; and (g) repeating steps (b) to (f) with one or more different values; wherein said segmenting, extracting, and analyzing are performed in accordance with the value.
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Specification