Optimizing performance of event detection by sensor data analytics
First Claim
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1. A computer-implemented method comprising:
- obtaining data measured by one or more sensors, the data corresponding to a blood glucose level;
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 and N>
0, wherein M and N are activity detection parameters;
automatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range;
determining a number of false positives and a number of true positives encountered over a plurality of activity detection results; and
optimize the activity detection parameters by, at least in part, adjusting M and N to maximize the number of true positives while maintaining the number of false positives encountered over a specified time period below a threshold number of false positives.
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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.
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Citations
16 Claims
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1. A computer-implemented method comprising:
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obtaining data measured by one or more sensors, the data corresponding to a blood glucose level; 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 and N>
0, wherein M and N are activity detection parameters;automatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range; determining a number of false positives and a number of true positives encountered over a plurality of activity detection results; and optimize the activity detection parameters by, at least in part, adjusting M and N to maximize the number of true positives while maintaining the number of false positives encountered over a specified time period below a threshold number of false positives. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-implemented method comprising:
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obtaining data measured by one or more sensors, the data corresponding to a blood glucose level; receiving a time-based constraint on false positive rate of activity detections, wherein the time-based constraint comprises a maximum number of false positives over a specified time window; determining at least one parameter maximizing true positive rate of activity detections subject to the time-based constraint, wherein the at least one parameter comprises a number M of sliding windows and a number N of consecutive sliding windows, wherein M>
1 and N>
0, said determining comprises the steps of;(a) selecting a value for the at least one parameter; (b) segmenting the data into a plurality of N sliding windows; (c) extracting one or more features from each of the plurality of N 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 to obtain a plurality of activity detection results, wherein a positive result is indicated if the plurality of activity detection results are positive for at least N consecutive windows; (e) calculating a number of false positives for the plurality of activity detection results; (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 for M and N to identify values of M and N that maximize the true positive rate while maintaining the number of false positives encountered over the specified time period below the maximum number of false positives; wherein said segmenting, extracting, and analyzing are performed in accordance with the values; and automatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range.
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11. 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, the data corresponding to a blood glucose level; 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; 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 and N>
0, wherein M and N are activity detection parameters; andautomatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range; determining a number of false positives and a number of true positives encountered over a plurality of activity detection results; and optimize the activity detection parameters by, at least in part, adjusting M and N to maximize the number of true positives while maintaining the number of false positives encountered over a specified time period below a threshold number of false positives. - View Dependent Claims (12, 13)
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14. 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, the data corresponding to a blood glucose level; receiving a time-based constraint on false positive rate of activity detections, wherein the time-based constraint comprises a maximum number of false positives over a specified time window; determining at least one parameter maximizing true positive rate of activity detections subject to the time-based constraint, wherein the at least one parameter comprises a number M of sliding windows and a number N of consecutive sliding windows, wherein M>
1 and N>
0, said determining comprises the steps of;(a) selecting a value for the at least one parameter; (b) segmenting the data into a plurality of N sliding windows; (c) extracting one or more features from each of the plurality of N 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 to obtain a plurality of activity detection results, wherein a positive result is indicated if the plurality of activity detection results are positive for at least N consecutive windows; (e) calculating a number of false positives for the plurality of activity detection results; (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 for M and N to identify values of M and N that maximize the true positive rate while maintaining the number of false positives encountered over the specified time period below the maximum number of false positives; wherein said segmenting, extracting, and analyzing are performed in accordance with the values; and automatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range.
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15. 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, the data corresponding to a blood glucose level; 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; 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 and N>
0, wherein M and N are activity detection parameters; andautomatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range; determining a number of false positives and a number of true positives encountered over a plurality of activity detection results; and optimize the activity detection parameters by adjusting M and N to maximize the number of true positives while maintaining the number of false positives encountered over a specified time period below a threshold number of false positives.
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16. 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, the data corresponding to a blood glucose level; receiving a time-based constraint on false positive rate of activity detections, wherein the time-based constraint comprises a maximum number of false positives over a specified time window; determining at least one parameter maximizing true positive rate of activity detections subject to the time-based constraint, wherein the at least one parameter comprises a number M of sliding windows and a number N of consecutive sliding windows, wherein M>
1 and N>
0, said determining comprises the steps of;(a) selecting a value for the at least one parameter; (b) segmenting the data into a plurality of N sliding windows; (c) extracting one or more features from each of the plurality of N 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 to obtain a plurality of activity detection results; (e) calculating a number of false positives for the plurality of activity detection results; (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 for M and N to identify values of M and N that maximize the true positive rate while maintaining the number of false positives encountered over the specified time period below the maximum number of false positives; wherein said segmenting, extracting, and analyzing are performed in accordance with the values; and automatically administering a drug to a patient in response to the activity detection result indicating the blood glucose level is outside of a predetermined range.
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Specification