Optimizing performance of event detection by sensor data analytics

A computer-implemented method comprising:

• 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.

A computerized apparatus having a processor, the processor being adapted to perform the steps of:

• 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;
• 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;
• 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.

A computerized apparatus having a processor, the processor being adapted to perform the steps of:

• 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.

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:

• 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;
• 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;
• 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.

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:

• 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.