# US 10,504,036 B2 · Optimizing performance of event detection by sensor data analytics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,504,036 B2 - Document ID: US10504036B2 - Status: Granted - Kind: B2 - Application number: 14/988,762 - Filed: 2016-01-06 - Published: 2017-07-06 - Granted: 2019-12-10 - Earliest claimed: 2016-01-06 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Lior Limonad; Nir Mashkif; Segev E Wasserkrug; Alexander Zadorojniy; Sergey Zeltyn - Web page: https://patentgrep.com/patent/US10504036B2 ## CPC - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: receiving a time-based constraint on false positive rate of activity detections, wherein the time-based constraint comprises the threshold number of false positives allowed over the specified time window. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the activity detection parameters further comprise at least one parameter selected from the group consisting of: a sliding window size; an overlap length between consecutive sliding windows; a type of classification algorithm utilized by the machine learning process; a parameter of classification algorithm utilized by the machine learning process; and type of features extracted. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein said determining comprises the steps of: (a) selecting a value for the activity detection parameters prior to said segmenting, extracting, analyzing, and determining; (b) performing said segmenting, extracting, analyzing, and determining in accordance with the values to obtain a plurality of activity detection results; and (e) repeating steps (b) to (d) with one or more different values. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the data is supervised. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein said optimizing the activity detection parameters comprises: using a first portion of the data as training set for the machine learning process; and using a second portion of the data as testing set for the machine learning process. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the one or more sensors are wearable sensors. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising preprocessing the data prior to said segmenting, the preprocessing comprising at least one step from the group consisting of: cleaning; and multi-dimensional synchronization. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, further comprising issuing an alert responsive to determining the activity detection result to be positive. ### Claim 10 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. ### Claim 11 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. ### Claim 12 (depends on 11) The computerized apparatus of claim 11, wherein the activity detection parameters further comprise at least one parameter selected from the group consisting of: a sliding window size; an overlap length between consecutive sliding windows; a type of classification algorithm utilized by the machine learning process; a parameter of classification algorithm utilized by the machine learning process; and type of features extracted. ### Claim 13 (depends on 11) The computerized apparatus of claim 11, wherein said determining comprises the steps of: (a) selecting a value for the activity detection parameters prior to said segmenting, extracting, analyzing, and determining; (b) performing said segmenting, extracting, analyzing, and determining in accordance with the values to obtain a plurality of activity detection results; and (e) repeating steps (b) to (d) with one or more different values. ### Claim 14 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. ### Claim 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: 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. ### Claim 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: 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.