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.
Please help me write a proper abstract based on the patent claims. 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. 2. The computer-implemented method of claim 1, further comprising: 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. 3. The computer-implemented method of claim 2, wherein the at least one parameter is selected from the group consisting of: a sliding window size; an overlap length between consecutive sliding windows; the number N of consecutive sliding windows; the threshold number M of activity detections; 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. 4. The computer-implemented method of claim 2, wherein said determining comprises the steps of: (a) selecting a value for the at least one parameter prior to said segmenting, extracting, analyzing, and determining; (b) performing said segmenting, extracting, analyzing, and determining in accordance with the value to obtain a plurality of activity detection results; (c) calculating false positive rate per time unit for the plurality of activity detection results based on the time-based constraint; (d) calculating true positive rate of the plurality of activity detection results; and (e) repeating steps (b) to (d) with one or more different values. 5. The computer-implemented method of claim 2, wherein the data is supervised. 6. The computer-implemented method of claim 2, wherein said determining at least one parameter 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. 7. The computer-implemented method of claim 2, wherein the time-based constraint on the false positive rate of activity detections is defined so as to reduce a probability of alert fatigue by a user receiving alerts on activity detections. 8. The computer-implemented method of claim 1, wherein the one or more sensors are wearable sensors. 9. 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. 10. The computer-implemented method of claim 1, further comprising issuing an alert responsive to determining the activity detection result to be positive. 11. A computer-implemented method comprising: 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. 12. A computerized apparatus having a processor, the processor being adapted to perform the steps of: 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. 13. The computerized apparatus of claim 12, wherein the processor is further adapted for: 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. 14. The computerized apparatus of claim 13, wherein the at least one parameter is selected from the group consisting of: a sliding window size; an overlap length between consecutive sliding windows; the number N of consecutive sliding windows; the threshold number M of activity detections; 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. 15. The computerized apparatus of claim 13, wherein said determining comprises the steps of: (a) selecting a value for the at least one parameter prior to said segmenting, extracting, analyzing, and determining; (b) performing said segmenting, extracting, analyzing, and determining in accordance with the value to obtain a plurality of activity detection results; (c) calculating false positive rate per time unit for the plurality of activity detection results based on the time-based constraint; (d) calculating true positive rate of the plurality of activity detection results; and (e) repeating steps (b) to (d) with one or more different values. 16. The computerized apparatus of claim 13, wherein the time-based constraint on the false positive rate of activity detections is defined so as to reduce a probability of alert fatigue by a user receiving alerts on activity detections. 17. A computerized apparatus having a processor, the processor being adapted to perform the steps of: 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. 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: 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. 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: 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.