A method and system for remotely monitoring intoxication of a user, comprising: prompting the user to provide a breath sample at a time point; at a breath sample acquisition device, generating a breath sample signal upon reception of the breath sample from the user, and broadcasting a unique signature proximal in time to the time point; using a sensor of a mobile computing device, generating an authentication signal derived from detection of the unique signature; at a processing system in communication with the mobile computing device and the sample acquisition device, receiving the breath sample signal and the authentication signal; generating a verification assessment that validates provision of the breath sample by the user; determining a value of an intoxication metric for the user based upon the breath sample signal; and transforming the verification assessment and the value of the intoxication metric into an analysis of intoxication of the user.

The invention provides a visualized time sequence pattern matching method based on Hough transformation, and relates to the technical field of data visualization analysis. The method comprises the steps of: firstly, judging whether historical data to be matched is one-dimensional time sequence data or multi-dimensional time sequence data, and if the historical data to be matched is the multi-dimensional time sequence data, performing normalization processing; performing time sequence selection: selecting a time sequence to be matched from the historical data in a time window pattern, and eliminating the selected time sequence from the historical data; converting a time sequence image in original coordinates to Hough space through the Hough transformation, and judging the similarity matching situation of the time sequence through a voting mechanism; and finally, screening the finally-matched results according to the voting results.

A biometric data measurement system and method, including a transmitting device including a measurement unit configured to measure a first biometric data, a setting unit configured to set a first reference time corresponding to an occurrence time of a first feature value of the first biometric data, and a transmission unit configured to transmit a signal at a time when a predetermined first time period has elapsed since the first reference time, and a receiving device including a measurement unit configured to measure a second biometric data, a setting unit configured to set a second reference time corresponding to an occurrence time of a second feature value of the second biometric data, and a reception unit configured to maintain an awaiting state for the signal during a third time period from a time when a second time period has elapsed since the second reference time.

A method of generating a user affect prediction includes receiving a label for a user-reported affect corresponding to interactions with the user interface, receiving events corresponding to the interactions with the user interface, identifying one or more patterns of the events as one or more gestures and extracting one or more features of the gestures. The method uses a machine learning model to generate a user affect prediction based on the training features. The user affect prediction represents a predicted user affect corresponding to the interactions with the user interface. The machine learning model may be trained by modifying one or more parameters of the machine learning model using a difference between the label and the generated user affect prediction.

Disclosed herein is an apparatus and method for generating a driving model. The driving model generation method includes sensing to collect a brain wave signal for a driver in a mobility for a predetermined time, determining the driver's state by analyzing the brain wave signal collected for the predetermined time, detecting at least one of operational information and driving information of the mobility on the basis of the determined state of the driver, and storing the detected information. Herein, the brain wave signal includes an event-related potential (ERP), and the determining includes determining, by analyzing the ERP, whether or not the driver is in an unstable state.

Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Methods and devices for determining a head movement are provided. A method comprises: acquiring, in response to a head movement performed by a user, a piece of electrooculographic information of the user; and determining information related to the head movement according to the piece of electrooculographic information and at least one piece of reference information. The head movement can be identified according to electrooculographic information. For some devices integrated with electrooculographic sensors, the electrooculographic sensor can be reused to collect the electrooculographic information, and thereby reduce implementation costs.

A system, method, and a computer program configured to perform a method for processing drilling data. The method includes transmitting a signal with a telemetry tool to a computer processor. The method includes applying a plurality of predetermined templates to the signal. The method also includes applying a plurality of first filters to the transmitted signal. The method also includes applying one or more second, adjustable filters to the transmitted signal and to the plurality of predetermined templates. The method also includes decoding the transmitted signal based on the best match between a) two or more of the plurality of predetermined templates, and b) the transmitted signal, wherein the two or more of the plurality of predetermined templates and the transmitted signal are processed through the same adjustable filter of the one or more second, adjustable filters.

A method of operating an activity tracking system includes collecting activity data from an activity tracking device, receiving an activity type associated with the activity data via a user input, and evaluating the collected activity data to determine whether one or more aspects thereof are within a predetermined range of values assigned to the received activity type.

A system and/or method configured to determine highlight segments. Content files that define content in a content segment set may be obtained. A highlight segment set may be determined from the content segment set. Determining the highlight segment set may include iterating (a)-(c) for multiple iterations. At (a), individual content segments included in the content segment set may be selected as a selected content segment for inclusion in the highlight segment set. At, (b) diversity scores for content segments that are (i) included in the content segment set and (ii) not yet selected for inclusion in the highlight segment set may be determined. At (c), one or more of the content segments may be disqualified for inclusion in the highlight segment set for future iterations based on the diversity scores.