Abstract: A method for detecting airbag deployment includes operating a plurality of sensors of the mobile device disposed in a vehicle during a drive to obtain a plurality of measurement signals, determining a change in at least one measurement signal of the plurality of measurement signals and that the change exceeds a first threshold. In response to determining that the change exceeds the first threshold, obtaining a pressure measurement signal from a pressure sensor of the plurality of sensors, determining a derivative of the pressure measurement signal, and determining that the derivative of the pressure measurement signal exceeds a second threshold. In response to determining that the derivative of the pressure measurement signal exceeds the second threshold, detecting a deployment of a vehicle airbag based on the change in the at least one measurement signal exceeding the first threshold and the derivative of the pressure measurement signal exceeding the second threshold.

Abstract: Techniques are disclosed for virtual tagging of vehicles that include generating an association between a user of a mobile device and the mobile device. The techniques include receiving a first set of measurements from one or more sensors of the mobile device while the mobile device is positioned in a first vehicle during a trip and training a machine-learning model using the first set of measurements. The techniques further include receiving a second set of measurements from the one or more sensors of the mobile device and determining, by executing the machine-learning model using the third set of measurements, that the mobile device is positioned in the first vehicle or a second vehicle.

Abstract: Techniques are disclosed for predicting a confidence of a total loss event. A mobile device detects a crash event using one or more sensors of a mobile device. The mobile device records a first set of data from the one or more sensors of the mobile device. The mobile device generates a first feature vector including the first set of data and vehicle data that includes an identifier of a vehicle. The mobile device generates a second feature vector using the first set of data and additional data types. The mobile device predicts a confidence of a total loss event by generating a first confidence value from a first machine-learning model using the first feature vector and a second confidence value from a second machine-learning model using the second feature vector.

Abstract: Techniques are disclosed for accessing sensor-based driving data that includes detecting a waking event associated with an application and activating the application in response to detecting the waking event. Activity data for a recorded time interval preceding the waking event may be used to detect a missed trip by: identifying a first time in which the activity data indicates a high probability of automotive activity and identifying a second time in which the activity data indicates a low probability of automotive activity, wherein the second time instant occurs after the first time. The missed trip may correspond to a first time interval that extends between the first time and the second time. Sensor data may then be received for a second time interval that begins prior to the first time and ends after the second time.

Abstract: Apparatuses and methods for predicting a crash using estimated vehicle speed. A set of sensor measurements are received from a mobile device disposed within a vehicle. A set of contiguous windows based on the sensor measurements may be defined. Each contiguous window represents a contiguous portion of the sensor measurements. A set of sensor measurements may be defined for each contiguous window. A trained neural network may execute, using the set of features, to generate one or more speed predictions. A vehicle crash prediction may be generated using the speed prediction. The vehicle crash prediction may then be transmitted to a remote device.

Abstract: Methods and systems are disclosed for detecting physical interaction with an electronic device. Gyroscopic data may be received from gyroscopic sensors incorporated within the electronic device. A set of frequency bands may be defined for each axis of the plurality of axes. A frequency in which each value of the gyroscopic data is included in the gyroscopic data may be determined. The values associated with each frequency of a frequency band may be aggregated to generate a magnitude for the frequency band. A probability may be determined using the modified magnitude associated with each frequency band. If the probability exceeds a threshold then an indication that the user is physically interacting with the electronic device may be transmitted to a remote device.

Abstract: Methods and systems are disclosed for detecting physical interaction with an electronic device. Gyroscopic data may be received from gyroscopic sensors incorporated within the electronic device. A set of frequency bands may be defined for each axis of the plurality of axes. A frequency in which each value of the gyroscopic data is included in the gyroscopic data may be determined. The values associated with each frequency of a frequency band may be aggregated to generate a magnitude for the frequency band. A probability may be determined using the modified magnitude associated with each frequency band. If the probability exceeds a threshold then an indication that the user is physically interacting with the electronic device may be transmitted to a remote device.

Abstract: A method for detecting airbag deployment includes operating a plurality of sensors of the mobile device disposed in a vehicle during a drive to obtain a plurality of measurement signals, determining a change in at least one measurement signal of the plurality of measurement signals and that the change exceeds a first threshold. In response to determining that the change exceeds the first threshold, obtaining a pressure measurement signal from a pressure sensor of the plurality of sensors, determining a derivative of the pressure measurement signal, and determining that the derivative of the pressure measurement signal exceeds a second threshold. In response to determining that the derivative of the pressure measurement signal exceeds the second threshold, detecting a deployment of a vehicle airbag based on the change in the at least one measurement signal exceeding the first threshold and the derivative of the pressure measurement signal exceeding the second threshold.

Abstract: Systems and methods perform signature extraction from an acquired spectrum of a pharmaceutical. An acquired spectrum of the pharmaceutical is measured using a spectrometer. The acquired spectrum is obtained from the spectrometer using a processor. A system-response function of the spectrometer is removed from the acquired spectrum using the processor. An intensity of the acquired spectrum is normalized to a predetermined scale using the processor. Fluorescence is removed from the acquired spectrum using the processor. Finally, an extracted signature of the pharmaceutical is obtained from the remainder of the acquired spectrum using the processor. If the acquired spectrum of the pharmaceutical is measured by the spectrometer through a container holding the pharmaceutical, a spectrum of the container is removed from the remainder of the acquired spectrum to produce the extracted signature of the pharmaceutical using the processor.

Abstract: Systems and methods perform signature extraction from an acquired spectrum of a pharmaceutical. An acquired spectrum of the pharmaceutical is measured using a spectrometer. The acquired spectrum is obtained from the spectrometer using a processor. A system-response function of the spectrometer is removed from the acquired spectrum using the processor. An intensity of the acquired spectrum is normalized to a predetermined scale using the processor. Fluorescence is removed from the acquired spectrum using the processor. Finally, an extracted signature of the pharmaceutical is obtained from the remainder of the acquired spectrum using the processor. If the acquired spectrum of the pharmaceutical is measured by the spectrometer through a container holding the pharmaceutical, a spectrum of the container is removed from the remainder of the acquired spectrum to produce the extracted signature of the pharmaceutical using the processor.

Abstract: A method of quantifying similarities between sequential data streams typically includes providing a pair of sequential data streams, designing a Hidden Markov Model (HMM) of at least a portion of each stream; and computing a quantitative measure of similarity between the streams using the HMMs. For a plurality of sequential data streams, a matrix of quantitative measures of similarity may be created. A spectral analysis may be performed on the matrix of quantitative measure of similarity matrix to define a multi-dimensional diffusion space, and the plurality of sequential data streams may be graphically represented and/or sorted according to the similarities therebetween. In addition, semi-supervised and active learning algorithms may be utilized to learn a user's preferences for data streams and recommend additional data streams that are similar to those preferred by the user. Multi-task learning algorithms may also be applied.