Abstract: A method for determining a user's stress level is performed by a smartphone app. Touch and motion feature values are generated, the feature values are weighted by regression parameters, and a stress score is generated based on the weighted touch and motion feature values. The touch feature values indicate how the user's finger moves over the smartphone and are generated from touch data points including X positions, Y positions and associated touch timestamp values. The motion feature values indicate movement of the smartphone and are generated from motion data points including X movements, Y movements, Z movements and associated motion timestamp values. The regression parameters are generated using touch and motion data identified by other users as being acquired while those other users were experiencing various perceived levels of stress. The app indicates to the user whether the stress score is higher or lower than a previously generated stress score.

Abstract: An autocomplete function for textual input uses situational parameters to predict the next words the user is intending to type. Situational and temporal parameters are based on textual input and sensor data of the user. A past time window is based on the situational and temporal parameters. Historical textual input and sensor data during the time window relating to the situational parameters are retrieved from a storage device and aggregated. A pre-existing model that relates the situational parameter to the time window is used to select a situational value based on the textual input and sensor data. Words relating to the situational parameter are listed that the user is likely to input next based on the selected situational value. The words are ranked by the probability that the user is intending to type each of the words. The highest ranked word is displayed to the user on a user interface.

Abstract: A device running a mobile app optimizes battery power, data storage, and accuracy of a model. Data concerning a setting and modality of a sensor of the device is collected. Historical data is retrieved concerning the amounts of battery power and data storage consumed while the sensor operates in the setting. The model is trained based on the data concerning the setting and modality and on the historical data concerning the consumed battery power and data storage. The model is based on the setting of the sensor. The accuracy achieved by the model at a maximum consumed battery power and data storage is measured. The accuracy achieved by the model is compared to an accuracy threshold. The model is adopted if the accuracy of the model equals or exceeds the accuracy threshold. The device is configured to operate the sensor using the setting and the mobile app using the model.

Abstract: An autocomplete function for textual input uses situational parameters to predict the next words the user is intending to type. Situational and temporal parameters are based on textual input and sensor data of the user. A past time window is based on the situational and temporal parameters. Historical textual input and sensor data during the time window relating to the situational parameters are retrieved from a storage device and aggregated. A pre-existing model that relates the situational parameter to the time window is used to select a situational value based on the textual input and sensor data. Words relating to the situational parameter are listed that the user is likely to input next based on the selected situational value. The words are ranked by the probability that the user is intending to type each of the words. The highest ranked word is displayed to the user on a user interface.

Abstract: A system for triggering mental healthcare services based on prediction of critical events receives both structured data and unstructured data about mental healthcare patients. Feature extraction is performed, thereby generating records of structured data, and strings of vectors of unstructured data. The system makes a quality assessment about the structured data, and a quality assessment about the unstructured data. In a model selection step, the system selects one model out of a plurality of selectable models, where the selection is made based on the quality assessments made. The selected model is trained with records of structured data and with strings of vectors of unstructured data. New real-time data is supplied to the trained model so that the trained model predicts whether a crisis event is likely to occur. If the system predicts that a crisis event is likely to occur, then the system outputs an alert.

Abstract: A system for determining a mental health state of a user and for adjusting output content accordingly includes a monitoring unit that monitors parameters of the user and acquires corresponding data, an analysis unit that extracts features from data acquired by the monitoring unit, a classification unit that detects change in the mental health state of the user based on the features extracted by the analysis unit and classifies the change in mental state of the user, a control unit that adapts the content to be output to the user by the output unit based on the detected change in the mental health state of the user classified by the classification unit, and an output unit that outputs content to the user based on the detected change in the mental health state of the user. A method determines the mental health state of the user and adjusts output content accordingly.

Abstract: An autocomplete function for textual input uses situational parameters to predict the next words the user is intending to type. Situational and temporal parameters are based on textual input and sensor data of the user. A past time window is based on the situational and temporal parameters. Historical textual input and sensor data during the time window relating to the situational parameters are retrieved from a storage device and aggregated. A pre-existing model that relates the situational parameter to the time window is used to select a situational value based on the textual input and sensor data. Words relating to the situational parameter are listed that the user is likely to input next based on the selected situational value. The words are ranked by the probability that the user is intending to type each of the words. The highest ranked word is displayed to the user on a user interface.

Abstract: A method for determining a user's stress level is performed by a smartphone app. Touch and motion feature values are generated, the feature values are weighted by regression parameters, and a stress score is generated based on the weighted touch and motion feature values. The touch feature values indicate how the user's finger moves over the smartphone and are generated from touch data points including X positions, Y positions and associated touch timestamp values. The motion feature values indicate movement of the smartphone and are generated from motion data points including X movements, Y movements, Z movements and associated motion timestamp values. The regression parameters are generated using touch and motion data identified by other users as being acquired while those other users were experiencing various perceived levels of stress. The app indicates to the user whether the stress score is higher or lower than a previously generated stress score.

Abstract: A method and apparatus for decomposition of signals with varying envelope into offset components are disclosed here, that sample the time variant envelope of a single carrier (SC) or a multi-carrier (MC) band limited signal, quantizes the sampled value using Nb quantization bits and decomposes the sample into Nb in-phase and quadrature components that are combined in pairs and modulated to generate a set of Nb offset signals. The pulse shape applied in each offset signal is selected according to the spectral mask needed for the signal and to minimize envelope fluctuations in each offset signal from the set of Nb components.

Abstract: A method and apparatus for decomposition of signals with varying envelope into offset components are disclosed here, that sample the time variant envelope of a single carrier (SC) or a multi-carrier (MC) band limited signal, quantizes the sampled value using Nb quantization bits and decomposes the sample into Nb in-phase and quadrature components that are combined in pairs and modulated to generate a set of Nb offset signals. The pulse shape applied in each offset signal is selected according to the spectral mask needed for the signal and to minimize envelope fluctuations in each offset signal from the set of Nb components.

Abstract: An apparatus for quantized linear amplification with nonlinear amplifiers that performs a linear amplification of variable-envelope single carrier (SC) or multi-carrier (MC) bandpass signals, based on sampled and quantized versions of its complex envelope, where the quantizer generates Nb bits that are mapped into Nm?Nb polar components, in which the quantized symbol can be decomposed, that are modulated as Nm constant or quasi constant envelope signals and where each one is amplified by a nonlinear amplifier.

Abstract: An apparatus for quantized linear amplification with nonlinear amplifiers that performs a linear amplification of variable-envelope single carrier (SC) or multi-carrier (MC) bandpass signals, based on sampled and quantized versions of its complex envelope, where the quantizer generates Nb bits that are mapped into Nm?Nb polar components, in which the quantized symbol can be decomposed, that are modulated as Nm constant or quasi constant envelope signals and where each one is amplified by a nonlinear amplifier.