Abstract: A method for determining a risk of decompensated heart failure in a user includes receiving a first set of data that is fixed with respect to time. A machine-learning model generates one or more initial risk factors based on the first set of data. A second set of data for the user that dynamically updates over time is received from a wearable cardiovascular physiology monitor. The machine-learning model is used to generate dynamic data classifiers based on the one or more initial risk factors. Aggregate risk scores for the user are then indicated based on an evaluation of the second set of data against the dynamic data classifiers. In this way, static electronic medical records may be combined with dynamic, real-time data from wearable cardiovascular physiology monitors to provide an accurate and continuously updating risk of decompensated heart failure for a user.

Abstract: A transit fare approach that offers a transit agency the ability to cap how many transit passes of a particular type in a given fare cycle would be equivalent of buying a discounted transit product (like a weekly pass, a bi-weekly pass, or a monthly pass). Once this equivalent is reached by a rider, the system automatically gives the rider the corresponding discount pass for free. The free pass remains activated and good for the remainder of the fare cycle. A software module provides a user app for the rider's mobile device and a controller app for a control unit of the transit agency, both of which operate in conjunction with each other to monitor the use of transit passes by the rider to determine when the rider qualifies for the fare cap-based free transit product and subsequently reward the rider with the free use of the appropriate transit product.

Abstract: A first data window of a pulse waveform signal comprising a first number of samples is analyzed to determine a level of confidence that a pulse sensing device is placed correctly. If an initial level of confidence is met, the user is given positive feedback, and a second data window of a pulse waveform signal comprising a second, larger number of samples is analyzed. If an increased level of confidence is met, the user is given increased positive feedback. If a level of confidence is not met, the user is given negative feedback. If a final level of confidence is met, the user is given feedback that the pulse sensing device is placed correctly.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: According to one aspect of the present disclosure, a method for estimating blood pressure is provided, comprising training a machine learning model on a cohort data set. The cohort data set may include subject-specific contextual data, time-varying features, and blood pressure measurements for a plurality of subjects. The method may include receiving contextual data for a specific subject, wherein the contextual data includes medical history data of the subject. The method may further include personalizing the machine learning model to the subject based on the contextual data. The method may include calibrating the machine learning model to the subject based on a set of time-varying features and blood pressure measurements of the subject. In addition, the method may include using the machine learning model and the time-varying features for the subject to generate a blood pressure estimate.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: A method and system, are provided herein, for automatic selection of one or more items (e.g., files, emails, etc.) in a user interface, based upon selection data received from a user (hard labels) and possibly also predicted (soft) labels (e.g., from a label prediction algorithm) based on behavior data associated with the selections. More particularly, a user selects and deselects items as positive and negative examples that are used as explicit labels to form an item selection list. The explicit labels are used possibly along with additional, predicted labels to create a list of generated items the user may want to select. This list is returned to the user interface as automatically selected items. In one example, the selection list may be iteratively updated upon receiving subsequent user selection data (e.g., user may select items incorrectly selected) by determining alternative items that the user may be interested in selecting.

Abstract: A wrist-worn pressure sensing device includes a pressure sensor. The wrist-worn pressure sensing device also includes a first strap that sets the position of the pressure sensor on a wearer's wrist and a second strap that engages with the first strap to adjust the overall length of the strap without moving the set position of the pressure sensor on the wearer's wrist.

Abstract: Generating responses to input utilizing an ontology-crowd-relevance methodology is described. The techniques described herein access a plurality of data items and determine an ontology associated with the plurality of data items. The ontology includes one or more ontological elements. Furthermore, the techniques describe sending, to a plurality of devices, a request to generate response templates based on the one or more ontological elements and receiving, from the plurality of devices, the response templates directed to the one or more ontological elements.

Abstract: According to one embodiment of the present disclosure, a computing device is provided, comprising a processor configured to receive an input. The input includes a first pulse pressure signal obtained using a wearable tonometer affixed to a body of a user. The processor is further configured to apply a transfer function to the first pulse pressure signal, wherein the transfer function converts the first pulse pressure signal into a transformed pulse pressure signal. The transformed pulse pressure signal simulates a second pulse pressure signal of a handheld tonometer concurrently applied to the body of the user.

Abstract: A method for determining a risk of decompensated heart failure in a user includes receiving a first set of data that is fixed with respect to time. A machine-learning model generates one or more initial risk factors based on the first set of data. A second set of data for the user that dynamically updates over time is received from a wearable cardiovascular physiology monitor. The machine-learning model is used to generate dynamic data classifiers based on the one or more initial risk factors. Aggregate risk scores for the user are then indicated based on an evaluation of the second set of data against the dynamic data classifiers. In this way, static electronic medical records may be combined with dynamic, real-time data from wearable cardiovascular physiology monitors to provide an accurate and continuously updating risk of decompensated heart failure for a user.

Abstract: A wearable tonometer is provided, comprising a sensing device. The sensing device may include a pressure sensor configured to measure a pulse pressure wave in an artery of user. The sensing device may include a resiliently deformable pad or pad-cap structure positioned on a sensing surface side of the pressure sensor and configured to contact skin of the user proximate the artery. The wearable tonometer may include a band that holds the sensing device in contact with the skin. In some embodiments, the sensing device may include a rigid internal structure configured to transmit the pulse pressure wave. In some embodiments, the wearable tonometer may include an adjustment mechanism configured to move the sensing device relative to the band. In some embodiments, the wearable tonometer may include a second resiliently deformable pad-cap structure, and a solid plate attached to the resiliently deformable pad-cap structures and the band.

Abstract: According to one aspect of the present disclosure, a method for estimating blood pressure is provided, comprising training a machine learning model on a cohort data set. The cohort data set may include subject-specific contextual data, time-varying features, and blood pressure measurements for a plurality of subjects. The method may include receiving contextual data for a specific subject, wherein the contextual data includes medical history data of the subject. The method may further include personalizing the machine learning model to the subject based on the contextual data. The method may include calibrating the machine learning model to the subject based on a set of time-varying features and blood pressure measurements of the subject. In addition, the method may include using the machine learning model and the time-varying features for the subject to generate a blood pressure estimate.

Abstract: Generating responses to input utilizing an ontology-crowd-relevance methodology is described. The techniques described herein access a plurality of data items and determine an ontology associated with the plurality of data items. The ontology includes one or more ontological elements. Furthermore, the techniques describe sending, to a plurality of devices, a request to generate response templates based on the one or more ontological elements and receiving, from the plurality of devices, the response templates directed to the one or more ontological elements.

Abstract: A system for transducing arterial pressure includes a one-piece flexible cap configured to fit around a flexible piezo-electric sensor that is configured to alter an internal resistance upon deflection. The flexible cap includes a deflection wall configured to deflect towards the flexible piezo-resistive sensor in proportion to pressure applied by the artery. A pressure-transducing medium is sealed between the one-piece flexible cap and the flexible piezo-resistive sensor, such that deflection of the deflection wall towards the flexible piezo-resistive sensor causes proportional deflection of the flexible piezo-resistive sensor.

Abstract: A wrist-worn pressure sensing device includes a pressure sensor. The wrist-worn pressure sensing device also includes a first strap that sets the position of the pressure sensor on a wearer's wrist and a second strap that engages with the first strap to adjust the overall length of the strap without moving the set position of the pressure sensor on the wearer's wrist.

Abstract: A first data window of a pulse waveform signal comprising a first number of samples is analyzed to determine a level of confidence that a pulse sensing device is placed correctly. If an initial level of confidence is met, the user is given positive feedback, and a second data window of a pulse waveform signal comprising a second, larger number of samples is analyzed. If an increased level of confidence is met, the user is given increased positive feedback. If a level of confidence is not met, the user is given negative feedback. If a final level of confidence is met, the user is given feedback that the pulse sensing device is placed correctly.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Techniques to organize information on a computing device using movable objects are described. A computer system may include a display operative to present a graphical user interface with a pointer to select one or more movable objects and position the movable objects at various target positions on the graphical user interface, an input device operative to receive selected movable objects and user movement to position the selected movable objects at a target position on the graphical user interface, and an object position component operative to anchor the selected movable objects at the target position using an anchor element to form a group of anchored objects, and arrange the group of anchored objects in a visual pattern relative to the anchor element. Other embodiments are described and claimed.