Abstract: Step detection accuracy in mobile devices is increased by determining whether swinging is taking place. According to the invention, swinging can be detected using threshold detection, Eigen analysis, hybrid frequency analysis, and/or gyroscope-based analysis, for example. The determination that swinging is (or may be) occurring can impact how the mobile device reports detected steps for step detection. A count of missteps and/or a level of certainty, based on swing detection, can be provided with a step count.

Abstract: Techniques for providing a user with an augmented virtuality (AV) experience are described herein. An example of a method of providing an AV experience includes determining a location of a mobile device, determining a context based on the location, obtaining AV object information, displaying the AV object information in relation to the context, detecting an interaction with the context, modifying the AV object information based on the interaction, and displaying the modified AV object information. The context may include weighting information. The weighting information may be based on Received Signal Strength Indication (RSSI) or Round-Trip Time (RTT) data. The weighting information may be associated with a composition of a physical object in the context. A user gesture may be received, and the AV object information may be modified based on the received gesture information.

Abstract: The disclosed embodiments include a method performed by server computer(s). The method includes obtaining private healthcare insurance claims data and public healthcare procedure code data, inserting that data in a n unlabeled fashion into an unsupervised machine learning program to model canonical healthcare episodes. A healthcare episode refers to all the services a given patient receives when visiting a healthcare facility for a particular purpose (e.g., setting a broken arm, giving birth, etc.). A canonical healthcare episode is the most likely episode to be experienced by the population or even a given patient with particular biographic information.

Abstract: System and method for determining location of a device using opposing cameras are disclosed. In one embodiment, the method includes activating a front-facing camera and a back-facing camera of the device, capturing a first pair of images from a first orientation using the front-facing camera and the back-facing camera, where the first pair of images includes a first front image and a first back image, monitoring the device being rotated to a second orientation, capturing a second pair of images from the second orientation using the front-facing camera and the back-facing camera, where the second pair of images includes a second front image and a second back image, and determining a location of the device using the first pair of images and the second pair of images.

Abstract: In one implementation, a method may comprise: storing a user profile indicative of at least one attribute of a user of a mobile station; determining a measurement value based, at least in part, on a signal from at least one sensor on the mobile station; and estimating a location of the mobile station based, at least in part, on an association of the at least one attribute and the measurement value with a context parameter map database.

Abstract: Various techniques are provided for identifying a position uncertainty of a mobile device, such as, based, at least in part, on a measure of potential hindrance of an estimated trajectory. For example, an example method may comprise estimating a trajectory of the mobile device within a particular environment, determining a measure of potential hindrance for at least a portion of the trajectory based, at least in part, on an electronic map that is indicative of a presence or an absence of one or more obstacles, and presenting an indication of a position uncertainty to a user of the mobile device. The position uncertainty may be based, at least in part, on the measure of potential hindrance.

Abstract: Various methods, apparatuses and/or articles of manufacture are provided which may be implemented to perform and/or otherwise support collaborative navigation between a plurality of mobile devices. In one example, a first mobile device, in response to a determination that at least a second mobile device is located within a threshold distance of the first mobile device, may obtain a navigation parameter indicative of an initial position and/or movement of the second mobile device which was determined based, at least in part, on a measurement from a sensor at the second mobile device. The first mobile device then affect a determined position and/or movement of the first mobile device based, at least in part, on the navigation parameter.

Abstract: Disclosed are systems, apparatus, devices, method, computer program products, and other implementations, including a method that includes capturing an image of a scene by an image capturing unit of a device that includes at least one sensor, determining relative device orientation of the device based, at least in part, on determined location of at least one vanishing point in the captured image of the scene, and performing one or more calibration operations for the at least one sensor based, at least in part, on the determined relative device orientation.

Abstract: A method for updating a digital map includes receiving a digital map that shows a first region and a second region, and where the digital map includes a border that separates the first region from the second region. The method also includes receiving user trajectory data and generating portal information based on the received user trajectory data that cross the border between the first and second regions. The digital map is then updated with the portal information.

Abstract: Systems, apparatus and methods to perform hypothesis localization are presented. An access point (AP) is associated with one specific area, such as an LCI (location context identifier). Each specific area defines area information comprising an identifier, a list of APs, and a map of the specific area. Each specific area, and associated APs from the list of APs, is hypothesized as the specific area containing a mobile device. For each hypothesized area, a mobile device trajectory is formed using just listed APs from that area, to form a plurality of mobile device trajectories. After evaluating the resulting plurality of mobile device trajectories, a selection is made of the best fitting mobile device trajectory.

Abstract: Various techniques are provided for identifying a position uncertainty of a mobile device, such as, based, at least in part, on a measure of potential hindrance of an estimated trajectory. For example, an example method may comprise estimating a trajectory of the mobile device within a particular environment, determining a measure of potential hindrance for at least a portion of the trajectory based, at least in part, on an electronic map that is indicative of a presence or an absence of one or more obstacles, and presenting an indication of a position uncertainty to a user of the mobile device. The position uncertainty may be based, at least in part, on the measure of potential hindrance.

Abstract: The position of a mobile device in a wireless network is refined by obtaining statistical samples of particles representing an initial estimated position of the device, obtaining one or more parameters of received signals from a set of one or more access points in the network, locking the set of one or more access points without using one or more additional access points to estimate the position, resampling the particles to obtain a second estimation of the position after locking the first set of one or more access points, and obtaining a refined estimated position based on a comparison of the first and second estimations of the position.

Abstract: Techniques are provided for adaptively sampling orientation sensors in positioning systems based on location (e.g., map) data. Embodiments can enable a device to use location, direction, and/or location information to anticipate an expected change in motion. The embodiments can then identify and prioritize a number of sampling strategies to alter sampling rates of orientation sensors, and implement at least one strategy, based on priority.

Abstract: Techniques for position estimation, e.g., of a mobile communication device, using trajectory are described herein. A method for estimating position of a mobile device described herein includes obtaining a routing graph corresponding to an area, wherein the routing graph indicates traversable paths through the area; collecting trajectory information corresponding to movement of the mobile device through the area; forming a trajectory graph from the trajectory information; comparing the trajectory graph to at least one subset of the routing graph to determine at least one matching subset of the routing graph; and estimating the position of the mobile device based at least in part on the at least one matching subset of the routing graph.

Abstract: Disclosed are systems, apparatus, devices, method, computer program products, and other implementations, including a method that includes capturing an image of a scene by an image capturing unit of a device that includes at least one sensor, determining relative device orientation of the device based, at least in part, on determined location of at least one vanishing point in the captured image of the scene, and performing one or more calibration operations for the at least one sensor based, at least in part, on the determined relative device orientation.

Abstract: Methods and apparatuses for modeling characteristics of a venue are disclosed. The method comprises identifying a set of constraints associated with the venue, determining a plurality of paths to be traveled by one or more mobile devices in accordance with the set of constraints, directing the one or more mobile devices to navigate the venue using the plurality of paths, receiving data collected by the one or more mobile devices, and generating a model of the venue using the data collected by the one or more mobile devices.

Abstract: Various methods, apparatuses and/or articles of manufacture are provided which may be implemented to estimate a trajectory of a mobile device within an indoor environment. In some embodiments, the trajectory may be estimated without the use of any signal-based positioning information. For example, a mobile device may estimate such a trajectory based, at least in part, on one or more sensor measurements obtained at the mobile device, and further affect the estimated trajectory based, at least in part, on one or more objects identified in an electronic map of the indoor environment.

Abstract: An estimated position of a pedestrian is obtained by detecting, by a movement sensor on the pedestrian, whether the pedestrian is moving, propagating particles based on a predictive pedestrian movement model upon detecting that the pedestrian is moving, and updating, by a wireless network, the position of the pedestrian upon failure by the movement sensor to detect that the pedestrian is moving. The movement sensor may be a pedometer. The wireless network may be a local wireless network such as a Wi-Fi network.

Abstract: In one implementation, a method may comprise: storing a user profile indicative of at least one attribute of a user of a mobile station; determining a measurement value based, at least in part, on a signal from at least one sensor on the mobile station; and estimating a location of the mobile station based, at least in part, on an association of the at least one attribute and the measurement value with a context parameter map database.

Abstract: Disclosed are systems, apparatus, devices, method, computer program products, and other implementations, including a method that includes capturing an image of a scene by an image capturing unit of a device that includes at least one sensor, determining relative device orientation of the device based, at least in part, on determined location of at least one vanishing point in the captured image of the scene, and performing one or more calibration operations for the at least one sensor based, at least in part, on the determined relative device orientation.