Abstract: Geofence crossing-based control systems and techniques are described herein. For example, a geofence crossing control technique may include receiving a location signal indicative of a range of locations in which a mobile computing device is located; receiving a velocity signal indicative of a speed and direction of the mobile computing device; generating, for each of a plurality of candidate geofence crossing times, a performance indicator based on the location signal, the velocity signal, and a boundary of the geofence; selecting a geofence crossing time from the plurality of candidate geofence crossing times based on the performance indicators; and transmitting a control signal representative of the geofence crossing time. Other embodiments may be disclosed and/or claimed.

Abstract: Technologies for determining a user's location include a mobile computing device (100) to determine, based on sensed inertial characteristics of the device, a walking gait of a user. The walking gait is one of a first gait indicative of the user holding the device to the user's side or a second gait indicative of the user swinging the device along the user's side. The device further detects that the user has taken a physical step based on the inertial characteristics and the determined walking gait of the user, and determines a raw directional heading of the device indicative of a direction of the physical step. The device determines an estimated location of the user based on the determined raw directional heading, an estimated step length, and the user's previous location.

Abstract: Embodiments of the present disclosure provide for an apparatus for facilitating a connection with an external sensor module, in accordance with some embodiments. In one instance, the apparatus may include a processor and a sensor hub coupled with the processor, wherein the sensor hub may include a bus to provide a connection between the apparatus and the external sensor module. The apparatus may further include signal pattern generation circuitry coupled with the sensor hub to generate a signal pattern in response to a connection of the external sensor module to the apparatus via the bus or disconnect of the external sensor module from the bus, to indicate an insert or remove event to the apparatus, and facilitate the connection of the apparatus with the external sensor module. Other embodiments may be described and/or claimed.

Abstract: A sensor interface circuit enables signal reconstruction on data received from a sensor prior to sending the data to various sensor clients. Multiple sensor clients have different frequencies configured to receive sensor data. The sensor interface circuit performs signal reconstructions including data interpolation to generate interpolated data signal having frequencies corresponding to the different frequencies configured for the different sensor clients. Signal reconstruction can also include filtering the data. The sensor interface circuit sends the reconstructed data to the multiple clients in accordance with their different frequencies.

Abstract: Memory sharing techniques are provided for offloading an application from a host processor to an integrated sensor hub (ISH). A methodology implementing the techniques according to an embodiment includes allocating a shared region of memory to be accessed by the host processor and by the ISH, in connection with the execution of a location application. The method also includes storing a location database in the shared region of memory. The location database is divided into segments, where each segment is associated with an area, for example, defined by a range of latitudes and longitudes. The method further includes transferring, through a direct memory access (DMA), one or more of the segments between the shared memory region and a second memory associated with the ISH. The method further includes executing at least a portion of the location application on the ISH, based on the data segments stored in the second memory.

Abstract: Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.

Abstract: Systems and methods may provide for a fitness sensor that is located and operates in a sensor hub. The fitness sensor may link to a Bluetooth link controller, a communications hub and numerous environmental and physical sensors in a platform that is conducive to low power utilization. Awakening a host processor only when valid content-oriented sensor data is available may assist to reduce a footprint of power consumption and time spent in computer processing fitness models.

Abstract: In a method for protecting electronic data, a data processing system (20, 20A, 20B) uses (a) a master electrocardiogram (ECG) identifier (44) for an authorized user of the system and (b) a secret chip key (26) for the system to encrypt data in the system. Also, before allowing the encrypted data to be decrypted, the system collects an ECG reading for a current user of the system and uses (a) the collected ECG reading for the current user and (b) at least one predetermined ECG identifier (EID) vector for the authorized user to determine whether the current user is the authorized user. In response to determining that the current user is the authorized user, the system uses the master ECG identifier (44) for the authorized user and the secret chip key (26) for the system to decrypt the data.

Abstract: Techniques are provided for unification of classifier models across device platforms of varying form factors and/or sensor calibrations. A methodology implementing the techniques according to an embodiment includes extracting classification features from data provided by sensors associated with a first device platform. The method also includes applying a feature mapping function to the extracted features. The feature mapping function is configured to transform the features such that the are suitable for use by a classifier model that is trained on data provided by sensors associated with a second device platform. The method further includes executing the classifier model on the transformed features to generate classifications, for example recognized activities associated with use of the first device. The feature mapping function is based on application of a statistical distribution distance minimization between a sampling of data provided by sensors of the first device and sensors of the second device.

Abstract: The disclosed invention is a device and method of use for gauze utilized in the treatment of hemorrhage. In an embodiment the device combines clotting factor concentrator gauze and urea in order to slow hemorrhage and prevent hemorrhage induced coagulopathy. In an embodiment, the gauze is comprised of a first layer comprised of gauze and a second layer embedded with urea. The urea reacts spontaneously when it comes into contact with water, and undergoes an endothermic reaction. This endothermic reaction cools the smaller blood vessels exposed from soft tissue trauma, causing enhanced platelet activity as well as vasoconstriction to minimize the bleeding that occurs before and after the clotting cascade is activated over the entire wound surface.

Abstract: Systems, apparatuses and methods may provide for detecting one or more trigger condition on a mobile platform and activating an offline calibration of one or more sensors on the mobile platform in response to the one or more trigger conditions. Additionally, an operational profile of the one or more sensors may be updated based on the offline condition. In one example, the trigger conditions include one or more of a time condition, a temperature condition, a motion condition or a system event condition.

Abstract: Devices (100), systems (200), and methods for automatically evaluating a potential stroke condition in a subject are disclosed and described.

Abstract: Technologies for determining a user's location include a mobile computing device (100) to determine, based on sensed inertial characteristics of the device, a walking gait of a user. The walking gait is one of a first gait indicative of the user holding the device to the user's side or a second gait indicative of the user swinging the device along the user's side. The device further detects that the user has taken a physical step based on the inertial characteristics and the determined walking gait of the user, and determines a raw directional heading of the device indicative of a direction of the physical step. The device determines an estimated location of the user based on the determined raw directional heading, an estimated step length, and the user's previous location.

Abstract: Technologies for detecting micro-motion based input gestures include a wrist-wearable computing device that includes sensors from which values for micro-motion states can be determined. Each micro-motion state is indicative of a motion-related characteristic of the wrist-wearable computing device that is used to determine whether a sequence of detected gesture steps matches an input gesture model associated with an input gesture. The input gesture model defines a required sequence of required gesture steps from which an input gesture may be determined.

Abstract: Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.

Abstract: Systems and methods may provide for obtaining first sensor data associated with a gyroscope and obtaining second sensor data associated with a magnetometer. Additionally, the first sensor data, the second sensor data and an extended Kalman filter may be used to calibrate the magnetometer. In one example, a sampling rate of the magnetometer is increased before obtaining the second sensor data and the sampling rate of the magnetometer is decreased after calibration of the magnetometer.

Abstract: Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.

Abstract: A method for managing a number of applications running in an electronic device includes determining a touch gesture of a number of touch gestures on a display of the electronic device, opening an interface on a display of the electronic device to display icons of all applications that are currently running in the background when one of the touch gestures is determined, and managing the applications currently running in the background according to the number of touch gestures. The touch gestures include a single touch gesture, a normal slide gesture, and a pressure slide gesture.

Abstract: A method and system for context sensing is described herein. The method includes determining if sensor data obtained via a number of sensors exceed a predetermined threshold. The method also includes increasing a sampling rate of any of the sensors to obtain context data corresponding to a computing device if the sensor data exceed the threshold. The method further includes analyzing the context data to classify a context of the computing device.

Abstract: Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.