Abstract:
This disclosure relates to reducing power consumption for cellular communication based on transport block size in combination with channel condition measurements for applications with certain application characteristics. In one embodiment, a transport block size for use for uplink communication with a base station by a wireless device may be selected. The transport block size may provide more robust communication characteristics than required for current channel conditions. The transport block size may be selected based on application characteristics of an application performing the uplink communication. A transmit power for the wireless device to use for the uplink communication may be selected based on the transport block size providing more robust communication characteristics than required for the current channel conditions. In particular, transport power selection may be biased towards a reduced transmit power based on the transport block size providing more robust communication characteristics than required for the current channel conditions.
Abstract:
A method for redundant transmission of real time data is provided. The method can include an edge node in a wireless network sending a first RTP packet including a first real time data frame to a second edge node. The method can further include the edge node determining that a radio link condition is sufficient to support redundant transmission of real time data to the second edge node. The method can additionally include the edge node, in response to determining that the radio link condition is sufficient to support redundant transmission of real time data, bundling the first real time data frame with a next sequential real time data frame that has not been previously sent to the second edge node in a second RTP packet at a PDCP layer of the edge node; and sending the second RTP packet to the second edge node.
Abstract:
In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.
Abstract:
Location mapping and navigation user interfaces may be generated and presented via mobile computing devices. A mobile device may detect its location and orientation using internal systems, and may capture image data using a device camera. The mobile device also may retrieve map information from a map server corresponding to the current location of the device. Using the image data captured at the device, the current location data, and the corresponding local map information, the mobile device may determine or update a current orientation reading for the device. Location errors and updated location data also may be determined for the device, and a map user interface may be generated and displayed on the mobile device using the updated device orientation and/or location data.
Abstract:
Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). In some embodiments, a method comprises: detecting, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.
Abstract:
In an example method, a mobile device receives motion data obtained by one or more sensors worn by a user. The mobile device determines, based on the motion data, that the user has fallen at a first time and whether the user has moved between a second time and a third time subsequent to the first time. Upon determining that the user has not moved between the second time and the third time, the mobile device initiates a communication to an emergency response service at a fourth time after the third time. The communication includes an indication that the user has fallen and a location of the user.
Abstract:
Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models. In an embodiment, a method comprises: establishing, by a processor of a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining, by the processor using the communication connection, machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining, by the processor, a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining, by the processor, a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining, by the processor, a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending, by the processor to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.
Abstract:
Location mapping and navigation user interfaces may be generated and presented via mobile computing devices. A mobile device may detect its location and orientation using internal systems, and may capture image data using a device camera. The mobile device also may retrieve map information from a map server corresponding to the current location of the device. Using the image data captured at the device, the current location data, and the corresponding local map information, the mobile device may determine or update a current orientation reading for the device. Location errors and updated location data also may be determined for the device, and a map user interface may be generated and displayed on the mobile device using the updated device orientation and/or location data.
Abstract:
The disclosure describes procedures for allocating network resources for a mobile device communicating within a Long Term Evolution (LTE) network. The mobile device can be configured to decode a physical downlink shared channel (PDSCH), acquire first and second physical downlink control channel (PDCCH) decode indicators from a payload of the same PDSCH communication, decode a PDCCH for downlink control information (DCI) associated with a first application data type based on the first PDCCH decode indicator a second application data type based on the second PDCCH decode indicator. The first PDCCH decode indicator can identify an upcoming LTE subframe where the mobile device is required to decode the PDCCH for DCI associated VoLTE resource assignments and the second PDCCH decode indicator can identify an upcoming LTE subframe where the mobile device is required to decode the PDCCH for DCI associated with high bandwidth best effort (BE) data resource assignments.
Abstract:
Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models. In an embodiment, a method comprises: establishing, by a processor of a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining, by the processor using the communication connection, machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining, by the processor, a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining, by the processor, a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining, by the processor, a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending, by the processor to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.