Abstract:
Methods, apparatus, systems, and articles of manufacture to track movement of sports implements are disclosed herein. An example sensing unit disclosed herein is to be coupled to a sports implement. The sensing unit includes an inertial measurement unit to obtain movement data of said sports implement during a swing of said sports implement and a swing analyzer to determine a follow-through pattern of the swing of said sports implement based on the movement data.
Abstract:
Edge devices utilizing personalized machine learning and methods of operating the same are disclosed. An example edge device includes a model accessor to access a first machine learning model from a cloud service provider. A local data interface is to collect local user data. A model trainer is to train the first machine learning model to create a second machine learning model using the local user data. A local permissions data store is to store permissions indicating constraints on the local user data with respect to sharing outside of the edge device. A permissions enforcer is to apply permissions to the local user data to create a sub-set of the local user data to be shared outside of the edge device. A transmitter is to provide the sub-set of the local user data to a public data repository.
Abstract:
Methods and apparatus for high speed location determinations are disclosed. An example apparatus includes at least two coils arranged along a zone of interest to generate a magnetic field, and a sensor to measure a change in the magnetic field associated with the at least two coils as an object of interest moves within or into the zone of interest. The example apparatus also includes a processor to determine a position of the object of interest based on the measured change.
Abstract:
Systems and methods may provide for using one or more generic classifiers to generate self-training data based on a first plurality of events associated with a device, and training a personal classifier based on the self-training data. Additionally, the one or more generic classifiers and the personal classifier may be used to generate validation data based on a second plurality of events associated with the device. In one example, the personal classifier is substituted for the one or more generic classifiers if the validation data indicates that the personal classifier satisfies a confidence condition relative to the one or more generic classifiers.
Abstract:
A method is described including storing reference vector data corresponding to user gestures at a plurality of neurons at pattern matching hardware, receiving real time signals from the sensor array and performing gesture recognition using the pattern matching hardware to compare incoming vector data corresponding to the real time signals with the reference vector data.
Abstract:
Embodiments of a mobile station and method for Wi-Fi scan scheduling and power adaption for low-power indoor location are generally described herein. In some embodiments, the mobile station may identify channels, beacon timing and rough signal strength levels of nearby access points (APs) from at least one of a previous full-channel scan or a Wi-Fi fingerprint database and may configure receiver sensitivity based on the rough signal strength levels for receipt of subsequent beacons. The mobile station may wake-up from a low-power state to receive beacons for the nearby access points on the identified channels at times based on the identified beacon timing. The received signal strength indicators (RSSIs) levels of the received beacons may be used for location determination.
Abstract:
A method of detecting a revisit position includes receiving at a computing system a plurality of position data points, each of the plurality of position data points including a signal scan measurement. The method farther includes calculating a first signal distance between a first signal scan measurement corresponding to a first position data point of the plurality of position data points and a second signal scan measurement corresponding to a second position data point of the plurality of position data points. The method further includes determining that the first signal distance is less than a first threshold, that the first signal distance is a local minimum for the first position data point, and the first signal distance is a local minimum for the second position data point. The method further includes, based on the determining, identifying the first and second position data points as revisit points.
Abstract:
Technologies for context-based management of wearable computing devices include a mobile computing device and a wearable computing device. The wearable computing device generates sensor data indicative of a location context of the wearable computing device and transmits the sensor data to the mobile computing device. The mobile computing device generates local sensor data indicative of a location context of the wearable computing device and fuses the local sensor data with the sensor data received from the wearable computing device. The mobile computing device determines a context of the wearable computing device based on the fused sensor data. The mobile computing device determines whether an adjustment to the functionality of the wearable computing device is required based on the determined context. The mobile computing device manages the functionality of the wearable computing device in response to determining that an adjustment to the functionality is required.
Abstract:
Examples are disclosed to track sport implements and/or objects of interest. An example apparatus includes a first coil to generate a first magnetic field having a first vertical component with a zero magnitude along a first line of interest and a second coil partially overlapped with the first coil, where the second coil is to generate a second magnetic field. The example apparatus also includes a sensor to measure a magnitude of the first magnetic field in the first line of interest and a processor to determine an object of interest has crossed the first line of interest based on the magnitude of the first magnetic field measured by the sensor.
Abstract:
Example blockchain-based digital data exchanges including example data publisher endpoint devices and example data subscriber endpoint systems are disclosed herein. Example data publisher endpoint devices disclosed herein include a datamart publisher client to transmit a message to a data exchange to publish availability of data to be accessed from data storage associated with the data publisher endpoint device, and in response to a request from a data subscriber endpoint system, initiate a transaction to provide the data subscriber endpoint system with access to the data. Disclosed example data publisher endpoint devices also include a blockchain client to publish a record of the transaction to a blockchain network when the transaction is validated by the datamart publisher client, the record to be included in a blockchain implemented by the blockchain network.