Nokia Technologies Oy

Georgia Tech Research Corporation

Alcatel Lucent

Nicholas Lane

Fahim Kawsar

Sourav Bhattacharya

Akhil Mathur

Gregory D Abowd

Thomas Ploetz

Mohammed Alloulah

Anton Isopoussu

Chulhong Min

Vincent Tseng

Alberto Gil Ramos

Hyeokhyen Kwon

Harish Kashyap Haresamudram

Eu Gen Catherine Tong

Yan Gao

An exemplary virtual IMU extraction system and method are disclosed for human activity recognition (HAR) or classifier system that can estimate inertial measurement units (IMU) of a person in video data extracted from public repositories of video data having weakly labeled video content. The exemplary virtual IMU extraction system and method of the human activity recognition (HAR) or classifier system employ an automated processing pipeline (also referred to herein as 'IMUTube') that integrates computer vision and signal processing operations to convert video data of human activity into virtual streams of IMU data that represents accelerometer, gyroscope, or other inertial measurement unit estimation that can measure acceleration, inertia, motion, orientation, force, velocity, etc. at a different location on the body. In other embodiments, the automated processing pipeline can be used to generate high-quality virtual accelerometer data from a camera sensor.

Method and system for automatic extraction of virtual on-body inertial measurement units

Methods and systems are disclosed for updating learned models. An embodiment comprises receiving a plurality of data sets representing sensed data from one or more devices and determining, using one or more local learned models, local parameters based on the received data sets. Another operation may comprise generating a combined data set by combining the plurality of data sets and, determining, using one or more local learned models, global parameters based on the combined data set. Another operation may comprise transmitting, to a remote system, the global parameters for determining updated global parameters using one or more global learned models based at least partially on the global parameters, and receiving, from the remote system, the updated global parameters. Another operation may comprise updating the one or more local learned models using both the local parameters and updated global parameters.

Updating learned models

This specification describes methods for performing inferencing based on input data, the methods comprising: initialising a neural network based on a set of stored model information, which defines a plurality of orthogonal binary basis vectors which are to be used to implement kernels in one or more hidden layers of the neural network, and plural sets of plural coefficients, each set of plural coefficients corresponding to a respective one of the kernels, wherein each of the coefficients in a given set of coefficients is associated with a respective one of the one or more orthogonal binary basis vectors; passing input data through the neural network such that convolution operations between the kernels and data arriving at the kernels are performed, wherein each of the kernels is implemented using a respective set of coefficients and the orthogonal binary basis vectors with which the coefficients in the set are associated; and outputting data from the neural network, the output data representing an inference corresponding to the input data. The specification also describes methods for generating model information based on which neural networks may be initialised.

Methods and apparatuses for inferencing using a neural network

In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.

Method and devices for processing sensor data by applying one or more processing pipelines to the sensor data

An apparatus comprising means for: using a generative neural network, trained to translate first sensor data to simulated second sensor data, to translate input first sensor data from a first sensor to simulated second sensor data; and providing the simulated second sensor data to a different, specific-task, machine-learning model for processing at least second sensor data.

Processing sensor data

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Cambridge, United Kingdom

Nicholas Lane