Abstract: Disclosed is a method for identifying and monitoring a shopping behavior in a user. The method includes capturing images from a depth camera mounted on a shelf unit, identifying a user from the captured image, identifying joints of the identified user by performing a deep neural network (DNN) body joint detection on the captured images; detecting and tracking actions of the identified user over a first time period; tracking an object from the bins over a second time period by associating the object with one or more joints among the identified joints that have entered the bins within the shelf unit, and determining an action of the identified user based at least in part on the associated object with the one or more joints and results from the deep learning identification on the bounding box.

Abstract: A motion capture system and method are provided. In a motion capture method, a plurality of motion datasets are accessed. Each motion dataset is associated with a motion sensing unit at which timestamped motion data points of that motion dataset are generated, each motion sensing unit is configured to be in physical contact with a different part of a body of interest. Each timestamped motion data point is timestamped at the motion sensing unit at which it is generated using a clock time that is synchronized across the plurality of motion sensing units. The timestamped motion data points are processed to generate a kinematic model which describes motion of the respective parts of the body of interest.

Abstract: Systems and methods for executing a data processing function are disclosed. A first processing device of a storage accelerator loads a first instruction set associated with a first application of a host computing device. A second processing device of the storage accelerator loads a second instruction set associated with the first application. A command is received from the host computing device. The command may be associated with data associated with the first application. The first processing device identifies at least a first criterion or a second criterion associated with the data. The first processing device processes the data according to the first instruction set in response to identifying the first criterion. The first processing device writes the data to a buffer of the second processing device in response to identifying the second criterion. The second processing device processes the data in the buffer according to the second instruction set.

Abstract: A ground reaction force plate apparatus and measuring system are provided. The apparatus includes a plate for receiving a force to be measured as applied by a subject to the plate and a plurality of load cells in an arrangement supporting the plate, each load cell being a single-axis sensor with the axis provided at an angle to a plane of the plate and oriented towards a centre of the plate. The load cells are configured to measure data relating to a force vector at the angled single-axis sensor when the subject applies a force to the plate. A data collection controller is provided for logging time series measurements of the load cells over a time in which the subject applies a force to the plate. The system applies machine learning processing to output the load cell measurements as a three-dimensional ground reaction force on the plate by a subject.

Abstract: A motion capture system and method are provided. In a motion capture method, a plurality of motion datasets are accessed. Each motion dataset is associated with a motion sensing unit at which timestamped motion data points of that motion dataset are generated, each motion sensing unit is configured to be in physical contact with a different part of a body of interest. Each timestamped motion data point is timestamped at the motion sensing unit at which it is generated using a clock time that is synchronized across the plurality of motion sensing units. The timestamped motion data points are processed to generate a kinematic model which describes motion of the respective parts of the body of interest.