Abstract: A method may include capturing image data associated with an object in a defined environment at one or more points in time. The method may include capturing radar data associated with the object in the defined environment at the same points in time. The method may include obtaining, by a machine learning model, the image data and the radar data associated with the object in the defined environment. The method may include pairing each image datum with a corresponding radar datum based on a chronological occurrence of the image data and the radar data. The method may include generating, by the machine learning model, a three-dimensional motion representation associated with the object that is associated with the image data and the radar data.

Abstract: An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

Abstract: A stump device may include a first image-capturing sensor configured to couple to at least one stump of a wicket positioned at a bowling end of a cricket field and capture image data of an initial motion of a cricket ball. The stump device may also include a second image-capturing sensor configured to couple to at least one stump of the wicket and capture image data of a trajectory and a flight path of the cricket ball. The stump device may additionally include a first radar sensor configured to couple to at least one stump of the wicket and capture radar data describing one or more initial launch parameters of the cricket ball. The stump device may include a second radar sensor configured to couple to at least one of the stumps of the wicket and capture radar data describing one or more movement parameters of a bowler.

Abstract: A method may include obtaining sensor data from one or more activity sensors, each of the activity sensors being coupled to a respective area of a sports user. The method may include obtaining image data of the sports user and each of the activity sensors coupled to the sports user. The method may include identifying, by a machine learning module and based on the sensor data and the image data, a respective muscle associated with each respective area to which the activity sensors are coupled. The method may include identifying movement of the sports user based on the sensor data from the activity sensors and the identified body part. The method may include analyzing the identified movement of the sports user including evaluating a body posture of the sports user, identifying one or more movement patterns of the sports user, and/or performing an injury assessment for the sports user.

Abstract: A cricket sensor may include one or more first image-capturing sensors configured to capture image data of a pitching motion of a bowler and image data of an initial motion of a cricket ball at a bowling end of a cricket field. The cricket sensor may include one or more second image-capturing sensors configured to capture image data of a trajectory and a flight path of the cricket ball towards a batting end of the cricket field. The cricket sensor may also include one or more first radar sensors configured to capture radar data describing one or more initial launch parameters of the cricket ball related to the trajectory and the flight path of the cricket ball towards the batting end of the cricket field.

Abstract: An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

Abstract: An example method to determine an object spin rate may include receiving a radar signal of a particular object in motion. The method may further include converting the radar signal into an input vector. The method may also include providing the input vector as input to a neural network. The neural network may include access to a set of initial data that has been generated based on multiple initial radar signals of multiple initial objects in motion. The method may further include determining a spin rate of the particular object in motion based on an analysis performed by the neural network of the input vector including time and frequency information of the particular object in motion in view of the set of initial data. The analysis may include comparing one or more elements of the input vector to one or more elements of the set of initial data.

Abstract: A cricket sensor may include one or more first image-capturing sensors configured to capture image data of a pitching motion of a bowler and image data of an initial motion of a cricket ball at a bowling end of a cricket field. The cricket sensor may include one or more second image-capturing sensors configured to capture image data of a trajectory and a flight path of the cricket ball towards a batting end of the cricket field. The cricket sensor may also include one or more first radar sensors configured to capture radar data describing one or more initial launch parameters of the cricket ball related to the trajectory and the flight path of the cricket ball towards the batting end of the cricket field.

Abstract: A stump device may include a first image-capturing sensor configured to couple to at least one stump of a wicket positioned at a bowling end of a cricket field and capture image data of an initial motion of a cricket ball. The stump device may also include a second image-capturing sensor configured to couple to at least one stump of the wicket and capture image data of a trajectory and a flight path of the cricket ball. The stump device may additionally include a first radar sensor configured to couple to at least one stump of the wicket and capture radar data describing one or more initial launch parameters of the cricket ball. The stump device may include a second radar sensor configured to couple to at least one of the stumps of the wicket and capture radar data describing one or more movement parameters of a bowler.

Abstract: A launch-monitoring system that models a portion of a golf club, golf swing, and golf ball may include a camera and a radar positioned orthogonally to a swing direction of the golf club. A series of images of the golf ball are collected during and after the golf club contacts the golf ball by the camera. The golf swing is captured by the radar. The images are converted into parameterized motion representations, and the radar signal is converted into time-frequency images, which are sent to a convolutional neural network. The convolutional neural network outputs golf club parameters, golf swing parameters, and golf ball parameters, which generate a visual model of the golf club, golf swing, and golf ball in a virtual space. The parameterized motion representations of the golf ball and the time frequency images of the golf swing are not correlated and operate independently from each other.

Abstract: An example method of modeling a portion of a golf club and a golf swing includes scanning the golf club to obtain scanning information, training a convolutional neural network using the scanning information, using at least one camera to obtain a series of images, converting the series of images into parameterized motion representations, using at least one radar to obtain a radar signal, converting the radar signal into time-frequency images, inputting the parameterized motion representations and the time-frequency images into the convolutional neural network, receiving golf club parameters and golf swing parameters as an output of the convolutional neural network, and generating a visual model of the golf club and the golf swing in a virtual space using the golf club parameters and the golf swing parameters.

Abstract: A method may include obtaining sensor data from one or more activity sensors, each of the activity sensors being coupled to a respective area of a sports user. The method may include obtaining image data of the sports user and each of the activity sensors coupled to the sports user. The method may include identifying, by a machine learning module and based on the sensor data and the image data, a respective muscle associated with each respective area to which the activity sensors are coupled. The method may include identifying movement of the sports user based on the sensor data from the activity sensors and the identified body part. The method may include analyzing the identified movement of the sports user including evaluating a body posture of the sports user, identifying one or more movement patterns of the sports user, and/or performing an injury assessment for the sports user.

Abstract: A method may include capturing image data associated with an object in a defined environment at one or more points in time. The method may include capturing radar data associated with the object in the defined environment at the same points in time. The method may include obtaining, by a machine learning model, the image data and the radar data associated with the object in the defined environment. The method may include pairing each image datum with a corresponding radar datum based on a chronological occurrence of the image data and the radar data. The method may include generating, by the machine learning model, a three-dimensional motion representation associated with the object that is associated with the image data and the radar data.

Abstract: An example method to determine an object spin rate may include receiving a radar signal of a particular object in motion. The method may further include converting the radar signal into an input vector. The method may also include providing the input vector as input to a neural network. The neural network may include access to a set of initial data that has been generated based on multiple initial radar signals of multiple initial objects in motion. The method may further include determining a spin rate of the particular object in motion based on an analysis performed by the neural network of the input vector including time and frequency information of the particular object in motion in view of the set of initial data. The analysis may include comparing one or more elements of the input vector to one or more elements of the set of initial data.

Abstract: A system for mapping a neural network architecture onto a computing core and a method of mapping a neural network architecture onto a computing core may be provided, the system comprises a neural network module configured to provide a neural network; a data input module coupled to the neural network module, the neural network module configured to provide input data to the neural network; a layer selector module coupled to the neural network module, the layer selector module configured to select a layer of the neural network; a pipeline module coupled to the layer selection module, the pipeline module configured to perform at least one backward pipelining analysis from the selected layer of the layer selector module, the pipeline module being arranged to perform the at least one backward pipelining analysis towards an input layer of the neural network; a mapper module coupled to the pipeline module, the mapper module being arranged to receive activation information from the pipeline module, the activation info